Multi-threaded Data Analytics Master View Table
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Solution Overview
Problem
The exponential increase in data and data structure updates poses challenges for data analysts, requiring recalculations and redesign of analytical reports, and complex queries to fetch relevant data, making existing data analytics inefficient.
Innovation Solution
Implementing multi-threaded data analytics through a computing system that generates a master view table, uses aspect analyzers to analyze data in parallel, and generates aspect relational view tables to determine insights, allowing for efficient data analysis and automatic updates with new data changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional single-threaded data analytics is used, then data analysis can be performed with simple processing, but analysis speed and efficiency are slow due to sequential processing
Solution Approach 1:
The patent segments the data analysis process into multiple independent threads, each handling different aspects of data processing simultaneously. The analytical report is divided into multiple segments that can be processed in parallel, with each thread independently analyzing specific data portions and contributing to the final consolidated result.
Solution Approach 2:
The patent introduces a temporal dimension by executing multiple analysis threads concurrently rather than sequentially. This transforms the single-dimensional sequential processing into multi-dimensional parallel processing, where multiple operations occur simultaneously across different time threads, dramatically improving throughput.
2Reliability
If data analysts manually re-calculate and re-design analytical reports for every data change, then analysis accuracy can be maintained, but the complexity and time required increase significantly
Solution Approach 1:
The system implements self-service through automated data change detection and trigger mechanisms. When source data changes, the system automatically detects the change, identifies affected analytical reports, and re-executes relevant analysis threads without human intervention, maintaining accuracy while eliminating manual complexity.
Solution Approach 2:
The patent establishes feedback loops where the system continuously monitors source data for changes and automatically triggers re-analysis when changes are detected. This closed-loop feedback mechanism ensures analysis accuracy is maintained through automated updates rather than manual intervention.
3Measurement precision
If complex queries are written to fetch relevant data from data repository, then data retrieval accuracy improves, but query complexity and execution time increase
Solution Approach 1:
The patent applies preliminary action by pre-fetching and caching relevant data from the data repository before analysis is needed. Data is proactively retrieved and stored in an optimized format, so when analysis threads need the data, it is already prepared and readily available, reducing both query complexity and execution time.
Data Source
AI summary
Various embodiments of systems and methods to provide multi-threaded data analytics. In one aspect, a master view table is generated by retrieving data records corresponding to a plurality of entities from one or more data source tables. A plurality of aspects are determined from the generated master view table. Further, a plurality of aspect relational view tables are generated by comparing the plurality of aspects with one another based on relationship factors. At least one insight of the data records are determined by analyzing the plurality of aspect relational view tables and the master view table in multi-thread.