Database Workload Term Vector Modeling for Resource Allocation
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Solution Overview
Problem
Current database workload analysis methods model resource usage without identifying specific causes, resulting in limited insight into relationships between workload factors and resource demands, leading to ineffective resource allocation and management.
Innovation Solution
The system represents database queries as term vectors in a multidimensional space, allowing for the calculation of workload similarity scores through classical information retrieval techniques, enabling the characterization of workloads and grouping of similar queries to improve resource management and application support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If database workload analysis models resource usage without identifying specific causes, then resource allocation can be performed, but insight into relationships between workload factors and resource demands is limited
Solution Approach 1:
The patent introduces term vectors as an intermediary representation between database queries and resource usage metrics. Each query is transformed into a term vector that captures its semantic meaning, allowing the system to analyze workload patterns without directly modeling complex cause-effect relationships. This intermediary representation enables insight generation while avoiding the complexity of direct causal modeling.
Solution Approach 2:
The patent changes the parameter representation from raw query text to term vectors in a multidimensional space. By transforming queries into vector representations with specific dimensions (terms) and values (weights), the system enables mathematical operations for similarity calculation and pattern recognition, thereby improving analytical insight without increasing modeling complexity.
2Measurement precision
If database queries are represented as term vectors in multidimensional space, then workload similarity can be calculated, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-computing term vectors for database queries and storing them in a multidimensional space. This allows similarity calculations to be performed efficiently using standard vector operations rather than requiring complex query analysis at runtime. The preliminary transformation enables precise similarity measurement while reducing computational complexity during actual workload analysis.
3Loss of information
If classical information retrieval techniques are applied to database workload analysis, then workload characterization improves, but the approach may not directly address database-specific performance factors
Solution Approach 1:
The patent applies universality by using classical information retrieval techniques (term vectors, similarity calculation) that can be applied to general text analysis and adapting them for database workload characterization. This multi-functional approach allows the same mathematical framework to handle both general pattern recognition and database-specific performance analysis, improving workload characterization while maintaining adaptability to database contexts.
Data Source
AI summary
Techniques for managing database workloads using similarity measures based on queries executed are described. Classical techniques from information retrieval are applied to the domain of database workload management. Specifically, the technique of using document term vectors to compute similarity measures are applied using the conceptual mapping of SQL workloads as “documents” composed of SQL queries as “terms.” The techniques include generating two or more sets of workloads with each workload representing a set of queries executed on at least one database. Based on the sets of workloads, workload term vectors are calculated that represent the set of queries executed on the database. Then, based on the calculated workload vectors, a similarity score is generated between the two or more sets of workloads.