AGDB Time Series Graphs and Query Structure

Active Graph Databases (AGDBs) are an innovative framework designed for efficiently managing and querying time-series data. By leveraging Active Graph Theory (AGT) and Active Graph Networks (AGN), AGDBs enable the creation of structured and synthetic relationships that can scale across various domains while maintaining efficiency in both small and large datasets.

Overview of AGDB Architecture

AGDB utilizes a hierarchical time-based structure combined with synthetic relationships to enable efficient querying and scalable handling of complex data. This design facilitates cross-domain contextual relationships, supporting advanced data interactions, rule-based querying, and scalable, efficient processing.

Architecture Diagram

graph TB
  subgraph AGDB
    AGT[Active Graph Theory]
    AGN[Active Graph Network]
    AGDBMain[(AGDB Main Database)]
    SyntheticRel[Synthetic Relationships]
    TimeHierarchy[Time Hierarchy Nodes]
    DataNodes[Data Nodes]

    AGT --> AGN
    AGN --> AGDBMain
    AGDBMain --> SyntheticRel
    AGDBMain --> TimeHierarchy
    TimeHierarchy --> DataNodes
  end

  AGDB -->|Integrates| AGT
  AGDB -->|Utilizes| AGN
  AGT -->|Provides Structure| SyntheticRel
  AGN -->|Facilitates Querying| DataNodes

Structure of AGDB Time Series Graphs

AGDB structures time-series data through hierarchical Time Nodes and Data Nodes. Synthetic relationships within the database enable efficient traversal and retrieval of specific time points or patterns, allowing AGDB to act as a powerful framework for scalable time-series querying.

AGDB Structure Diagram

graph TD
  TimeHierarchy[Time Hierarchy]
  Year[Year]
  Month[Month]
  Day[Day]
  Hour[Hour]
  Minute[Minute]
  Checkpoints[Predefined Checkpoints]
  Data[Data Nodes]

  TimeHierarchy --> Year
  Year --> Month
  Month --> Day
  Day --> Hour
  Hour --> Minute
  Minute --> Data
  TimeHierarchy --> Checkpoints
  Checkpoints --> Minute
  Checkpoints -->|Synthetically link| Hour

Hierarchical Structure: Organized from Year down to Minute, each node level enables efficient time-based navigation.
Checkpoints: Serve as reference points within the hierarchy, allowing quicker access to relevant data via synthetic pathing.
Data Nodes: Store attributes for each time interval, making each data point easily accessible.

Query Structure for AGDB

The query structure for AGDBs supports flexible access to data across predefined and synthetic relationships. Using a path-based syntax, AGDB queries are intuitive and efficient for time-series and context-rich data.

Example Query Structure

graph LR
  Year2024[2024]
  Month11[November]
  Day04[4th]
  Hour10[10:00 AM]
  Minute45[10:45 AM]
  DataNode[Data Node]

  Year2024 --> Month11
  Month11 --> Day04
  Day04 --> Hour10
  Hour10 --> Minute45
  Minute45 --> DataNode
  Checkpoint1040[Checkpoint 10:40 AM]
  Checkpoint1040 -->|+5 Minutes| Minute45

Direct Navigation: Queries traverse through the year, month, day, hour, and minute levels until reaching the target node.
Synthetic Pathing: Checkpoints at predefined intervals enable rapid traversal, allowing queries to skip to approximate points and increment from there.

Definitions and Components

AGT (Active Graph Theory)

AGT provides the foundational logic for defining and managing relationships within AGDB, modeling data as interconnected nodes with contextual relationships.

graph LR
  AGT[Active Graph Theory]
  Nodes[Data Nodes]
  Relationships[Relationships]
  ContextualInference[Contextual Inference]

  AGT --> Nodes
  AGT --> Relationships
  Relationships --> ContextualInference

Nodes: Represent data entries or entities.
Relationships: Define connections between nodes.
Contextual Inference: Adds depth to data by inferring relationships based on contextual cues.

AGN (Active Graph Networks)

AGN utilizes AGT’s principles to support querying and interaction within AGDB. Through rules and policies, AGN automates and simplifies navigation through AGDB.

graph TD
  AGN[Active Graph Network]
  QueryEngine[Query Engine]
  TraversalRules[Traversal Rules]
  SyntheticPathing[Synthetic Pathing]
  Checkpoints[Checkpoints]

  AGN --> QueryEngine
  QueryEngine --> TraversalRules
  TraversalRules --> SyntheticPathing
  SyntheticPathing --> Checkpoints

Query Engine: Processes requests by applying AGN’s traversal rules.
Traversal Rules: Define how nodes are accessed based on AGDB structure.
Synthetic Pathing: Creates shortcuts between nodes, improving query efficiency.

Example JSON Structure for AGDB

This structure organizes AGDB data, relationships, and policies into a flexible format that allows for easy traversal and analysis.

{
    "metadata": {
        "title": "BTC-USD Time Series Data",
        "source": "AGT Platform",
        "description": "Time-series AGDB for BTC-USD trading data with predefined checkpoints",
        "created_at": "2024-11-04",
        "timezone": "UTC"
    },
    "schema": {
        "entity": "BTC_USD_Data",
        "type": "TimeSeriesNode",
        "domain": "TradingData",
        "attributes": ["Time", "Node_ID", "Open", "High", "Low", "Close", "Volume"]
    },
    "data": [
        ["2024-10-14 07:30:00", "node_0001", 50, 52, 48, 51, 5000],
        ["2024-10-14 07:31:00", "node_0002", 51, 55, 43, 55, 3000]
    ],
    "relationships": [
        {
            "type": "temporal_sequence",
            "from": "node_0001",
            "to": "node_0002",
            "relationship": "next"
        }
    ],
    "policies": {
        "AGN": {
            "trading_inference": {
                "rules": {
                    "time_series_trend": {
                        "relationship": "temporal_sequence",
                        "weight_threshold": 0.5
                    },
                    "volatility_correlation": {
                        "attributes": ["High", "Low"],
                        "relationship": "correlates_with",
                        "weight_threshold": 0.3
                    }
                }
            }
        }
    }
}

Sample Queries and Structure

Basic Queries

Get Specific Time Node: Retrieve data at a particular time.
```
get-node-type ts-path {domain}/2024/11/04/10/45
```

Use Checkpoint for Efficiency:

get-node-type ts-path {domain}/2024/11/04/10/40 +5

Rule-Based Strategy Example

Apply Trading Strategy:
```
get-node-type ts-path TRADING/2024/11/04/11/45
```
This query fetches data at 11:45 AM for trading strategies.

Unified Command Structure

Core Commands and Syntax Structure

Graph Creation and Initialization
- create-graph -name "financial_time_series" -type "AGDB"
Node and Relationship Management
- create-node -id "node_001" -type "TimeSeriesNode" -attributes {...}
- create-relationship -from "node_001" -to "node_002" -type "next"
Setting Edges, Attributes, and Domains
- set-edge -from "node_001" -to "node_002" -weight 0.8
- set-attribute -node "node_001" -attributes {...}
- set-domain -graph "financial_time_series" -name "Trading"
Retrieving Nodes, Relationships, and Domains
- get-node.attribute -name "node_001"
- get-relationship -node "node_001"
- get-domain -node "node_001"
AGN/AGDB Specific Commands
- get-AGN -policy "trading_inference"
- set-AGN -policy "trading_inference" -rules {...}

Example JSON Query Logic

To optimize queries, AGDB uses a hierarchical time-based navigation structure with checkpoints for faster traversal.

Query Example for Time Range:

{
    "command": "get-node",
    "start": "2024-10-14 08:00:00",
    "end": "2024-10-14 08:30:00"
}

Relationship-Based Query for Correlation:

{
    "command": "get-relationship",
    "type": "correlates_with",
    "attributes": ["High", "Low"]
}

Conclusion

This README provides a high-level overview of AGDB architecture, query structure, and example usage. By integrating AGT and AGN, AGDB offers a powerful, scalable framework for time-series and complex data management, making it ideal for various fields, including finance and healthcare. The unified query structure allows users to access and manipulate data efficiently, making AGDB a versatile and user-friendly database solution.