Data Attribute Retrieval With Hierarchical Query Planning
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Solution Overview
Problem
Existing data retrieval processes face challenges such as heterogeneity in data formats, distribution across multiple sources, and query performance optimization, leading to increased latency and resource consumption due to duplicative communications and API calls.
Innovation Solution
The system generates dependency trees for data attributes, identifies an anchor dependency tree with the longest length, and iteratively queries data sources based on the current leaf node of this tree, optimizing the retrieval process to reduce duplicative communications and conserve resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional data retrieval processes are used to obtain data attributes from multiple sources, then complete data coverage is achieved, but latency and resource consumption increase due to duplicative communications and API calls
Solution Approach 1:
The system performs preliminary actions by generating dependency trees and identifying an anchor dependency tree with the longest length before executing queries. This preliminary analysis of data attribute dependencies enables optimized query generation that avoids duplicative communications, thereby reducing latency while maintaining retrieval efficiency
Solution Approach 2:
The system segments the data retrieval process into distinct phases: generating dependency trees for each data attribute, identifying an anchor dependency tree, and iteratively generating queries based on current leaf nodes. This segmentation allows the system to systematically eliminate redundant API calls while ensuring complete data coverage across multiple sources
2Reliability
If queries are generated for each data attribute independently, then all required data attributes are retrieved, but processing and computing resources are consumed due to redundant API calls
Solution Approach 1:
The system merges redundant queries by identifying the anchor dependency tree and generating queries based on its current leaf nodes. This approach combines multiple data attribute retrieval operations into a single optimized query sequence, ensuring all required data is retrieved while minimizing processing and computing resource consumption through reduced API calls
3Adaptability or versatility
If multiple API calls are made to retrieve data attributes from different data sources, then comprehensive data coverage is achieved, but network resources are consumed
Solution Approach 1:
The system performs preliminary analysis by generating dependency trees and identifying the anchor dependency tree before executing queries. This preliminary action enables the system to determine the optimal query sequence that covers all data sources comprehensively while minimizing network resource consumption through eliminated redundant API calls
Data Source
AI summary
In some implementations, a device may receive a request to obtain a set of data attributes. The device may generate one or more hierarchical lists for respective data attributes included in the set of data attributes. The device may identify an anchor hierarchical list, from the one or more hierarchical lists, that is associated with a highest order among the one or more hierarchical lists. The device may generate a first query to a first data source indicated by a first data attribute in a top position of the anchor hierarchical list, wherein the first query requests a first one or more data attributes including the first data attribute and any other data attributes, associated with the first data source, that are in top positions of respective hierarchical lists of the one or more hierarchical lists. The device may transmit, to the first data source, the first query.