Double Entry Transition Table for Event Data Querying
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Solution Overview
Problem
The existing systems for recording and querying events in data structures face challenges such as unstructured data, leading to complex queries, differing analyst interpretations, and difficulties in tracking net changes over time, along with potential errors in data recording.
Innovation Solution
A double entry transition table data structure is implemented, where each event is recorded twice, once as a transition from one state to another and once in reverse, allowing for a net change calculation and facilitating simpler and standardized querying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If events are recorded in a simple flat structure, then data storage is simple, but querying becomes complex and inconsistent
Solution Approach 1:
The patent segments each event into multiple structured fields including event type, entity ID, attribute name, old value, new value, and timestamp. This segmentation transforms unstructured event data into a query-friendly format where specific attributes can be efficiently filtered and aggregated, resolving the contradiction between simple storage and complex querying.
Solution Approach 2:
The patent adds temporal dimension by recording old values and new values for attributes, creating a before-after state transition structure. This dimensional transformation enables efficient calculation of net changes and state transitions without requiring complex queries over raw event streams.
2Adaptability or versatility
If different analysts use different query approaches, then flexibility is high, but result consistency deteriorates
Solution Approach 1:
The patent enforces a homogeneous data structure where all events are recorded with the same set of standardized fields (event type, entity ID, attribute name, old value, new value, timestamp). This homogenization ensures that different analysts querying the same data will always get consistent results, while still allowing flexible query construction.
3Measurement precision
If net change tracking is implemented manually, then accuracy can be maintained, but computational overhead increases
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing the old value and new value for each attribute change in the event structure. This allows net change to be computed simply by subtracting old value from new value during queries, eliminating the need for complex manual tracking while maintaining accuracy and improving execution speed.
4Speed
If data structure is optimized for specific queries, then query performance improves, but adaptability to new event types decreases
Solution Approach 1:
The patent creates a universal event structure that can accommodate multiple event types (user actions, system events, business transactions) through a single standardized schema. The flexible field design allows the same structure to handle diverse event types while maintaining optimized query performance through consistent field naming and organization.
Data Source
AI summary
It may be desired to query a large record of events to retrieve certain segments of the data that provide information of interest. However, the data might not be structured such that it is conducive to generate/execute queries that inquire about net changes of events over time. Query language may also be interpreted differently by different analysts. In some embodiments data related to events is assembled into a data structure having a format logically equivalent to a double entry transition table. Each event may have two entries in the table: a first entry representing the event as a transition from a previous state to a current state, and a second entry representing the reverse transition of the first entry. In some embodiments, a query executor tool may be provided as a graphical user interface for entering information of interest to be queried.