Data Storage System for Interaction Event Analysis
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Solution Overview
Problem
Existing methods for storing and analyzing data from interactions between external agents and systems face challenges in efficiently managing vast volumes of data, particularly in accessing and retrieving relevant information in a timely manner due to the unpredictability of significant events and limitations in computer processing power.
Innovation Solution
A method involving the definition of potential events of interest, monitoring interactions, assigning profile identities, and storing data records in a structured manner across dispersed storage devices to facilitate flexible and efficient analysis, allowing for the retrieval and construction of scenario records that include historical and contextual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from all interactions is stored in a centralized manner, then complete data availability is achieved, but access and retrieval speed deteriorates due to processing bottlenecks
Solution Approach 1:
The patent segments the centralized data storage system into multiple dispersed storage devices, each holding portions of the interaction data. This segmentation allows parallel access to different data portions simultaneously, improving retrieval speed while maintaining complete data availability across the distributed system.
Solution Approach 2:
The patent introduces a new organizational dimension by storing data in two distinct orders: by event type and by profile identity. This dual-dimensional organization enables flexible querying paths, allowing the system to retrieve data quickly based on either event characteristics or user profiles without centralized processing bottlenecks.
2Productivity
If data is stored in a fixed structure, then retrieval efficiency is improved, but flexibility to analyze different events of interest deteriorates
Solution Approach 1:
The patent creates a universal data storage structure that serves multiple analysis functions simultaneously. By organizing data both by event type and by profile identity, the system can efficiently support various analytical queries - whether analyzing specific event patterns, user behavior profiles, or combinations thereof - without requiring reorganization of the underlying data structure.
Solution Approach 2:
The patent changes the organizational parameters of data storage from a single fixed dimension to multiple dimensions (event type, profile identity, timing). This multi-parameter organization allows the system to efficiently retrieve data based on different analytical needs by varying the query parameters rather than changing the storage structure itself.
3Reliability
If all interaction data is monitored and stored, then comprehensive analysis capability is achieved, but storage and processing resource requirements increase
Solution Approach 1:
The patent extracts and stores only the essential elements of interaction data in a structured format - specifically organizing by event type and profile identity while maintaining timing information. This extraction approach captures the critical information needed for comprehensive analysis while reducing redundant data storage and improving processing efficiency.
Data Source
AI summary
Methods of storing data records produced from monitoring interactions between external agents and a system are described. The method defines specific interactions that occur between the external agents and the system as events of interest. A chain of interactions occurring during respective interaction sessions between a respective external agent and the system are monitored and events of interest occurring in the chain are determined. Data records from the monitored chain are produced, the respective data record including data identifying determined events of interest and data associated therewith. A profile identity, representative of the external agent, is assigned to each data record produced during an interaction session. Data records of individual events of interest are stored in a way ordered according to the type of event of interest and data records of events of interest occurring during an interaction session are stored in a way ordered according to assigned profile identity.