COUF Data Analysis Method for Big-Data Processing Efficiency
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Solution Overview
Problem
Current big-data analysis methods, such as Hadoop and Spark, face inefficiencies due to storage requirements and redundant data processing, leading to increased processing time and resource consumption, especially when dealing with large datasets.
Innovation Solution
The COUF method involves dividing data sets into units, calculating simple statistics for each unit, and storing these in a database, allowing for efficient calculation of subsequent query statistics without re-reading the original data, thereby reducing redundant processing and storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If Hadoop stores data across multiple data nodes in a cluster, then data storage capacity and distributed processing capability are improved, but storage requirements and processing time increase due to redundant data storage and repeated computations
Solution Approach 1:
The patent pre-calculates and stores simple statistics (such as sum, count, minimum, maximum) for each data unit during the data ingestion phase. When queries are executed, the system retrieves and combines these pre-computed statistics rather than re-reading and re-processing the original data, thereby eliminating redundant computations and reducing processing time while maintaining distributed storage capabilities
2Productivity
If Spark processes data in-memory, then processing speed is improved, but memory consumption increases significantly
Solution Approach 1:
The patent extracts only the essential statistical features (sum, count, min, max) from the raw data and stores them in a compressed format. This extraction reduces the data volume from terabytes of raw data to much smaller statistic tables that can be efficiently stored in memory or on disk, enabling fast query processing without requiring large memory resources
3Productivity
If the same query is executed continually on Hadoop, then processing efficiency is improved due to cached results, but when different queries are submitted, redundant data reading and computation occur
Solution Approach 1:
The patent creates a universal statistic table that contains pre-computed simple statistics for all data units. This single statistic table serves multiple query types and can be used to answer various analytical questions without requiring separate data reads or computations. The same statistic table structure supports different query patterns, making the system universally efficient for diverse analytical workloads
Data Source
AI summary
A calculate once use forever (COUF) method to efficiently determine statistics on data records of a data set. Data records are read from an original data set and grouped together into units and simple statistic on each unit are calculated and stored in a database. When a query asking for an additional statistic comes in, the additional statistic can be determined quickly and efficiently from the already calculated unit simple statistics so that read redundancies and computational redundancies can be eliminated.