Server Data Record Weighting and Scoring
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Solution Overview
Problem
Servers face challenges in efficiently processing and returning data records due to heterogeneity in data structure and content, especially when handling open database requests with few search parameters and broad search ranges, leading to computational complexity and time-intensiveness.
Innovation Solution
A server system that retrieves data records, determines weight values for their parameter values, calculates aggregated scores, and returns only the highest-scoring records to ensure efficient processing and compatibility across diverse data structures and formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the server retrieves and processes all data records matching broad search parameters, then the completeness of search results is improved, but the computational load and processing time increase significantly
Solution Approach 1:
The server performs preliminary actions by determining weight values for fields and calculating aggregated scores for all retrieved data records before the final selection. This pre-computation of scores allows for efficient filtering in subsequent steps, reducing the computational burden during the actual result generation phase while maintaining completeness of evaluation.
Solution Approach 2:
The server extracts only the necessary subset of data records that meet the search criteria and have the highest aggregated scores. By taking out only the relevant high-scoring records from the complete set of retrieved records, the server reduces processing time and resource consumption while maintaining result quality.
2Adaptability or versatility
If the server processes heterogeneous data structures with different formats and schemas, then the adaptability to diverse data sources is improved, but the device complexity increases
Solution Approach 1:
The server applies parameter changes by transforming heterogeneous data structures into a unified format through field weight determination and aggregated score calculation. By changing the representation parameters of diverse data records into a common scoring framework, the server achieves compatibility across different data structures without requiring complex case-by-case handling for each format.
Solution Approach 2:
The server implements a universal processing mechanism that handles multiple data formats and schemas through a single aggregated score calculation approach. This multi-functional system can process various heterogeneous data sources using the same weight determination and scoring logic, reducing device complexity while maintaining adaptability.
3Productivity
If the server returns a limited number of top-scoring data records, then the productivity and response time are improved, but the quantity of information returned is reduced
Solution Approach 1:
The server performs partial action by calculating aggregated scores for all retrieved records but returning only the top-N highest-scoring records. This partial execution of the full processing pipeline maintains processing efficiency while providing sufficient information quality by selecting only the most relevant results, balancing productivity with information quantity.
Data Source
AI summary
Methods, servers, and computer programs for processing multiple data records in response to a database request with search indications are provided. The server retrieves multiple data records with parameter values in the fields which correspond to the search indications indicated by the database request, determines weight values for at least two of the fields based on the corresponding parameter values of the at least two fields, calculates an aggregated score for at least a number of the retrieved data records based on the determined weight values, and returns at least a subset of the retrieved data records to the client based on the calculated aggregated scores.