Event Sequence Analysis via State Stream Pattern Filtering
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Solution Overview
Problem
Conventional data processing systems struggle to effectively analyze event sequences due to the vast amount of data, intermingled information, and lack of structure, making it difficult to derive actionable insights from event histories.
Innovation Solution
A system that uses processors to determine state types and state keys associated with entities, generating state streams and employing pattern filters to identify matching sequences, allowing for efficient querying and analysis of event data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional data processing systems are used to analyze event sequences, then the current state of the system can be viewed, but actionable insights from event histories become difficult to discern due to data volume, intermingling, and lack of structure
Solution Approach 1:
The patent segments event data into discrete events with specific attributes (event type, entity, timestamp, etc.) and organizes them into structured event streams. This segmentation transforms unstructured data into analyzable units, enabling the system to process and extract insights from large volumes of event data without information loss.
Solution Approach 2:
The patent introduces an event processing system as an intermediary between raw event data and analysis queries. This intermediary layer standardizes events into a common format, filters and enriches event streams, and provides structured interfaces for querying, thereby enabling actionable insights to be extracted despite the large quantity of underlying data.
2Loss of information
If detailed logs or event histories are maintained to provide comprehensive information, then the data becomes more complete, but constructing queries that provide actionable insights becomes more limited and difficult
Solution Approach 1:
The patent implements self-service through automated event processing where the system automatically ingests, standardizes, and structures event data without requiring manual intervention. Event streams are automatically enriched with contextual information and organized into manageable formats, reducing the complexity of query construction while maintaining complete event information.
Solution Approach 2:
The patent changes the parameters of event data by transforming raw events into standardized event objects with consistent attributes (event ID, type, entity, timestamp, metadata). This parameter standardization enables simpler querying operations while preserving all original event information, as queries can operate on uniform event structures rather than heterogeneous log formats.
3Productivity
If event data is structured with state types and state keys, then pattern matching and filtering become more efficient, but the initial processing and organization of data becomes more complex
Solution Approach 1:
The patent applies preliminary action by pre-processing event data to extract and assign state types and state keys before queries are executed. Event streams are pre-filtered, pre-aggregated, and pre-organized into structured formats with assigned state classifications. This preliminary structuring enables efficient pattern matching and filtering operations while the initial complexity is handled once during data preparation rather than repeatedly during querying.
Data Source
AI summary
Embodiments are directed to managing event information. A plurality of events associated with entities may be provided. A plurality of state types may be determined based on the plurality of events such that each state type is associated with a state. State keys associated with each entity may be determined based on events associated with each entity and the state types. A state stream for each entity may be provided based on their state keys such that each state stream may be an ordered sequence of the keys associated with each entity. In response to a query that includes a pattern filter, the pattern filter may be employed to determine a portion of the entities based on the state stream for each entity such that the pattern filter matches the state stream for each of the portion of the entities.