Hierarchical Dataset Retrieval via Dynamic Subsetting
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Solution Overview
Problem
Existing database management systems (DBMS) are inefficient in selectively retrieving data from large hierarchical industrial asset datasets, particularly in dividing and transmitting data subsets based on user-defined criteria, such as asset layers and properties, which is crucial for efficient data management in industrial sites.
Innovation Solution
A method is introduced that allows users to retrieve a hierarchical dataset by specifying search parameters and pagination criteria, dividing the dataset into multiple data subsets based on threshold sizes and asset layer values, and transmitting these subsets sequentially, enabling controlled and efficient data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the entire hierarchical dataset is retrieved at once, then complete data is obtained, but the data transfer burden and processing complexity increase significantly
Solution Approach 1:
The hierarchical dataset is divided into multiple data subsets based on pagination criteria and threshold sizes. Each subset contains a manageable portion of the data (e.g., specific asset layers or property groups), allowing incremental retrieval and processing while maintaining data completeness through systematic segmentation of the overall dataset.
2Loss of energy
If data is divided into multiple subsets, then data transfer burden is reduced, but the retrieval process complexity increases
Solution Approach 1:
The system dynamically adjusts data subset generation based on user-defined pagination criteria and threshold sizes. The retrieval process adapts to different query requirements by flexibly partitioning data according to asset layers, properties, or custom parameters, optimizing the balance between transfer efficiency and process complexity for each specific retrieval scenario.
3Productivity
If selective retrieval based on search parameters is implemented, then retrieval efficiency improves, but the system complexity increases
Solution Approach 1:
The system applies different retrieval strategies to different portions of the hierarchical dataset based on local characteristics. Search parameters target specific asset layers, properties, or data regions, allowing efficient selective retrieval of relevant data while maintaining simplified processing for each localized query. The hierarchical structure enables targeted access to specific data regions without requiring complex global processing.
Data Source
AI summary
In some implementations, the method includes receiving data characterizing a user request indicative of retrieval of a portion of a hierarchical dataset associated with a hierarchical industrial asset and stored in a partition of a database. The user request includes pagination criteria and a search parameter. The method also includes selecting the portion of the hierarchical dataset based on the search parameter. The method further includes generating a plurality of data subsets from at least the portion of the hierarchical dataset. The searching is based on a threshold data subset size included in the pagination criteria. The generating includes dividing the portion of the hierarchical subset into the plurality of data subsets. The size of each data subset of the plurality of data subsets is less than the threshold data subset size. The method further includes providing the plurality of data subsets.