Distributed Interaction Table for Real-Time Data Correlation
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Solution Overview
Problem
Existing data management systems struggle to efficiently process and analyze interaction data from various sources, as data arrives in fragments and different consumer applications require different formats, making it inflexible to handle and process.
Innovation Solution
A distributed computing system implements the map-reduce algorithm to map raw interaction data into entities, which are then correlated and stored in an interaction table, allowing for flexible processing and consumption by various data consumer applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If interaction data is stored in predefined output format, then data structure is standardized, but flexibility for different data consumer applications is reduced
Solution Approach 1:
The system implements a universal interaction table structure that can serve multiple data consumer applications with different analysis requirements. The table stores interaction data in a standardized format that accommodates various consumer needs without requiring separate predefined output formats for each application type, thereby achieving multi-functionality and reducing format management complexity.
Solution Approach 2:
The system dynamically adapts the interaction table structure based on the specific needs of different data consumer applications. Rather than using fixed predefined output formats, the system can flexibly modify and query the interaction table in different ways depending on the consumer's requirements, enabling dynamic adaptation to various analytical needs while maintaining a single unified data structure.
2Productivity
If all interaction data is collected before processing, then data completeness is improved, but processing time is increased
Solution Approach 1:
The system performs preliminary actions by immediately processing and storing interaction data as it arrives in the interaction table, rather than waiting for complete data collection. This allows real-time or near-real-time processing of interaction events, significantly reducing processing time and enabling timely analysis while the data is still fresh and relevant.
Solution Approach 2:
The system maintains continuous processing of interaction data as it streams in, rather than batch processing after collection. The interaction table continuously accepts and stores new interaction records, enabling uninterrupted processing flow that improves productivity while eliminating waiting time associated with complete data collection.
3Productivity
If interaction data is processed in fragments, then processing time is reduced, but data consistency and correlation are compromised
Solution Approach 1:
The system segments interaction data into discrete, manageable records that can be processed independently as they arrive. Each interaction fragment is immediately processed and stored as a separate record in the interaction table, enabling efficient parallel processing while maintaining the ability to correlate related data through common identifiers and timestamps, thus resolving the contradiction between processing efficiency and data consistency.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor and verify data consistency as fragments are processed. The interaction table structure includes mechanisms to track and correlate interaction fragments based on their relationships, ensuring data consistency and accuracy are maintained even when processing occurs in fragments rather than as complete datasets.
Data Source
AI summary
A system or method may receive, on a distributed computing system comprising a plurality of computing nodes, raw interaction data describing at least one interaction between two or more parties. The distributed computing may implement a map-reduce algorithm to map the raw interaction data into one or more entities, wherein each entity describes an attribute of an interaction. The distributed computing system my further update an interaction table to include each of the entities, wherein entities describing attributes of the same interaction are correlated.