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msmarco_v2.1_doc_01_1668404129#19_2446670624 | http://intellspot.com/open-source-data-visualization/ | 10 Best Open Source Data Visualization Software and Tools | Best Open Source Data Visualization Software
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| Highly customizable and extensible, you can custom charts defined by you. Your data is safe. The data is processed only by the web browser. There is no server-side operation. Requirements: git, bower
Website: http://rawgraphs.io
7. Chart.js
For small projects, ChartJS is a great tool. It is a simple but very flexible JavaScript charting tool for designers and developers. The tool provides you with beautiful flat designs for charts that are responsive by default. | 6,775 | 7,242 |
msmarco_v2.1_doc_01_1668404129#20_2446671493 | http://intellspot.com/open-source-data-visualization/ | 10 Best Open Source Data Visualization Software and Tools | Best Open Source Data Visualization Software
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| git, bower
Website: http://rawgraphs.io
7. Chart.js
For small projects, ChartJS is a great tool. It is a simple but very flexible JavaScript charting tool for designers and developers. The tool provides you with beautiful flat designs for charts that are responsive by default. Chart.js can be used with ES6 modules, plain JavaScript and module loaders. ChartJS is for sure one of the simplest but stunning open source visualization software and charting libraries. You can present your data in 8 different ways. Each of them animated and customisable. Key features and benefits: | 6,965 | 7,544 |
msmarco_v2.1_doc_01_1668404129#21_2446672475 | http://intellspot.com/open-source-data-visualization/ | 10 Best Open Source Data Visualization Software and Tools | Best Open Source Data Visualization Software
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| Chart.js can be used with ES6 modules, plain JavaScript and module loaders. ChartJS is for sure one of the simplest but stunning open source visualization software and charting libraries. You can present your data in 8 different ways. Each of them animated and customisable. Key features and benefits: 8 types of animated and customisable charts including line, bar, horizontal bar, pie, polar area, scatter plot, maps. HTML5 Canvas – with super rendering performance in all modern browsers. Responsive charts – for great scale granularity. Well organized documentation with detailed information. Chart.js can be installed via npm or bower. | 7,242 | 7,883 |
msmarco_v2.1_doc_01_1668404129#22_2446673525 | http://intellspot.com/open-source-data-visualization/ | 10 Best Open Source Data Visualization Software and Tools | Best Open Source Data Visualization Software
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| 8 types of animated and customisable charts including line, bar, horizontal bar, pie, polar area, scatter plot, maps. HTML5 Canvas – with super rendering performance in all modern browsers. Responsive charts – for great scale granularity. Well organized documentation with detailed information. Chart.js can be installed via npm or bower. Website: www.chartjs.org
8. Ember Charts
Ember Charts is another top simple open source visualization tool and repository built with D3.js and Ember.js. It includes bar, pie, and scatter plots, time series which are absolutely easy to extend and modify. It uses SVG to render charts. | 7,544 | 8,167 |
msmarco_v2.1_doc_01_1668404129#23_2446674559 | http://intellspot.com/open-source-data-visualization/ | 10 Best Open Source Data Visualization Software and Tools | Best Open Source Data Visualization Software
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| Website: www.chartjs.org
8. Ember Charts
Ember Charts is another top simple open source visualization tool and repository built with D3.js and Ember.js. It includes bar, pie, and scatter plots, time series which are absolutely easy to extend and modify. It uses SVG to render charts. Ember Charts can offer you the best practices in chart interactivity and presentation. Key features and benefits: Highly customizable and flexible. Robust & polished chart base- weird data will not cause your charts to break. A lot of features – labels, tooltips, add legends, and mouseover effects. | 7,883 | 8,467 |
msmarco_v2.1_doc_01_1668404129#24_2446675549 | http://intellspot.com/open-source-data-visualization/ | 10 Best Open Source Data Visualization Software and Tools | Best Open Source Data Visualization Software
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| Ember Charts can offer you the best practices in chart interactivity and presentation. Key features and benefits: Highly customizable and flexible. Robust & polished chart base- weird data will not cause your charts to break. A lot of features – labels, tooltips, add legends, and mouseover effects. You can roll your own charts by extending their ChartComponent class – automatic resizing, reasonable defaults for margins, etc. To get started with Ember Charts, you will need node installed as a development dependency. Website: http://opensource.addepar.com
9. Highcharts
This program is written in “pure Javascript ” and provides a very friendly user interface. | 8,167 | 8,832 |
msmarco_v2.1_doc_01_1668404129#25_2446676635 | http://intellspot.com/open-source-data-visualization/ | 10 Best Open Source Data Visualization Software and Tools | Best Open Source Data Visualization Software
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| You can roll your own charts by extending their ChartComponent class – automatic resizing, reasonable defaults for margins, etc. To get started with Ember Charts, you will need node installed as a development dependency. Website: http://opensource.addepar.com
9. Highcharts
This program is written in “pure Javascript ” and provides a very friendly user interface. It is one of the most popular open source data visualization and library for building a wide variety of beautiful graphs. You can add interactive and mobile-optimized charts to your web or mobile projects. You will also find a robust documentation and advanced responsiveness. In addition, the software has a lot of great animations that can attract many visitors to your website. Key features and benefits: | 8,467 | 9,240 |
msmarco_v2.1_doc_01_1668404129#26_2446677824 | http://intellspot.com/open-source-data-visualization/ | 10 Best Open Source Data Visualization Software and Tools | Best Open Source Data Visualization Software
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| It is one of the most popular open source data visualization and library for building a wide variety of beautiful graphs. You can add interactive and mobile-optimized charts to your web or mobile projects. You will also find a robust documentation and advanced responsiveness. In addition, the software has a lot of great animations that can attract many visitors to your website. Key features and benefits: Responsive and mobile ready. Data can be given in any form, including CSV, JSON. Annotations to describe charts or data points by drawing simple shapes and text anywhere on the canvas. Touch-optimized charts for a great experience on mobile and touch screens. Flexible styling and elegant charts. | 8,833 | 9,537 |
msmarco_v2.1_doc_01_1668404129#27_2446678928 | http://intellspot.com/open-source-data-visualization/ | 10 Best Open Source Data Visualization Software and Tools | Best Open Source Data Visualization Software
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| Responsive and mobile ready. Data can be given in any form, including CSV, JSON. Annotations to describe charts or data points by drawing simple shapes and text anywhere on the canvas. Touch-optimized charts for a great experience on mobile and touch screens. Flexible styling and elegant charts. Big Data Ready. Vibrant community. Website: www.highcharts.com
10. Victory
Victory is still not so popular open source data visualization software tool as the above ones. | 9,240 | 9,708 |
msmarco_v2.1_doc_01_1668404129#28_2446679797 | http://intellspot.com/open-source-data-visualization/ | 10 Best Open Source Data Visualization Software and Tools | Best Open Source Data Visualization Software
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| Big Data Ready. Vibrant community. Website: www.highcharts.com
10. Victory
Victory is still not so popular open source data visualization software tool as the above ones. Might be because Victory is a usable only with React JS . The majority of other libraries are used with any front javascript framework. Victory is a powerful chart library that allows you to create interactive data visualizations with fully customizable styles and behaviors. It uses the sam | 9,537 | 10,000 |
msmarco_v2.1_doc_01_1668416358#0_2446680662 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| 10 Open Source Decision Tree Software: For Classification & Regression
10 Open Source Decision Tree Software Tools
Over the past few years, open source decision tree software tools have been in high demand for solving analytics and predictive data mining problems. Decision trees are a popular type of supervised learning algorithm that builds classification or regression models in the shape of a tree (that’s why they are also known as regression and classification trees). They work for both categorical data and continuous data. On this page, we collected 10 best open source license classification tree software solutions that run on Windows, Linux, and Mac OS X.
1. Weka
This is a Java-based free and open source tool for Windows, Linux, and Mac OS X. Weka is a powerful collection of machine learning algorithms for data mining purposes. The algorithms can be applied directly to a dataset as well as called from Java code. Weka contains tools for classification, regression, visualization and clustering, and association rules. Moreover, Weka has free online courses that teach machine learning and data mining using Weka. Website: | 0 | 1,139 |
msmarco_v2.1_doc_01_1668416358#1_2446682212 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| Weka
This is a Java-based free and open source tool for Windows, Linux, and Mac OS X. Weka is a powerful collection of machine learning algorithms for data mining purposes. The algorithms can be applied directly to a dataset as well as called from Java code. Weka contains tools for classification, regression, visualization and clustering, and association rules. Moreover, Weka has free online courses that teach machine learning and data mining using Weka. Website: https://www.cs.waikato.ac.nz/
2. KNIME
KNIME Analytics Platform is one of the best open solutions for data-driven innovation. The platform is fast to deploy, easy to scale, and very intuitive to learn. KNIME will provide you with 1500 modules, hundreds of ready-to-run examples (including decision tree examples), a variety of integrated tools, and an extremely wide choice of advanced algorithms. Great software for any data scientist. | 672 | 1,576 |
msmarco_v2.1_doc_01_1668416358#2_2446683523 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| https://www.cs.waikato.ac.nz/
2. KNIME
KNIME Analytics Platform is one of the best open solutions for data-driven innovation. The platform is fast to deploy, easy to scale, and very intuitive to learn. KNIME will provide you with 1500 modules, hundreds of ready-to-run examples (including decision tree examples), a variety of integrated tools, and an extremely wide choice of advanced algorithms. Great software for any data scientist. In addition, KNIME has great examples of how to build prediction or classification model using a decision tree algorithm. Knime is available for Windows, Linux, and Mac OSX. Website: www.knime.com
3. Rapid Miner
This is a very powerful and popular data mining software solution which provides you with predictive advanced analytics. | 1,140 | 1,909 |
msmarco_v2.1_doc_01_1668416358#3_2446684701 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| In addition, KNIME has great examples of how to build prediction or classification model using a decision tree algorithm. Knime is available for Windows, Linux, and Mac OSX. Website: www.knime.com
3. Rapid Miner
This is a very powerful and popular data mining software solution which provides you with predictive advanced analytics. And it is one of the best open source decision tree software tool with no-coding required. Written in Java, it holds a variety of data mining functions such as visualization, data pre-processing, cleansing, filtering, clustering, and predictive analysis. Its Decision Tree operator generates a decision tree model, which can be used for classification and regression. Also, it is easily integrated with WEKA. Rapid Miners runs on Windows, Linux, and Mac OSX. | 1,576 | 2,368 |
msmarco_v2.1_doc_01_1668416358#4_2446685899 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| And it is one of the best open source decision tree software tool with no-coding required. Written in Java, it holds a variety of data mining functions such as visualization, data pre-processing, cleansing, filtering, clustering, and predictive analysis. Its Decision Tree operator generates a decision tree model, which can be used for classification and regression. Also, it is easily integrated with WEKA. Rapid Miners runs on Windows, Linux, and Mac OSX. Website: https://rapidminer.com
4. SilverDecisions
SilverDecisions is a free and open source decision tree software with a great set of layout options. It is a specialized software for creating and analyzing decision trees. The decision tree can be easily exported to JSON, PNG or SVG format. | 1,910 | 2,661 |
msmarco_v2.1_doc_01_1668416358#5_2446687057 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| Website: https://rapidminer.com
4. SilverDecisions
SilverDecisions is a free and open source decision tree software with a great set of layout options. It is a specialized software for creating and analyzing decision trees. The decision tree can be easily exported to JSON, PNG or SVG format. In addition, they will provide you with a rich set of examples of decision trees in different areas such as research and development project decision tree, city council management software and etc. The software works on Windows, Linux, and Mac OS X.
Website: http://silverdecisions.pl/
5. Orange
Orange is a free and open source data visualization software and machine learning tool for novice and expert. Orange will surprise you! | 2,368 | 3,093 |
msmarco_v2.1_doc_01_1668416358#6_2446688191 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| In addition, they will provide you with a rich set of examples of decision trees in different areas such as research and development project decision tree, city council management software and etc. The software works on Windows, Linux, and Mac OS X.
Website: http://silverdecisions.pl/
5. Orange
Orange is a free and open source data visualization software and machine learning tool for novice and expert. Orange will surprise you! It allows you to explore statistical distributions, box and whisker plots, scatter plots, or dive much deeper with decision trees, heatmaps, MDS, hierarchical clustering, and linear regression models. In addition, Orange graphic user interface allows you to focus on exploratory data analysis instead of coding. Orange is used for teaching at schools, universities and in professional training courses all over the world. When it comes to decision trees, Orange has numerous implementations of classification tree learners: TreeLearner, SimpleTreeLearner, and a C45Learner. | 2,661 | 3,667 |
msmarco_v2.1_doc_01_1668416358#7_2446689604 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| It allows you to explore statistical distributions, box and whisker plots, scatter plots, or dive much deeper with decision trees, heatmaps, MDS, hierarchical clustering, and linear regression models. In addition, Orange graphic user interface allows you to focus on exploratory data analysis instead of coding. Orange is used for teaching at schools, universities and in professional training courses all over the world. When it comes to decision trees, Orange has numerous implementations of classification tree learners: TreeLearner, SimpleTreeLearner, and a C45Learner. Orange works on Windows, Linux, and Mac OS X.
6. SMILES
SMILES is an open source machine learning software system that incorporates different features from other machine learning techniques and paradigms and, more importantly, it presents several innovations in almost all of these features. SMILES extends classical decision tree learners with new splitting criteria, new partitions, non-greedy search, extraction of several and different solutions. Its applications are basically data-mining and any other machine learning task where decision trees could be useful. SMILES can also extract comprehensible models from ensembles of classifiers or from neural networks (through mimicking). | 3,093 | 4,356 |
msmarco_v2.1_doc_01_1668416358#8_2446691273 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| Orange works on Windows, Linux, and Mac OS X.
6. SMILES
SMILES is an open source machine learning software system that incorporates different features from other machine learning techniques and paradigms and, more importantly, it presents several innovations in almost all of these features. SMILES extends classical decision tree learners with new splitting criteria, new partitions, non-greedy search, extraction of several and different solutions. Its applications are basically data-mining and any other machine learning task where decision trees could be useful. SMILES can also extract comprehensible models from ensembles of classifiers or from neural networks (through mimicking). The software has been checked on several Intel-based machines (PCs) under different versions of Linux and Ms. Windows. Website: http://users.dsic.upv.es
7. Scikit-learn
Scikit-learn is a simple but efficient machine learning library for Python and an open source tool for data mining and data analysis. It is accessible to everybody and reusable in various contexts. | 3,667 | 4,723 |
msmarco_v2.1_doc_01_1668416358#9_2446692737 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| The software has been checked on several Intel-based machines (PCs) under different versions of Linux and Ms. Windows. Website: http://users.dsic.upv.es
7. Scikit-learn
Scikit-learn is a simple but efficient machine learning library for Python and an open source tool for data mining and data analysis. It is accessible to everybody and reusable in various contexts. It includes a wide variety of algorithms and methods, and decision tree classification is very well supported. Scikit-learn features tree algorithms: ID3, C4.5, C5.0, and CART. Scikit-learn makes it easy to create classification tree that predicts the value of a target variable by learning simple decision rules inferred from the data features. Runs on Windows, Mac, and Linux. | 4,356 | 5,102 |
msmarco_v2.1_doc_01_1668416358#10_2446693889 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| It includes a wide variety of algorithms and methods, and decision tree classification is very well supported. Scikit-learn features tree algorithms: ID3, C4.5, C5.0, and CART. Scikit-learn makes it easy to create classification tree that predicts the value of a target variable by learning simple decision rules inferred from the data features. Runs on Windows, Mac, and Linux. Website: http://scikit-learn.org/
8. OC1 Decision Tree Software System
An open source decision tree software system designed for applications where the instances have continuous values (see discrete vs continuous data ). The OC1 software allows the user to create both standard, axis-parallel decision trees and oblique (multivariate) trees. OC1 has been used for classification of data representing diverse problem domains, including astronomy, gene finding, and others. | 4,723 | 5,574 |
msmarco_v2.1_doc_01_1668416358#11_2446695147 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| Website: http://scikit-learn.org/
8. OC1 Decision Tree Software System
An open source decision tree software system designed for applications where the instances have continuous values (see discrete vs continuous data ). The OC1 software allows the user to create both standard, axis-parallel decision trees and oblique (multivariate) trees. OC1 has been used for classification of data representing diverse problem domains, including astronomy, gene finding, and others. OC1 is written in ANSI C, and combines a variety of features designed to support flexible experimentation on a variety of types of data. It provides support for cross-validation experiments, generation of artificial data, and graphical display of data sets and decision trees. Website: http://www.cbcb.umd.edu/
9. Simple Decision Tree
This is an Excel Add-in released under the terms of GPL v3 with additional permissions. | 5,102 | 5,997 |
msmarco_v2.1_doc_01_1668416358#12_2446696451 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| OC1 is written in ANSI C, and combines a variety of features designed to support flexible experimentation on a variety of types of data. It provides support for cross-validation experiments, generation of artificial data, and graphical display of data sets and decision trees. Website: http://www.cbcb.umd.edu/
9. Simple Decision Tree
This is an Excel Add-in released under the terms of GPL v3 with additional permissions. It is initially created for the Stanford Decisions and Ethics Center and open-sourced it for the Decision Professionals Network. It is good if you need a really simple open source decision tree solution. Website: https://sites.google.com/site/simpledecisiontree/
10. Rattle
Rattle is a popular GUI for data mining using R. It is also a great solution when it comes to open source decision tree software. | 5,574 | 6,401 |
msmarco_v2.1_doc_01_1668416358#13_2446697687 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| It is initially created for the Stanford Decisions and Ethics Center and open-sourced it for the Decision Professionals Network. It is good if you need a really simple open source decision tree solution. Website: https://sites.google.com/site/simpledecisiontree/
10. Rattle
Rattle is a popular GUI for data mining using R. It is also a great solution when it comes to open source decision tree software. The tool runs on Linux, Mac OS, and Windows. Rattle is a powerful tool that presents statistical and visual summaries of data, builds both unsupervised and supervised machine learning models from the data, presents the performance of models graphically and etc. Except for business and commercial enterprises purposes, Rattle is used also for teaching in Australian and American universities. In addition, Rattle has a number of decision tree examples you can use. Website: | 5,998 | 6,875 |
msmarco_v2.1_doc_01_1668416358#14_2446698972 | http://intellspot.com/open-source-decision-tree/ | 10 Open Source Decision Tree Software: For Classification & Regression | 10 Open Source Decision Tree Software Tools
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| The tool runs on Linux, Mac OS, and Windows. Rattle is a powerful tool that presents statistical and visual summaries of data, builds both unsupervised and supervised machine learning models from the data, presents the performance of models graphically and etc. Except for business and commercial enterprises purposes, Rattle is used also for teaching in Australian and American universities. In addition, Rattle has a number of decision tree examples you can use. Website: https://rattle.togaware.com/
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Silvia Valcheva
Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc. Leave a Reply
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msmarco_v2.1_doc_01_1668424113#0_2446700314 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| 5 Best Open Source Graph Databases: Java, C++, Python
5 Best Open Source Graph Databases
There are many reasons for searching the best open source Graph Database programs . In the last few years, Graph Databases are becoming more and more popular, as they provide a great flexibility to represent your data. The majority of Graph Databases are written in Java but there is a list of good solutions in Python, .NET, PHP, and C++. What is Graph database? Graph database helps you discover relationships between data. In Mathematical meaning, a Graph is a collection of elements – called Nodes that are joined together by Edges. Each Node depicts some information and each Edge depicts a connection between two Nodes. With this in mind, a Graph Database is just a Database Program that models Nodes and Edges in a Graph as first-class units. The main idea behind those Graphs is to allow you represent complex connections between data in a much more clear and understandable way. | 0 | 976 |
msmarco_v2.1_doc_01_1668424113#1_2446701635 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| Graph database helps you discover relationships between data. In Mathematical meaning, a Graph is a collection of elements – called Nodes that are joined together by Edges. Each Node depicts some information and each Edge depicts a connection between two Nodes. With this in mind, a Graph Database is just a Database Program that models Nodes and Edges in a Graph as first-class units. The main idea behind those Graphs is to allow you represent complex connections between data in a much more clear and understandable way. They make it easier to form different associations and create theories about your data. Here, we collected some of the best free and open source graph databases. 1. Neo4j’s Community Edition
Neo4j is one of the most popular highly scalable and native graph database (written in Java) designed to leverage data relationships. Neo4j’s Graph Platform is optimized to store, map, analyze, and traverse networks of connected data in order to uncover invisible contexts and hidden relationships. | 452 | 1,466 |
msmarco_v2.1_doc_01_1668424113#2_2446703005 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| They make it easier to form different associations and create theories about your data. Here, we collected some of the best free and open source graph databases. 1. Neo4j’s Community Edition
Neo4j is one of the most popular highly scalable and native graph database (written in Java) designed to leverage data relationships. Neo4j’s Graph Platform is optimized to store, map, analyze, and traverse networks of connected data in order to uncover invisible contexts and hidden relationships. With the help of intuitively mapping data points and the connections between them, Neo4j powers intelligent, real-time applications including: artificial intelligence, machine learning; the internet of things; real-time recommendations; master data management; | 977 | 1,727 |
msmarco_v2.1_doc_01_1668424113#3_2446704107 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| With the help of intuitively mapping data points and the connections between them, Neo4j powers intelligent, real-time applications including: artificial intelligence, machine learning; the internet of things; real-time recommendations; master data management; fraud detection; identity and access management. Neo4j’s comes with 2 editions: community and enterprise edition. Community Edition is ideal for learning Neo4j and small projects that do not require high levels of scaling or professional services and support. | 1,466 | 1,987 |
msmarco_v2.1_doc_01_1668424113#4_2446704974 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| fraud detection; identity and access management. Neo4j’s comes with 2 editions: community and enterprise edition. Community Edition is ideal for learning Neo4j and small projects that do not require high levels of scaling or professional services and support. Enterprise Edition has the same features as Community Edition, with enterprise-grade availability, management, and scale-up and scale-out capabilities. Key features and benefits of Neo4j’s Community Edition: Labeled property graph model
Native graph processing & storage
Cypher graph query language
Fast writes via native label indexes
Fast reads via composite indexes
ACID Transactions
High-performance native API
Language drivers for Java, Python, C# & JavaScript, PHP, NodeJS, etc. Extremely fast. 2. | 1,728 | 2,491 |
msmarco_v2.1_doc_01_1668424113#5_2446706096 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
About The Author
| Enterprise Edition has the same features as Community Edition, with enterprise-grade availability, management, and scale-up and scale-out capabilities. Key features and benefits of Neo4j’s Community Edition: Labeled property graph model
Native graph processing & storage
Cypher graph query language
Fast writes via native label indexes
Fast reads via composite indexes
ACID Transactions
High-performance native API
Language drivers for Java, Python, C# & JavaScript, PHP, NodeJS, etc. Extremely fast. 2. HyperGraphDB
HyperGraphDB is an open-source data storage mechanism with a general-purpose. As the name suggests, it is a database for storing hypergraphs. HyperGraphDB is designed mainly for knowledge management, AI, and semantic web projects, but it is also used as an embedded object-oriented database for Java projects of all sizes. Or a graph database. While HyperGraphDB falls into the general family of graph databases, much of its design provides the means to manage structure-rich information with arbitrary layers of complexity. | 1,988 | 3,029 |
msmarco_v2.1_doc_01_1668424113#6_2446707492 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| HyperGraphDB
HyperGraphDB is an open-source data storage mechanism with a general-purpose. As the name suggests, it is a database for storing hypergraphs. HyperGraphDB is designed mainly for knowledge management, AI, and semantic web projects, but it is also used as an embedded object-oriented database for Java projects of all sizes. Or a graph database. While HyperGraphDB falls into the general family of graph databases, much of its design provides the means to manage structure-rich information with arbitrary layers of complexity. Key features and benefits: Graph-oriented storage. Powerful data modeling and knowledge representation. N-ary, higher-order relationships between graph Nodes. Graph traversals. | 2,492 | 3,206 |
msmarco_v2.1_doc_01_1668424113#7_2446708549 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
About The Author
| Key features and benefits: Graph-oriented storage. Powerful data modeling and knowledge representation. N-ary, higher-order relationships between graph Nodes. Graph traversals. Relational-style queries. Customizable indexing and storage management. Extensible, dynamic DB schema. Out of the box Java OO database. Non-blocking concurrent writes and reads! | 3,029 | 3,384 |
msmarco_v2.1_doc_01_1668424113#8_2446709245 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| Relational-style queries. Customizable indexing and storage management. Extensible, dynamic DB schema. Out of the box Java OO database. Non-blocking concurrent writes and reads! P2P framework for data distribution. 3. Titan Distributed Graph Database
Titan is one of the most scalable open source graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges. You can do it in a Hadoop (one of the best big data tools for storage and management data), extremely helpful for huge datasets. Titan is a transactional database that supports thousands of concurrent users executing complex graphs in real time. | 3,206 | 3,861 |
msmarco_v2.1_doc_01_1668424113#9_2446710242 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| P2P framework for data distribution. 3. Titan Distributed Graph Database
Titan is one of the most scalable open source graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges. You can do it in a Hadoop (one of the best big data tools for storage and management data), extremely helpful for huge datasets. Titan is a transactional database that supports thousands of concurrent users executing complex graphs in real time. Titan has elastic and linear scalability for a growing data and user base. Implementation language – Java. Key features and benefits: Multi-datacenter high availability. Support for ACID and eventual consistency. | 3,384 | 4,074 |
msmarco_v2.1_doc_01_1668424113#10_2446711279 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| Titan has elastic and linear scalability for a growing data and user base. Implementation language – Java. Key features and benefits: Multi-datacenter high availability. Support for ACID and eventual consistency. Support for various storage backends: Apache Cassandra, Apache HBase, Oracle BerkeleyDB. Support for global graph data analytics, reporting, and ETL through integration with big data platforms: Apache Spark, Apache Giraph, and Apache Hadoop. Support for geo, numeric range, and full-text search via: | 3,862 | 4,374 |
msmarco_v2.1_doc_01_1668424113#11_2446712139 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| Support for various storage backends: Apache Cassandra, Apache HBase, Oracle BerkeleyDB. Support for global graph data analytics, reporting, and ETL through integration with big data platforms: Apache Spark, Apache Giraph, and Apache Hadoop. Support for geo, numeric range, and full-text search via: ElasticSearch, Solr, Lucene. Native integration with the TinkerPop graph stack: Gremlin graph query language, Gremlin graph server, Gremlin applications. 4. ArangoDB
ArangoDB is a very fast solution thanks to its C++ core. | 4,074 | 4,597 |
msmarco_v2.1_doc_01_1668424113#12_2446713005 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| ElasticSearch, Solr, Lucene. Native integration with the TinkerPop graph stack: Gremlin graph query language, Gremlin graph server, Gremlin applications. 4. ArangoDB
ArangoDB is a very fast solution thanks to its C++ core. You can use ArangoDB as a specialized database for key/values, documents or graphs and mix up those data models in one query. ArangoDB is designed as a native multi-model database that allows you model your data in a very flexible way and support a large number of use cases. You can extend ArangoDB to suit better your needs. You can add logic through Foxx, a Google V8 powered JavaScript framework for database microservices, providing full access to the functionality of ArangoDB on a C++ level. Key features and benefits: | 4,375 | 5,123 |
msmarco_v2.1_doc_01_1668424113#13_2446714097 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
About The Author
| You can use ArangoDB as a specialized database for key/values, documents or graphs and mix up those data models in one query. ArangoDB is designed as a native multi-model database that allows you model your data in a very flexible way and support a large number of use cases. You can extend ArangoDB to suit better your needs. You can add logic through Foxx, a Google V8 powered JavaScript framework for database microservices, providing full access to the functionality of ArangoDB on a C++ level. Key features and benefits: Pattern Matching
Shortest Path
Distributed Graphs
Nested Properties
Full blown Traversals
Transactions
Self-organizing cluster state management. Fast cluster startup and much more. 5. Apache TinkerPop
Apache TinkerPop is also a great open source graph database that is gaining popularity. Apache TinkerPop is a vendor-agnostic, graph computing framework distributed for both batch analytic graph processors (OLAP) and real-time, transactional graph databases (OLTP). | 4,598 | 5,590 |
msmarco_v2.1_doc_01_1668424113#14_2446715439 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| Pattern Matching
Shortest Path
Distributed Graphs
Nested Properties
Full blown Traversals
Transactions
Self-organizing cluster state management. Fast cluster startup and much more. 5. Apache TinkerPop
Apache TinkerPop is also a great open source graph database that is gaining popularity. Apache TinkerPop is a vendor-agnostic, graph computing framework distributed for both batch analytic graph processors (OLAP) and real-time, transactional graph databases (OLTP). When a data system is TinkerPop-enabled, you are able to model your domain as a graph and analyze it using the Gremlin graph traversal language. Moreover, all TinkerPop-enabled systems integrate with one another. Key features and benefits: Graph traversal language – Gremlin designed to work across languages. Tying together relational and graph databases in one model. | 5,123 | 5,960 |
msmarco_v2.1_doc_01_1668424113#15_2446716630 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
About The Author
| When a data system is TinkerPop-enabled, you are able to model your domain as a graph and analyze it using the Gremlin graph traversal language. Moreover, all TinkerPop-enabled systems integrate with one another. Key features and benefits: Graph traversal language – Gremlin designed to work across languages. Tying together relational and graph databases in one model. Up until Tinkerpop 3 is an option for people working in Python and other languages. Can be used for small graphs or massive graphs in a distributed environment. OLTP and OLAP traversals
Imperative and declarative traversals. Need More Open Source Graph Databases? Here are some other good solutions: | 5,590 | 6,260 |
msmarco_v2.1_doc_01_1668424113#16_2446717648 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| Up until Tinkerpop 3 is an option for people working in Python and other languages. Can be used for small graphs or massive graphs in a distributed environment. OLTP and OLAP traversals
Imperative and declarative traversals. Need More Open Source Graph Databases? Here are some other good solutions: ThingSpan – massively scalable Graph analytics platform. It is powered by Objectivity/DB, that leverages the open source stack by natively integrating with Apache Spark and the Hadoop Distributed File System. Cayley – an open-source graph inspired by the graph database behind Freebase and Google’s Knowledge Graph. OpenLink Virtuoso – a modern enterprise-grade solution for data access, virtualization, integration and multi-model relational database management. Apache Giraph – an iterative graph processing system built for high scalability. | 5,960 | 6,805 |
msmarco_v2.1_doc_01_1668424113#17_2446718861 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
About The Author
| ThingSpan – massively scalable Graph analytics platform. It is powered by Objectivity/DB, that leverages the open source stack by natively integrating with Apache Spark and the Hadoop Distributed File System. Cayley – an open-source graph inspired by the graph database behind Freebase and Google’s Knowledge Graph. OpenLink Virtuoso – a modern enterprise-grade solution for data access, virtualization, integration and multi-model relational database management. Apache Giraph – an iterative graph processing system built for high scalability. VelocityGraph – an open source C# .NET hybrid NoSQL Graph Database and Object Database that can be Distributed. high-performance data persistence. InfoGrid – an open source web Graph Database with many additional software components. About The Author
Silvia Valcheva
Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc. | 6,260 | 7,360 |
msmarco_v2.1_doc_01_1668424113#18_2446720340 | http://intellspot.com/open-source-graph-database/ | 5 Best Open Source Graph Databases: Java, C++, Python | 5 Best Open Source Graph Databases
5 Best Open Source Graph Databases
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| VelocityGraph – an open source C# .NET hybrid NoSQL Graph Database and Object Database that can be Distributed. high-performance data persistence. InfoGrid – an open source web Graph Database with many additional software components. About The Author
Silvia Valcheva
Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc. This site uses Akismet to reduce spam. Learn how your comment data is processed. | 6,805 | 7,441 |
msmarco_v2.1_doc_01_1668431996#0_2446721330 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| 10 Best Open Source Web Crawlers: Web Data Extraction Software
10 Open Source Web Crawlers: Best List
As you are searching for the best open source web crawlers, you surely know they are a great source of data for analysis and data mining. Internet crawling tools are also called web spiders, web data extraction software, and website scraping tools. The majority of them are written in Java, but there is a good list of free and open code data extracting solutions in C#, C, Python, PHP, and Ruby. You can download them on Windows, Linux, Mac or Android. Web content scraping applications can benefit your business in many ways. They collect content from different public websites and deliver the data in a manageable format. They help you monitoring news, social media, images, articles, your competitors, and etc. On this page: | 0 | 830 |
msmarco_v2.1_doc_01_1668431996#1_2446722544 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| You can download them on Windows, Linux, Mac or Android. Web content scraping applications can benefit your business in many ways. They collect content from different public websites and deliver the data in a manageable format. They help you monitoring news, social media, images, articles, your competitors, and etc. On this page: 10 of the best open source web crawlers. How to choose open source web scraping software? ( with an Infographic in PDF)
1. Scrapy
Scrapy is an open source and collaborative framework for data extracting from websites. It is a fast, simple but extensible tool written in Python. | 499 | 1,107 |
msmarco_v2.1_doc_01_1668431996#2_2446723540 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| 10 of the best open source web crawlers. How to choose open source web scraping software? ( with an Infographic in PDF)
1. Scrapy
Scrapy is an open source and collaborative framework for data extracting from websites. It is a fast, simple but extensible tool written in Python. Scrapy runs on Linux, Windows, Mac, and BSD. It extracting structured data that you can use for many purposes and applications such as data mining, information processing or historical archival. Scrapy was originally designed for web scraping. However, it is also used to extract data using APIs or as a web crawler for general purposes. Key features and benefits: | 830 | 1,472 |
msmarco_v2.1_doc_01_1668431996#3_2446724569 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Scrapy runs on Linux, Windows, Mac, and BSD. It extracting structured data that you can use for many purposes and applications such as data mining, information processing or historical archival. Scrapy was originally designed for web scraping. However, it is also used to extract data using APIs or as a web crawler for general purposes. Key features and benefits: Built-in support for extracting data from HTML/XML sources using extended CSS selectors and XPath expressions. Generating feed exports in multiple formats (JSON, CSV, XML). Built on Twisted
Robust encoding support and auto-detection. Fast and simple. 2. | 1,108 | 1,726 |
msmarco_v2.1_doc_01_1668431996#4_2446725574 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Built-in support for extracting data from HTML/XML sources using extended CSS selectors and XPath expressions. Generating feed exports in multiple formats (JSON, CSV, XML). Built on Twisted
Robust encoding support and auto-detection. Fast and simple. 2. Heritrix
Heritrix is one of the most popular free and open-source web crawlers in Java. Actually, it is an extensible, web-scale, archival-quality web scraping project. Heritrix is a very scalable and fast solution. You can crawl/archive a set of websites in no time. In addition, it is designed to respect the robots.txt exclusion directives and META robots tags. | 1,472 | 2,091 |
msmarco_v2.1_doc_01_1668431996#5_2446726580 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Heritrix
Heritrix is one of the most popular free and open-source web crawlers in Java. Actually, it is an extensible, web-scale, archival-quality web scraping project. Heritrix is a very scalable and fast solution. You can crawl/archive a set of websites in no time. In addition, it is designed to respect the robots.txt exclusion directives and META robots tags. Runs on Linux/Unixlike and Windows. Key features and benefits: HTTP authentication
NTLM Authentication
XSL Transformation for link extraction
Search engine independence
Mature and stable platform
Highly configurable
Runs from any machine
3. WebSphinix
WebSphinix is a great easy to use personal and customizable web crawler. It is designed for advanced web users and Java programmers allowing them to crawl over a small part of the web automatically. | 1,727 | 2,542 |
msmarco_v2.1_doc_01_1668431996#6_2446727790 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Runs on Linux/Unixlike and Windows. Key features and benefits: HTTP authentication
NTLM Authentication
XSL Transformation for link extraction
Search engine independence
Mature and stable platform
Highly configurable
Runs from any machine
3. WebSphinix
WebSphinix is a great easy to use personal and customizable web crawler. It is designed for advanced web users and Java programmers allowing them to crawl over a small part of the web automatically. This web data extraction solution also is a comprehensive Java class library and interactive development software environment. WebSphinix includes two parts: the Crawler Workbench and the WebSPHINX class library. The Crawler Workbench is a good graphical user interface that allows you to configure and control a customizable web crawler. The library provides support for writing web crawlers in Java. | 2,091 | 2,944 |
msmarco_v2.1_doc_01_1668431996#7_2446729036 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| This web data extraction solution also is a comprehensive Java class library and interactive development software environment. WebSphinix includes two parts: the Crawler Workbench and the WebSPHINX class library. The Crawler Workbench is a good graphical user interface that allows you to configure and control a customizable web crawler. The library provides support for writing web crawlers in Java. WebSphinix runs on Windows, Linux, Mac, and Android IOS. Key features and benefits: Visualize a collection of web pages as a graph
Concatenate pages together for viewing or printing them as a single document
Extract all text matching a certain pattern. Tolerant HTML parsing
Support for the robot exclusion standard
Common HTML transformations
Multithreaded Web page retrieval
4. Apache Nutch
When it comes to best open source web crawlers, Apache Nutch definitely has a top place in the list. | 2,542 | 3,438 |
msmarco_v2.1_doc_01_1668431996#8_2446730324 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| WebSphinix runs on Windows, Linux, Mac, and Android IOS. Key features and benefits: Visualize a collection of web pages as a graph
Concatenate pages together for viewing or printing them as a single document
Extract all text matching a certain pattern. Tolerant HTML parsing
Support for the robot exclusion standard
Common HTML transformations
Multithreaded Web page retrieval
4. Apache Nutch
When it comes to best open source web crawlers, Apache Nutch definitely has a top place in the list. Apache Nutch is popular as a highly extensible and scalable open source code web data extraction software project great for data mining. Nutch can run on a single machine but a lot of its strength is coming from running in a Hadoop cluster. Many data analysts and scientists, application developers, and web text mining engineers all over the world use Apache Nutch. Apache Nutch is a cross-platform solution written in Java. Key features and benefits: | 2,944 | 3,891 |
msmarco_v2.1_doc_01_1668431996#9_2446731663 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Apache Nutch is popular as a highly extensible and scalable open source code web data extraction software project great for data mining. Nutch can run on a single machine but a lot of its strength is coming from running in a Hadoop cluster. Many data analysts and scientists, application developers, and web text mining engineers all over the world use Apache Nutch. Apache Nutch is a cross-platform solution written in Java. Key features and benefits: Fetching and parsing are done separately by default
Supports a wide variety of document formats: Plain Text, HTML/XHTML+XML, XML, PDF, ZIP and many others
Uses XPath and namespaces to do the mapping
Distributed filesystem (via Hadoop)
Link-graph database
NTLM authentication
5. Norconex
A great tool for those who are searching open source web crawlers for enterprise needs. Norconex allows you to crawl any web content. You can run this full-featured collector on its own, or embed it in your own application. | 3,439 | 4,402 |
msmarco_v2.1_doc_01_1668431996#10_2446733019 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Fetching and parsing are done separately by default
Supports a wide variety of document formats: Plain Text, HTML/XHTML+XML, XML, PDF, ZIP and many others
Uses XPath and namespaces to do the mapping
Distributed filesystem (via Hadoop)
Link-graph database
NTLM authentication
5. Norconex
A great tool for those who are searching open source web crawlers for enterprise needs. Norconex allows you to crawl any web content. You can run this full-featured collector on its own, or embed it in your own application. Works on any operating system. Can crawl millions on a single server of average capacity. In addition, it has many content and metadata manipulation options. Also, it can extract page “featured” image. Key features and benefits: | 3,891 | 4,631 |
msmarco_v2.1_doc_01_1668431996#11_2446734162 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Works on any operating system. Can crawl millions on a single server of average capacity. In addition, it has many content and metadata manipulation options. Also, it can extract page “featured” image. Key features and benefits: Multi-threaded
Supports different hit interval according to different schedules
Extract text out of many file formats (HTML, PDF, Word, etc.) Extract metadata associated with documents
Supports pages rendered with JavaScript
Language detection
Translation support
Configurable crawling speed
Detects modified and deleted documents
Supports external commands to parse or manipulate documents
Many others
6. BUbiNG
BUbiNG will surprise you. It is a next-generation open source web crawler. BUbiNG is a Java fully distributed crawler (no central coordination). | 4,402 | 5,189 |
msmarco_v2.1_doc_01_1668431996#12_2446735356 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Multi-threaded
Supports different hit interval according to different schedules
Extract text out of many file formats (HTML, PDF, Word, etc.) Extract metadata associated with documents
Supports pages rendered with JavaScript
Language detection
Translation support
Configurable crawling speed
Detects modified and deleted documents
Supports external commands to parse or manipulate documents
Many others
6. BUbiNG
BUbiNG will surprise you. It is a next-generation open source web crawler. BUbiNG is a Java fully distributed crawler (no central coordination). It is able to crawl several thousands pages per second. Collect really big datasets. BUbiNG distribution is based on modern high-speed protocols so to achieve very high throughput. BUbiNG provides massive crawling for the masses. It is completely configurable, extensible with little efforts and integrated with spam detection. | 4,631 | 5,517 |
msmarco_v2.1_doc_01_1668431996#13_2446736639 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| It is able to crawl several thousands pages per second. Collect really big datasets. BUbiNG distribution is based on modern high-speed protocols so to achieve very high throughput. BUbiNG provides massive crawling for the masses. It is completely configurable, extensible with little efforts and integrated with spam detection. Key features and benefits: High parallelism
Fully distributed
Uses JAI4J, a thin layer over JGroups that handles job assignment. Detects (presently) near-duplicates using a fingerprint of a stripped page
Fast
Massive crawling. 7. GNU Wget
GNU Wget is a free and open source software tool written in C for retrieving files using HTTP, HTTPS, FTP, and FTPS. | 5,190 | 5,873 |
msmarco_v2.1_doc_01_1668431996#14_2446737714 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Key features and benefits: High parallelism
Fully distributed
Uses JAI4J, a thin layer over JGroups that handles job assignment. Detects (presently) near-duplicates using a fingerprint of a stripped page
Fast
Massive crawling. 7. GNU Wget
GNU Wget is a free and open source software tool written in C for retrieving files using HTTP, HTTPS, FTP, and FTPS. The most distinguishing feature is that GNU Wget has NLS-based message files for many different languages. In addition, it can optionally convert absolute links in downloaded documents to relative documents. Runs on most UNIX-like operating systems as well as Microsoft Windows. GNU Wget is a powerful website scraping tool with a variety of features. Key features and benefits: | 5,517 | 6,252 |
msmarco_v2.1_doc_01_1668431996#15_2446738840 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| The most distinguishing feature is that GNU Wget has NLS-based message files for many different languages. In addition, it can optionally convert absolute links in downloaded documents to relative documents. Runs on most UNIX-like operating systems as well as Microsoft Windows. GNU Wget is a powerful website scraping tool with a variety of features. Key features and benefits: Can resume aborted downloads, using REST and RANGE
Can use filename wild cards and recursively mirror directories
Supports HTTP proxies
Supports HTTP cookies
Supports persistent HTTP connections
Unattended / background operation
8. Arachnode.net
Arachnode.net is for those who are looking for open source web crawlers in is a C#. Arachnode.net is a class library which downloads content from the internet, indexes this content and provides methods to customize the process. You can use the tool for personal content aggregation or you can use the tool for extracting, collecting and parse downloaded content into multiple forms. Discovered content is indexed and stored in Lucene. | 5,873 | 6,933 |
msmarco_v2.1_doc_01_1668431996#16_2446740293 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Can resume aborted downloads, using REST and RANGE
Can use filename wild cards and recursively mirror directories
Supports HTTP proxies
Supports HTTP cookies
Supports persistent HTTP connections
Unattended / background operation
8. Arachnode.net
Arachnode.net is for those who are looking for open source web crawlers in is a C#. Arachnode.net is a class library which downloads content from the internet, indexes this content and provides methods to customize the process. You can use the tool for personal content aggregation or you can use the tool for extracting, collecting and parse downloaded content into multiple forms. Discovered content is indexed and stored in Lucene. NET indexes. Arachnode.net is a good software solution for text mining purposes as well as for learning advanced crawling techniques. Key features and benefits: .NET architecture – the most comprehensive open source C#. Configurable rules and actions
Lucene. | 6,252 | 7,191 |
msmarco_v2.1_doc_01_1668431996#17_2446741632 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| NET indexes. Arachnode.net is a good software solution for text mining purposes as well as for learning advanced crawling techniques. Key features and benefits: .NET architecture – the most comprehensive open source C#. Configurable rules and actions
Lucene. NET Integration
SQL Server and full-text indexing
.DOC/.PDF/.PPT/.XLS Indexing
HTML to XML and XHTML
Full JavaScript/AJAX Functionality
Multi-threading and throttling
Respectful crawling
Analysis services
9. OpenSearchServer
OpenSearchServer is an open source enterprise class search engine and web crawling software. It is a fully integrated and very powerful solution. One of the best solutions out there. OpenSearchServer has one of the high rated reviews on the internet. | 6,933 | 7,666 |
msmarco_v2.1_doc_01_1668431996#18_2446742768 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| NET Integration
SQL Server and full-text indexing
.DOC/.PDF/.PPT/.XLS Indexing
HTML to XML and XHTML
Full JavaScript/AJAX Functionality
Multi-threading and throttling
Respectful crawling
Analysis services
9. OpenSearchServer
OpenSearchServer is an open source enterprise class search engine and web crawling software. It is a fully integrated and very powerful solution. One of the best solutions out there. OpenSearchServer has one of the high rated reviews on the internet. It is packed with a full set of search functions and allows you to build your own indexing strategy. The web crawler includes inclusion or exclusion filters with wildcards, HTTP authentication, screenshot, sitemap, Etc. It is written in C, C++, and Java PHP and is a cross-platform solution. Key features and benefits: A fully integrated solution
The crawlers can index everything
Full-text, boolean and phonetic search
17 language options
Automatic classifications
Scheduling for periodic tasks
Parsing: | 7,191 | 8,171 |
msmarco_v2.1_doc_01_1668431996#19_2446744150 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| It is packed with a full set of search functions and allows you to build your own indexing strategy. The web crawler includes inclusion or exclusion filters with wildcards, HTTP authentication, screenshot, sitemap, Etc. It is written in C, C++, and Java PHP and is a cross-platform solution. Key features and benefits: A fully integrated solution
The crawlers can index everything
Full-text, boolean and phonetic search
17 language options
Automatic classifications
Scheduling for periodic tasks
Parsing: Office documents ( such as Word, Excel, Powerpoint), OpenOffice documents, PDF files, Web pages (HTML), RTF, plain text, audio files, metadata images and etc. 10. Nokogiri
If you use Ruby, Nokogiri could be your solution. Nokogiri can transform a webpage into a ruby object. In addition, it makes all the web crawling process really easy and simple. | 7,667 | 8,521 |
msmarco_v2.1_doc_01_1668431996#20_2446745398 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Office documents ( such as Word, Excel, Powerpoint), OpenOffice documents, PDF files, Web pages (HTML), RTF, plain text, audio files, metadata images and etc. 10. Nokogiri
If you use Ruby, Nokogiri could be your solution. Nokogiri can transform a webpage into a ruby object. In addition, it makes all the web crawling process really easy and simple. Nokogiri is an HTML, XML, SAX, and Reader parser. It has many features and the ability to search documents via XPath or CSS3 selectors is one of the best. Nokogiri is a large library and provides example usages for parsing and examining a document. This data extraction software runs on Windows, Linux, Mac OS, Ubuntu. Key features and benefits: | 8,172 | 8,867 |
msmarco_v2.1_doc_01_1668431996#21_2446746481 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Nokogiri is an HTML, XML, SAX, and Reader parser. It has many features and the ability to search documents via XPath or CSS3 selectors is one of the best. Nokogiri is a large library and provides example usages for parsing and examining a document. This data extraction software runs on Windows, Linux, Mac OS, Ubuntu. Key features and benefits: XML/HTML DOM parser which handles broken HTML
XML/HTML SAX parser
XML/HTML Push parser
XPath 1.0 support for document searching
CSS3 selector support for document searching
XML/HTML builder
XSLT transformer
How to choose the best open source website crawler? Crawling or scraping data software tools are becoming more and more popular. Hundreds of options have become available with different functionality and scalability. Choosing the right option can be a tricky business. Here are some tips to help you find out the right open source web scraping software for your needs. | 8,521 | 9,443 |
msmarco_v2.1_doc_01_1668431996#22_2446747796 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| XML/HTML DOM parser which handles broken HTML
XML/HTML SAX parser
XML/HTML Push parser
XPath 1.0 support for document searching
CSS3 selector support for document searching
XML/HTML builder
XSLT transformer
How to choose the best open source website crawler? Crawling or scraping data software tools are becoming more and more popular. Hundreds of options have become available with different functionality and scalability. Choosing the right option can be a tricky business. Here are some tips to help you find out the right open source web scraping software for your needs. Scalability
The web data extraction solution that you choose should be scalable. If your data needs are growing, the crawling tool shouldn’t slow you down. Your future data requirements should be covered. This means the website crawler architecture should permit adding extra machines and bandwidth to handle future scaling up. Distributed web crawling
It means all downloaded pages have to be distributed among many computers (even hundreds of computers) in fraction of seconds. | 8,867 | 9,923 |
msmarco_v2.1_doc_01_1668431996#23_2446749252 | http://intellspot.com/open-source-web-crawlers/ | 10 Best Open Source Web Crawlers: Web Data Extraction Software | 10 Open Source Web Crawlers: Best List
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| Scalability
The web data extraction solution that you choose should be scalable. If your data needs are growing, the crawling tool shouldn’t slow you down. Your future data requirements should be covered. This means the website crawler architecture should permit adding extra machines and bandwidth to handle future scaling up. Distributed web crawling
It means all downloaded pages have to be distributed among many computers (even hundreds of computers) in fraction of seconds. In other words, the web data extraction software should have the capability | 9,443 | 9,999 |
msmarco_v2.1_doc_01_1668445968#0_2446750201 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
Comparison Chart: Primary Data vs Secondary Data
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| Primary Data VS Secondary Data: Definition, Sources, Advantages
Primary Data VS Secondary Data
When it comes to the difference between primary data and secondary data in marketing research, many examples and types of data collection methods can be given. Primary sources of data collection have their advantages (such as addressing specific research problems) and applications in data management and storage. On the other hand, secondary data gathering has also a range of benefits, best practices, and important meaning in the marketing and data world. On this page you will learn: What is primary data and what is secondary data? Definition. Primary data vs secondary data: sources, advantages, and disadvantages. Comparison Chart – Infographic in PDF. | 0 | 754 |
msmarco_v2.1_doc_01_1668445968#1_2446751513 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
Comparison Chart: Primary Data vs Secondary Data
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| What is primary data and what is secondary data? Definition. Primary data vs secondary data: sources, advantages, and disadvantages. Comparison Chart – Infographic in PDF. For each type of business to be successful, it is absolutely crucial to have reliable information for the market and customer characteristics. This is where intensive research methods and data collection tools come to make it possible. What is Primary data? Sources, Advantages, and Disadvantages. Primary data is information collected directly from first-hand experience. | 582 | 1,127 |
msmarco_v2.1_doc_01_1668445968#2_2446752616 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
Comparison Chart: Primary Data vs Secondary Data
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| For each type of business to be successful, it is absolutely crucial to have reliable information for the market and customer characteristics. This is where intensive research methods and data collection tools come to make it possible. What is Primary data? Sources, Advantages, and Disadvantages. Primary data is information collected directly from first-hand experience. This is the information that you gather for the purpose of a particular research project. Primary data collection is a direct approach that is tailored to specific company needs. It can be a long process but does provide important first-hand information in many business cases. Primary data is the original data – from the first source. It is like raw material. | 754 | 1,489 |
msmarco_v2.1_doc_01_1668445968#3_2446753909 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
Comparison Chart: Primary Data vs Secondary Data
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| This is the information that you gather for the purpose of a particular research project. Primary data collection is a direct approach that is tailored to specific company needs. It can be a long process but does provide important first-hand information in many business cases. Primary data is the original data – from the first source. It is like raw material. Most popular examples of primary data sources are: Interview (personal interview, telephone, e-mail)
Self-administered surveys and questionnaires
Field observation
Experiments
Life histories
Action research
Case studies
Diary entries, letters, and other correspondence
Eyewitness accounts
Ethnographic research
Personal narratives, memoirs
In fact, the source of primary data is the population sample from which you gather your data. The sample is selected by some of the different types of sampling methods and techniques. Advantages of primary data: Resolve specific research issues
Performing your own research allows you to address and resolve issues specific to your own business situation. | 1,128 | 2,185 |
msmarco_v2.1_doc_01_1668445968#4_2446755538 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
Comparison Chart: Primary Data vs Secondary Data
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| Most popular examples of primary data sources are: Interview (personal interview, telephone, e-mail)
Self-administered surveys and questionnaires
Field observation
Experiments
Life histories
Action research
Case studies
Diary entries, letters, and other correspondence
Eyewitness accounts
Ethnographic research
Personal narratives, memoirs
In fact, the source of primary data is the population sample from which you gather your data. The sample is selected by some of the different types of sampling methods and techniques. Advantages of primary data: Resolve specific research issues
Performing your own research allows you to address and resolve issues specific to your own business situation. The collected information is the exact information that the researcher wants to know and he reports it in a way that benefits the specific situation in an organization. Marketers and researchers are asked to find data regarding specific markets instead of finding data for the mass market. This is the main difference from secondary data. Better accuracy
Primary data is much more accurate because it is directly collected from a given population. A higher level of control
The marketer can control easily the research design and method. | 1,489 | 2,723 |
msmarco_v2.1_doc_01_1668445968#5_2446757340 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
Comparison Chart: Primary Data vs Secondary Data
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| The collected information is the exact information that the researcher wants to know and he reports it in a way that benefits the specific situation in an organization. Marketers and researchers are asked to find data regarding specific markets instead of finding data for the mass market. This is the main difference from secondary data. Better accuracy
Primary data is much more accurate because it is directly collected from a given population. A higher level of control
The marketer can control easily the research design and method. In addition, you have a higher level of control over how the information is gathered. Up-to-date information
The primary market research is a great source of latest and up-to-date information as you collect it directly from the field in real-time. Usually, secondary data is not so up-to-date and recent. You are the owner of the information
Information collected by the researcher is their own and is typically not shared with others. Thus, the information can remain hidden from other current and potential competitors. | 2,186 | 3,245 |
msmarco_v2.1_doc_01_1668445968#6_2446758958 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
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| In addition, you have a higher level of control over how the information is gathered. Up-to-date information
The primary market research is a great source of latest and up-to-date information as you collect it directly from the field in real-time. Usually, secondary data is not so up-to-date and recent. You are the owner of the information
Information collected by the researcher is their own and is typically not shared with others. Thus, the information can remain hidden from other current and potential competitors. Disadvantages: More expensive
It could be very expensive to obtain primary data collection methods because the marketer or the research team has to start from the beginning. It means they have to follow the whole study procedure, organizing materials, process and etc. Time-consuming
It is a matter of a lot of time to conduct the research from the beginning to the end. Often it is much longer in comparison with the time needed to collect secondary data. | 2,724 | 3,702 |
msmarco_v2.1_doc_01_1668445968#7_2446760495 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
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| Disadvantages: More expensive
It could be very expensive to obtain primary data collection methods because the marketer or the research team has to start from the beginning. It means they have to follow the whole study procedure, organizing materials, process and etc. Time-consuming
It is a matter of a lot of time to conduct the research from the beginning to the end. Often it is much longer in comparison with the time needed to collect secondary data. Can have a lot of limits
Primary data is limited to the specific time, place or number of participants and etc. To compare, secondary data can come from a variety of sources to give more details. Not always possible
For example, many researches can be just too large to be performed by your company. What is Secondary Data? Sources, Advantages, and Disadvantages. | 3,245 | 4,066 |
msmarco_v2.1_doc_01_1668445968#8_2446761874 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
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| Can have a lot of limits
Primary data is limited to the specific time, place or number of participants and etc. To compare, secondary data can come from a variety of sources to give more details. Not always possible
For example, many researches can be just too large to be performed by your company. What is Secondary Data? Sources, Advantages, and Disadvantages. Secondary data is the data that have been already collected for another purpose but has some relevance to your research needs. In addition, the data is collected by someone else instead of the researcher himself. Secondary data is second-hand information. It is not used for the first time. That is why it is called secondary. | 3,702 | 4,393 |
msmarco_v2.1_doc_01_1668445968#9_2446763121 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
Comparison Chart: Primary Data vs Secondary Data
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| Secondary data is the data that have been already collected for another purpose but has some relevance to your research needs. In addition, the data is collected by someone else instead of the researcher himself. Secondary data is second-hand information. It is not used for the first time. That is why it is called secondary. Secondary data sources provide valuable interpretations and analysis based on primary sources. They may explain in detail primary sources and often uses them to support a specific thesis or a point of view. Most common examples of secondary data sources are: Previous research
Mass media products
Government reports
Official statistics
Letters
Diaries
Web information
Google Analytics or other sources that show statistics and data for digital customer experience. Historical data
Encyclopedias
Monographs
Journal articles
Biography
Research analysis
Dissertations
Works of criticism and interpretation
Financial sources such as profit and loss statements balance sheets, inventory records, sales records and etc. | 4,066 | 5,107 |
msmarco_v2.1_doc_01_1668445968#10_2446764731 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
Comparison Chart: Primary Data vs Secondary Data
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| Secondary data sources provide valuable interpretations and analysis based on primary sources. They may explain in detail primary sources and often uses them to support a specific thesis or a point of view. Most common examples of secondary data sources are: Previous research
Mass media products
Government reports
Official statistics
Letters
Diaries
Web information
Google Analytics or other sources that show statistics and data for digital customer experience. Historical data
Encyclopedias
Monographs
Journal articles
Biography
Research analysis
Dissertations
Works of criticism and interpretation
Financial sources such as profit and loss statements balance sheets, inventory records, sales records and etc. Advantages of Secondary Data: Ease of Access
The secondary data sources are very easy to access. The internet world changed how secondary research exists. Nowadays, you have so much information available just by clicking with the mouse in front of the computer. Low Cost or Free
The majority of secondary sources are absolutely free for use or at very low costs. | 4,393 | 5,470 |
msmarco_v2.1_doc_01_1668445968#11_2446766380 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
Comparison Chart: Primary Data vs Secondary Data
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| Advantages of Secondary Data: Ease of Access
The secondary data sources are very easy to access. The internet world changed how secondary research exists. Nowadays, you have so much information available just by clicking with the mouse in front of the computer. Low Cost or Free
The majority of secondary sources are absolutely free for use or at very low costs. It saves not only your money but your efforts. In comparison with primary research where you have to design and conduct a whole primary study process from the beginning, secondary research allows you to gather data without having to put any money on the table. Time-saving
As the above advantage suggests, you can perform secondary research in no time. Sometimes it is a matter of a few Google searches to find a credible source of information. Generating new insights and understandings from previous analysis
Reanalyzing old data can bring unexpected new understandings and points of view or even new relevant conclusions. | 5,107 | 6,095 |
msmarco_v2.1_doc_01_1668445968#12_2446767927 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
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| It saves not only your money but your efforts. In comparison with primary research where you have to design and conduct a whole primary study process from the beginning, secondary research allows you to gather data without having to put any money on the table. Time-saving
As the above advantage suggests, you can perform secondary research in no time. Sometimes it is a matter of a few Google searches to find a credible source of information. Generating new insights and understandings from previous analysis
Reanalyzing old data can bring unexpected new understandings and points of view or even new relevant conclusions. Larger sample size
Big datasets often use a larger sample than those that can be gathered by primary data collection. Larger samples mean that the final inference becomes much more straightforward. Longitudinal analysis
Secondary data allows you to perform a longitudinal analysis which means the studies are performed spanning over a large period of time. This can help you to determine different trends. In addition, you can find secondary data from many years back up to a couple of hours ago. | 5,471 | 6,592 |
msmarco_v2.1_doc_01_1668445968#13_2446769608 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
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| Larger sample size
Big datasets often use a larger sample than those that can be gathered by primary data collection. Larger samples mean that the final inference becomes much more straightforward. Longitudinal analysis
Secondary data allows you to perform a longitudinal analysis which means the studies are performed spanning over a large period of time. This can help you to determine different trends. In addition, you can find secondary data from many years back up to a couple of hours ago. It allows you to compare data over time. Anyone can collect the data
Secondary data research can be performed by people that aren’t familiar with the different types of quantitative and qualitative research methods. Practically, anyone can collect secondary data. Disadvantages: Not specific to your needs
Here is the main difference with the primary method. | 6,095 | 6,951 |
msmarco_v2.1_doc_01_1668445968#14_2446771028 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
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| It allows you to compare data over time. Anyone can collect the data
Secondary data research can be performed by people that aren’t familiar with the different types of quantitative and qualitative research methods. Practically, anyone can collect secondary data. Disadvantages: Not specific to your needs
Here is the main difference with the primary method. Secondary data is not specific to the researcher’s need due to the fact that it was collected in the past for another reason. That is why the secondary data might be unreliable and unuseful and in many business and marketing cases. Secondary data sources can give you a huge amount of information, but quantity does not mean appropriateness. Lack of control over data quality
You have no control over the data quality at all. In comparison, with primary methods that are largely controlled by the data-driven marketer, secondary data might lack quality. | 6,593 | 7,505 |
msmarco_v2.1_doc_01_1668445968#15_2446772509 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
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| Secondary data is not specific to the researcher’s need due to the fact that it was collected in the past for another reason. That is why the secondary data might be unreliable and unuseful and in many business and marketing cases. Secondary data sources can give you a huge amount of information, but quantity does not mean appropriateness. Lack of control over data quality
You have no control over the data quality at all. In comparison, with primary methods that are largely controlled by the data-driven marketer, secondary data might lack quality. It means the quality of secondary data should be examined in detail since the source of the information may be questionable. As you relying on secondary data for your decision-making process, you must evaluate the reliability of the information by finding out how the information was collected and analyzed. Biasness
As the secondary data is collected by someone else than you, typically the data is biased in favor of the person who gathered it. This might not cover your requirements as a researcher or marketer. Not timely
Secondary data is collected in the past which means it might be out-of-date. | 6,952 | 8,108 |
msmarco_v2.1_doc_01_1668445968#16_2446774229 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
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| It means the quality of secondary data should be examined in detail since the source of the information may be questionable. As you relying on secondary data for your decision-making process, you must evaluate the reliability of the information by finding out how the information was collected and analyzed. Biasness
As the secondary data is collected by someone else than you, typically the data is biased in favor of the person who gathered it. This might not cover your requirements as a researcher or marketer. Not timely
Secondary data is collected in the past which means it might be out-of-date. This issue can be crucial in many different situations. Not proprietary Information
Generally, secondary data is not collected specifically for your company. Instead, it is available to many companies and people either for free or for a little fee. So this is not exactly an “information advantage” for you and your competitors also have access to the data. Comparison Chart: | 7,506 | 8,484 |
msmarco_v2.1_doc_01_1668445968#17_2446775776 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
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| This issue can be crucial in many different situations. Not proprietary Information
Generally, secondary data is not collected specifically for your company. Instead, it is available to many companies and people either for free or for a little fee. So this is not exactly an “information advantage” for you and your competitors also have access to the data. Comparison Chart: Primary Data vs Secondary Data
As you see, there is a significant difference between primary data and secondary data. Still, both types have their key benefits and applications. Which type to choose? The choice between primary and secondary data in marketing research depends on several considerations such as: the purpose of the research; | 8,109 | 8,824 |
msmarco_v2.1_doc_01_1668445968#18_2446777059 | http://intellspot.com/primary-data-vs-secondary-data/ | Primary Data VS Secondary Data: Definition, Sources, Advantages | Primary Data VS Secondary Data
Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
Comparison Chart: Primary Data vs Secondary Data
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| Primary Data vs Secondary Data
As you see, there is a significant difference between primary data and secondary data. Still, both types have their key benefits and applications. Which type to choose? The choice between primary and secondary data in marketing research depends on several considerations such as: the purpose of the research; availability of financial resources and time; the degree of precision required and etc. Download the following comparison chart/infographic in PDF: Primary vs Secondary data
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Silvia Valcheva
Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc. | 8,485 | 9,319 |
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Primary Data VS Secondary Data
What is Primary data? Sources, Advantages, and Disadvantages.
What is Secondary Data? Sources, Advantages, and Disadvantages.
Comparison Chart: Primary Data vs Secondary Data
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| availability of financial resources and time; the degree of precision required and etc. Download the following comparison chart/infographic in PDF: Primary vs Secondary data
About The Author
Silvia Valcheva
Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc. Leave a Reply
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msmarco_v2.1_doc_01_1668456031#0_2446779616 | http://intellspot.com/qualitative-data-analysis-methods/ | Qualitative Data Analysis Methods And Techniques | Qualitative Data Analysis Methods And Techniques
Qualitative Data Analysis Methods And Techniques
What is qualitative data analysis?
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| Qualitative Data Analysis Methods And Techniques
Qualitative Data Analysis Methods And Techniques
As data is an invaluable source of business insight, the knowing what are the various qualitative data analysis methods and techniques has a crucial importance. There are different approaches, types of statistical methods, strategies, and ways to analyze qualitative data. Choosing the best way and technique depends on the nature of your data and the goals set in qualitative research. On this page: What is qualitative data analysis? Definition. Techniques and methods used in qualitative data analysis: list and explanation. Infographic in PDF. What is qualitative data analysis? | 0 | 680 |
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Qualitative Data Analysis Methods And Techniques
What is qualitative data analysis?
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| Definition. Techniques and methods used in qualitative data analysis: list and explanation. Infographic in PDF. What is qualitative data analysis? Let’s define it: Qualitative Data Analysis (QDA) involves the process and procedures for analyzing data and providing some level of understanding, explanation, and interpretation of patterns and themes in textual data. Just to recall that qualitative data is data that can’t be expressed as a number. Qualitative data consist of words, pictures, observations, and symbols, not numbers. For more details see our post qualitative vs quantitative data. | 534 | 1,130 |
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Qualitative Data Analysis Methods And Techniques
What is qualitative data analysis?
Qualitative Data Analysis Methods And Techniques
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| Let’s define it: Qualitative Data Analysis (QDA) involves the process and procedures for analyzing data and providing some level of understanding, explanation, and interpretation of patterns and themes in textual data. Just to recall that qualitative data is data that can’t be expressed as a number. Qualitative data consist of words, pictures, observations, and symbols, not numbers. For more details see our post qualitative vs quantitative data. Generally, QDA has 4 main steps: Becoming familiar with your data. You need to read and re-read the data, write down detailed notes and impressions, and deciding which pieces of data possess value. Coding into themes – this means to create categories and subcategories. Each category is an example of some thematic idea. | 680 | 1,451 |
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Qualitative Data Analysis Methods And Techniques
What is qualitative data analysis?
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| Generally, QDA has 4 main steps: Becoming familiar with your data. You need to read and re-read the data, write down detailed notes and impressions, and deciding which pieces of data possess value. Coding into themes – this means to create categories and subcategories. Each category is an example of some thematic idea. The goal is to group data associated with some thematic idea which allows them to be examined together. The list of build themes represents your first set of codes. Searching for patterns and connections – looking for the relative importance of data and identifying relationships between data sets or themes. Interpretation of the data and drawing conclusions. Qualitative Data Analysis Methods And Techniques
There are a wide variety of qualitative data analysis methods and techniques and the most popular and best known of them are: | 1,130 | 1,987 |
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Qualitative Data Analysis Methods And Techniques
What is qualitative data analysis?
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| The goal is to group data associated with some thematic idea which allows them to be examined together. The list of build themes represents your first set of codes. Searching for patterns and connections – looking for the relative importance of data and identifying relationships between data sets or themes. Interpretation of the data and drawing conclusions. Qualitative Data Analysis Methods And Techniques
There are a wide variety of qualitative data analysis methods and techniques and the most popular and best known of them are: 1. Grounded Theory Analysis
The grounded analysis is a method and approach that involves generating a theory through the collection and analysis of data. That theory explains how an event or aspect of the social world “works”. Grounded theory is one of the most popular qualitative strategies that leads to a general, abstract theory of a process or action grounded in the views of people that participate in a study. The main feature of the grounded theory, which differentiates it from other qualitative data analysis methods is its focus on theory development. | 1,452 | 2,551 |
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| 1. Grounded Theory Analysis
The grounded analysis is a method and approach that involves generating a theory through the collection and analysis of data. That theory explains how an event or aspect of the social world “works”. Grounded theory is one of the most popular qualitative strategies that leads to a general, abstract theory of a process or action grounded in the views of people that participate in a study. The main feature of the grounded theory, which differentiates it from other qualitative data analysis methods is its focus on theory development. Grounded theory technique is very suitable to use, especially when you have to analyze large quantities of unstructured qualitative data. ( to know what unstructured data mean see our post unstructured data examples ). Key data collection methods and techniques that Grounded theory uses: Interview with open-ended questions
Focus groups
Participant observation
Study of texts
Note: data analysis in Grounded theory happens at the same time as data collection. | 1,987 | 3,011 |
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Qualitative Data Analysis Methods And Techniques
What is qualitative data analysis?
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| Grounded theory technique is very suitable to use, especially when you have to analyze large quantities of unstructured qualitative data. ( to know what unstructured data mean see our post unstructured data examples ). Key data collection methods and techniques that Grounded theory uses: Interview with open-ended questions
Focus groups
Participant observation
Study of texts
Note: data analysis in Grounded theory happens at the same time as data collection. It means you shouldn’t wait until all your data is collected and then analyze it. The methods have to be flexible and change when the data show a new theory or direction. 2. Content Analysis
Content analysis is one of the most widely used qualitative data techniques for interpreting meaning from text data and thus identify important aspects of the content. It includes processes and procedures for the categorization of text data for the purpose of classification and summarization. | 2,551 | 3,496 |
msmarco_v2.1_doc_01_1668456031#7_2446789069 | http://intellspot.com/qualitative-data-analysis-methods/ | Qualitative Data Analysis Methods And Techniques | Qualitative Data Analysis Methods And Techniques
Qualitative Data Analysis Methods And Techniques
What is qualitative data analysis?
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| It means you shouldn’t wait until all your data is collected and then analyze it. The methods have to be flexible and change when the data show a new theory or direction. 2. Content Analysis
Content analysis is one of the most widely used qualitative data techniques for interpreting meaning from text data and thus identify important aspects of the content. It includes processes and procedures for the categorization of text data for the purpose of classification and summarization. The text can be various formats such as documents, pictures, video, audio, and etc. The most popular application of content analysis nowadays is in analyzing the social media field. It is used to quantify patterns and trends in social media communication. You will be surprised with the broad range of content analysis software solutions today. They help you to examine almost any type of unstructured text data such as business documents, emails, social media, chats, comments, news, blogs, and much more. | 3,012 | 4,003 |
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Qualitative Data Analysis Methods And Techniques
What is qualitative data analysis?
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| The text can be various formats such as documents, pictures, video, audio, and etc. The most popular application of content analysis nowadays is in analyzing the social media field. It is used to quantify patterns and trends in social media communication. You will be surprised with the broad range of content analysis software solutions today. They help you to examine almost any type of unstructured text data such as business documents, emails, social media, chats, comments, news, blogs, and much more. In addition, the machine learning classification algorithms can significantly increase the amount of text which can be labeled and classified. Key purposes of content analysis are: To find out important aspects of the content. To present them in a clear and effective way. To support some argument. | 3,496 | 4,302 |
msmarco_v2.1_doc_01_1668456031#9_2446791845 | http://intellspot.com/qualitative-data-analysis-methods/ | Qualitative Data Analysis Methods And Techniques | Qualitative Data Analysis Methods And Techniques
Qualitative Data Analysis Methods And Techniques
What is qualitative data analysis?
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| In addition, the machine learning classification algorithms can significantly increase the amount of text which can be labeled and classified. Key purposes of content analysis are: To find out important aspects of the content. To present them in a clear and effective way. To support some argument. In fact, content analysis is not a single method. It has popular techniques and approaches: conventional, directed, or summative. The main differences among the techniques are origins of codes and coding schemes. 3. | 4,003 | 4,518 |
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Qualitative Data Analysis Methods And Techniques
What is qualitative data analysis?
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| In fact, content analysis is not a single method. It has popular techniques and approaches: conventional, directed, or summative. The main differences among the techniques are origins of codes and coding schemes. 3. Narrative analysis
Narrative analysis is also one of the best-known qualitative data analysis methods and techniques. Narrative analysis is an approach to analyzing different stories people create. The purpose is to understand how people form meaning in their lives as narratives. It uses various types of stories such as autobiography, family stories, photos, life experience, as the units of analysis. It focuses on the ways in which people create and use different stories to interpret and explain the daily life and the world. | 4,302 | 5,049 |
msmarco_v2.1_doc_01_1668456031#11_2446794081 | http://intellspot.com/qualitative-data-analysis-methods/ | Qualitative Data Analysis Methods And Techniques | Qualitative Data Analysis Methods And Techniques
Qualitative Data Analysis Methods And Techniques
What is qualitative data analysis?
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| Narrative analysis
Narrative analysis is also one of the best-known qualitative data analysis methods and techniques. Narrative analysis is an approach to analyzing different stories people create. The purpose is to understand how people form meaning in their lives as narratives. It uses various types of stories such as autobiography, family stories, photos, life experience, as the units of analysis. It focuses on the ways in which people create and use different stories to interpret and explain the daily life and the world. What can we discover from the narrative analysis? An interesting knowledge and understandings about people, their culture and how they are changing. It helps us making sense of the past and create meanings of it. Helps in organizing information about how humans interpret events. Show us the values and beliefs that guide those interpretations. | 4,519 | 5,394 |
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