Targeted Multi-Dimensional Data Extraction for Real-Time Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Massive parallel distributed data processing systems face challenges in efficiently extracting and compressing large volumes of transactional data from online platforms, leading to significant data noise and increased storage requirements, which hinder real-time reporting and analytics.
Innovation Solution
A targeted multi-dimensional data extraction system that filters out irrelevant data, uses denormalized databases for efficient processing, and employs a data compression mechanism to reduce data volume by up to 160 times, allowing for near real-time reporting and focused data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If massive parallel distributed data processing systems process all transactional data, then complete data analysis is achieved, but data volume and storage requirements increase significantly
Solution Approach 1:
The patent extracts only the relevant subset of transactional data needed for specific analytical purposes using targeted multi-dimensional queries. Instead of processing all transactional data, the system identifies and extracts only the data points that satisfy specific dimensional criteria, thereby reducing data volume while maintaining analysis completeness for the intended purpose.
Solution Approach 2:
The patent segments transactional data into multiple dimensions (e.g., time, category, region, product type) and allows selective extraction along specific dimensional paths. This segmentation enables the system to process only the necessary portions of data for each analytical query rather than handling the entire dataset.
2Measurement precision
If all transactional data is retained for analysis, then comprehensive reporting capabilities are maintained, but processing time increases
Solution Approach 1:
The patent performs preliminary data organization by structuring transactional data into a multi-dimensional framework with predefined dimensions and hierarchies before analysis. This preliminary structuring allows for rapid retrieval and processing of relevant data subsets when analytical queries are executed, eliminating the need to scan through all raw transactional data during actual processing.
3Measurement precision
If detailed transactional data is stored for real-time reporting, then reporting accuracy is improved, but storage costs increase
Solution Approach 1:
The patent implements local quality by organizing data with enhanced detail and granularity at specific dimensional levels where it is most needed for accurate reporting, while maintaining summarized or aggregated data at other levels. This allows the system to maintain reporting accuracy for specific analytical perspectives without storing exhaustive detailed data across all dimensions simultaneously.
Data Source
AI summary
Methods and systems for extracting targeted data for real-time reporting are discussed. In an example, a system can include a data store, a server, and a denormalized database. The data store can maintain data created by an application. The server can be communicatively coupled to the data store. The server can include a data extraction module and a data compression module. The data extraction module can extract a subset of the data stored in the data store according to an extraction scheme. The data compression module can compress the extracted subset of the data into a set of aggregated key value pairs. The denormalized database can store the aggregated key value pairs.