| Certainly, we can flatten the JSON structure to simplify the node storage and make it CSV-friendly while maintaining the predefined schema for efficient querying. This approach should make it easier to store time-series data as rows and facilitate faster lookup and relationship traversal, especially for time-based queries. Let’s structure it to match your requirements and then break down the logic for setting up predefined checkpoints. | |
| ### Revised JSON Structure for Time-Series Data in AGDB | |
| This flattened structure will store metadata, schema definitions, and node data in a way that optimizes for simplicity and quick access. Each entry in the `nodes` section will follow a CSV-like format but retains enough structure to be directly loaded and queried. | |
| ```json | |
| { | |
| "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": [ | |
| // Flattened time-series data entries in CSV-like format | |
| ["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], | |
| // Additional entries go here | |
| ], | |
| "relationships": [ | |
| // Predefined relationships for cardinal (checkpoints) and standard nodes | |
| { | |
| "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 | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| ``` | |
| ### Explanation of Each Section | |
| 1. **Metadata**: | |
| - Provides information about the dataset, source, description, and creation timestamp. This is particularly useful for keeping track of multiple AGDBs. | |
| 2. **Schema**: | |
| - Defines the structure of each data entry (or node) in the `data` section. | |
| - The `attributes` field specifies the order of fields in the data rows, similar to a CSV header row, making it easier to map attributes to node properties. | |
| 3. **Data**: | |
| - Flattened time-series data where each entry is a row of values matching the schema's attributes. | |
| - Each entry begins with a timestamp (formatted in `YYYY-MM-DD HH:MM:SS`), followed by `Node_ID`, and then the financial data values: Open, High, Low, Close, and Volume. | |
| - This structure simplifies parsing, storage, and querying. | |
| 4. **Relationships**: | |
| - Stores predefined relationships between nodes, including temporal sequences (e.g., `next`, `previous`), which allow traversal through the time series. | |
| - Cardinal (checkpoint) nodes can be defined here, such as daily or hourly intervals, to act as reference points for efficient time-based queries. | |
| 5. **Policies**: | |
| - Specifies inference rules for AGNs that apply to this dataset. For example, relationships like `temporal_sequence` or `correlates_with` can guide AGN in deriving insights across nodes. | |
| ### Enhanced Query Logic Using Cardinal Nodes (Checkpoints) | |
| To optimize queries for large datasets, we can introduce **cardinal nodes** that act as checkpoints within the time series. Here’s how these checkpoints can be structured and utilized: | |
| 1. **Define Checkpoints**: | |
| - Create a cardinal node for each hour (or other intervals, like days) that can link to the closest time-based nodes within that period. | |
| - Example: If the dataset starts at 8:00 AM, create an hourly checkpoint at `08:00`, `09:00`, and so on, which links to the first node of that hour. | |
| 2. **Node-Checkpoint Relationships**: | |
| - Each checkpoint node will connect to the nodes within its respective hour. | |
| - For instance, `2024-10-14 08:00:00` checkpoint links to all nodes within `08:00 - 08:59`, helping you skip directly to relevant entries. | |
| 3. **Example Relationships for Checkpoints**: | |
| ```json | |
| { | |
| "relationships": [ | |
| { | |
| "type": "temporal_checkpoint", | |
| "from": "2024-10-14 08:00:00", | |
| "to": "node_0800", | |
| "relationship": "hourly_start" | |
| }, | |
| { | |
| "type": "temporal_sequence", | |
| "from": "node_0800", | |
| "to": "node_0801", | |
| "relationship": "next" | |
| } | |
| ] | |
| } | |
| ``` | |
| 4. **Querying with Checkpoints**: | |
| - When querying for a specific time, first find the nearest checkpoint. From there, navigate within the hour to locate the exact timestamp. | |
| - Example query: If searching for `2024-10-14 10:45`, start at `10:00` checkpoint and navigate forward until reaching `10:45`. | |
| ### API Queries and Command Logic | |
| Using the proposed flattened structure, we can create a simplified command set for interacting with the data. Here’s how each command might be structured and used: | |
| 1. **`create-graph`**: | |
| - Initializes a graph structure based on the schema and metadata defined in JSON. If the schema is time series, it creates relationships accordingly. | |
| 2. **`create-node`**: | |
| - Adds a new row of data to `data`, following the structure in `schema`. | |
| - Can specify relationships, such as linking a new node to the previous node in time. | |
| 3. **`get-node`**: | |
| - Retrieves the data for a specific node, either by node ID or timestamp. | |
| - Supports attribute filtering, e.g., `get-node.attribute -name "2024-10-14 08:30:00" -attributes "Open, Close"`. | |
| 4. **`set-attribute`**: | |
| - Allows updating node attributes, for example, to modify the `Close` value of a specific timestamped node. | |
| 5. **`create-relationship`**: | |
| - Defines relationships between nodes, such as `next`, `previous`, or custom relationships like volatility correlation between attributes. | |
| 6. **`get-relationship`**: | |
| - Retrieves relationships based on filters, such as `get-relationship -node_id node_0800 -type temporal_sequence`. | |
| ### Example JSON Query Logic | |
| To make queries more efficient, here’s how we might structure and execute a typical query: | |
| 1. **Query Example**: Retrieve data for a specific time range, `2024-10-14 08:00` to `2024-10-14 08:30`. | |
| - **Step 1**: Start at `08:00` checkpoint. | |
| - **Step 2**: Traverse forward, retrieving each node until reaching `08:30`. | |
| - **API Call Example**: | |
| ```json | |
| { | |
| "command": "get-node", | |
| "start": "2024-10-14 08:00:00", | |
| "end": "2024-10-14 08:30:00" | |
| } | |
| ``` | |
| 2. **Relationship-based Query Example**: Find volatility correlation nodes linked by `correlates_with`. | |
| - **Command**: | |
| ```json | |
| { | |
| "command": "get-relationship", | |
| "type": "correlates_with", | |
| "attributes": ["High", "Low"] | |
| } | |
| ``` | |
| - This command retrieves relationships based on the attributes and relationship type defined in the policies. | |
| ### Final Thoughts | |
| This flattened structure, combined with the cardinal nodes, simplifies the JSON file while retaining its flexibility for both time-series data and other structured data. By using this approach: | |
| - **Efficient Querying**: With cardinal nodes, time-based queries can jump directly to relevant checkpoints, enhancing retrieval efficiency. | |
| - **Flexible Schema**: You can still add new attributes or relationships, making the AGDB flexible for diverse datasets. | |
| - **Scalable Relationships**: With structured data stored in a CSV format, you maintain scalability while ensuring that AGNs/AGDBs can handle complex relationships. | |
| Let’s proceed with this approach, refining the query logic and API commands to ensure it covers your use case fully. | |