Test Data Summary Accumulation via Tree Structure Segmentation
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Solution Overview
Problem
The excessive demands of storing test data for multiple clients accessing the same data pose a challenge in managing and efficiently utilizing test data generated by automatic test equipment.
Innovation Solution
A system that generates data objects from test data, arranges them in a tree or relational structure, and stores summaries within a data model, allowing clients to selectively read and store summaries while deleting the original data objects, thereby optimizing storage and access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If test data is stored for multiple clients to access, then data availability and accessibility are improved, but storage demands and resource consumption increase excessively
Solution Approach 1:
The patent segments test data into two distinct components: detailed data objects and summary information. Detailed data objects are stored in a data model accessible to multiple clients, while summary information is extracted and stored separately in a tree structure. This segmentation allows clients to access summaries without requiring access to the full detailed data, thereby reducing storage demands while maintaining data accessibility.
Solution Approach 2:
The patent extracts summary information from detailed test data objects and stores it in a separate tree structure. This extraction process removes the burden of storing complete detailed data for all clients while preserving essential information in a condensed format. Clients can access these extracted summaries independently, reducing the overall storage requirements while maintaining versatility of access.
2Loss of information
If detailed test data is retained for long-term storage, then data completeness and analysis capability are improved, but storage costs and system resource usage increase
Solution Approach 1:
The patent applies local quality by providing different levels of data detail to different clients based on their specific needs. The tree structure stores summary information that provides a condensed view, while the data model retains detailed data objects for clients requiring in-depth analysis. This local quality approach ensures data completeness for those who need it while reducing overall storage costs by not forcing all clients to access or store complete detailed data.
Solution Approach 2:
The patent creates summary copies of detailed test data and stores them in a tree structure. These summaries are condensed representations that capture essential information without requiring the full detailed data. Clients can access these copied summaries for most analysis needs, reducing storage costs while maintaining sufficient data completeness for typical use cases.
3Speed
If data objects are maintained in memory for client access, then data access speed and responsiveness are improved, but memory consumption and system overhead increase
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing summary information in a tree structure before clients need it. This summary data is readily available in an optimized format, allowing clients to access key information quickly without requiring the system to process or retrieve complete detailed data objects from storage. This preliminary preparation of summary data improves access speed while reducing the memory and system overhead of maintaining complete detailed data in active memory.
Data Source
AI summary
In one embodiment, there is disclosed a system for accumulation of summaries of test data. The system includes a data populator having code to: (1) generate data objects from the test data and store the data objects in a data model, (2) arrange the data objects in a tree structure, (3) generate summaries of the test data, (4) store the summaries of the test data in the tree structure, and (5) delete the data objects. The system also includes a number of clients in communication with the data model, the clients having code to: (1) selectively read the test data from the data objects stored in the data model, and (2) read the summaries of the test data stored in the tree structure. Other embodiments are also disclosed.