Distributed Interactive Database for Real-Time Complex Data Processing
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Solution Overview
Problem
Current database systems are inadequate for processing vast amounts of complex data in real-time, as they require segmentation criteria to be specified in advance and lack the ability to perform set operations, limiting their capability for dynamic and interactive data analysis, especially in applications like marketing campaigns, scientific research, and speech processing.
Innovation Solution
A distributed and interactive database architecture that allows parallel and asynchronous data processing using parameterized metadata modules for customizable data processing, enabling real-time query processing and dynamic modification of selection criteria through a user interface and artificial intelligence assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If batch processing is used to process vast amounts of complex data, then data processing capability is improved, but real-time interactivity and dynamic criterion modification are lost
Solution Approach 1:
The system segments the batch processing workflow into distinct interactive stages: data loading, criterion specification, processing execution, and result analysis. Each stage can be independently controlled and modified, allowing users to intervene at any point while maintaining the overall batch processing efficiency for handling vast data volumes.
Solution Approach 2:
The system introduces dynamic criterion specification that allows users to modify segmentation and selection criteria during the processing workflow rather than requiring fixed pre-defined criteria. This dynamic adjustment capability enables real-time interactivity while the system continues to process vast amounts of complex data through its batch processing architecture.
2Productivity
If segmentation criteria are specified in advance, then processing efficiency is improved, but adaptability to dynamically determined criteria is worsened
Solution Approach 1:
The system performs preliminary data loading and preparation in batch mode, organizing vast amounts of complex data into accessible structures before processing begins. This preliminary action enables efficient subsequent processing while allowing criteria to be specified and modified interactively during the actual segmentation and analysis phases.
Solution Approach 2:
The system allows dynamic modification of segmentation parameters and selection criteria during the processing workflow. Users can adjust criterion parameters, change segmentation approaches, and modify selection rules based on emerging insights from the data, all while maintaining processing efficiency through the established batch processing framework.
3Measurement precision
If all processing is repeated with modified criteria, then processing accuracy is improved, but loss of time increases
Solution Approach 1:
The system recovers and reuses intermediate processing results, data structures, and organized information from previous processing stages when criteria are modified. Rather than discarding all previous work and starting completely anew, the system retains processed data in accessible formats that can be quickly reprocessed with new criteria, significantly reducing the time penalty of criterion modifications.
4Ease of operation
If prior art systems are used for real-time data analysis, then simplicity of data types is maintained, but capability to handle complex data types is lost
Solution Approach 1:
The system implements a universal processing framework that handles multiple complex data types (relational data, hierarchical data, network data, multimedia data) through a single integrated architecture. This multi-functional system maintains the ease of operation of simpler systems while gaining the capability to process vast amounts of complex data types through unified batch processing and interactive criterion specification.
Data Source
AI summary
The various embodiments of the invention provide a data processing system and method, for applications such as marketing campaign management, speech recognition and signal processing. An exemplary system embodiment includes a first data repository adapted to store a plurality of entity and attribute data; a second data repository adapted to store a plurality of entity linkage data; a metadata data repository adapted to store a plurality of metadata modules, with a first metadata module having a plurality of selectable parameters, received through a control interface, and having a plurality of metadata linkages to a first subset of metadata modules; and a multidimensional data structure. The control interface may modify the plurality of selectable parameters in response to received control information. A plurality of processing nodes are adapted to use the plurality of selectable parameters to assemble a first plurality of data from the first and second data repositories and from input data, to reduce the first plurality of data to form a second plurality of data, and to aggregate and dimension the second plurality of data for storage in the multidimensional data structure.