Analytics Engine Selection via Comparative Reference Data Analysis
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Solution Overview
Problem
Selecting an appropriate analytics engine for a specific task or purpose is challenging due to unclear performance with respect to target data sets, often leading to wasted resources or invalid results, as existing methods do not adequately consider the properties and resource constraints of the target data set.
Innovation Solution
A comparative analysis of candidate analytics engines is performed by ingesting reference data into multiple engines to compile characteristic data, which includes metrics such as memory usage, processor time, and precision, to determine distinct attributes and select the most suitable engine for utilization based on these criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an analytics engine is selected without comparative analysis, then the selection process is simple and fast, but the analytic performance and accuracy cannot be guaranteed
Solution Approach 1:
The patent performs preliminary comparative analysis by ingesting reference data into multiple candidate analytics engines before actual deployment. This advance evaluation compiles characteristic data including memory usage, processor time, and precision metrics, allowing the system to pre-determine the most suitable engine for specific data sets and task requirements, thereby ensuring reliable analytic performance without complexity during runtime
Solution Approach 2:
The system automatically executes the comparative analysis process without manual intervention. The analytics engine selection management module autonomously ingests reference data, runs multiple candidate engines, compiles characteristic data, and determines the optimal engine based on predefined criteria such as precision, memory usage, and processor time, eliminating the need for manual evaluation while ensuring reliable performance
2Measurement precision
If multiple analytics engines are tested with reference data, then the analytic accuracy and compatibility improve, but the processing time and resource consumption increase
Solution Approach 1:
The patent applies partial action by selectively testing only a subset of available analytics engines against the reference data rather than exhaustively evaluating all possible engines. The system identifies candidate engines based on task requirements and evaluates only those most likely to perform well, compiling characteristic data for a limited set of engines to achieve sufficient analytic accuracy without excessive processing time
Solution Approach 2:
The comparative analysis using reference data is performed as a preliminary step before actual analytics tasks. By pre-evaluating candidate engines on representative reference data and compiling their characteristic performance metrics, the system establishes a performance baseline that guides subsequent engine selection, avoiding the need to re-evaluate engines for each new task and thereby reducing overall processing time
3Loss of energy
If candidate analytics engines are evaluated on resource usage, then resource efficiency improves, but the complexity of evaluation criteria increases
Solution Approach 1:
The patent transforms the evaluation of multiple resource usage parameters into a unified framework by normalizing diverse metrics such as memory usage, processor time, and disk I/O into comparable performance scores. The system changes the parameter representation from raw resource consumption values to standardized efficiency metrics that can be directly compared across different analytics engines, improving resource efficiency evaluation without overwhelming complexity
Solution Approach 2:
The patent creates a universal evaluation framework that handles multiple resource usage types (memory, processor, disk I/O) through a single multi-functional assessment mechanism. The analytics engine selection management module applies the same evaluation process to different resource metrics, compiling characteristic data that integrates all resource usage aspects into a unified set of performance criteria, thereby achieving comprehensive resource efficiency evaluation with manageable complexity
Data Source
AI summary
Disclosed aspects relate to analytics engine selection management. A set of reference data may be ingested by a first analytics engine to compile a first set of characteristic data. The set of reference data may be ingested by a second analytics engine to compile a second set of characteristic data. The first set of characteristic data may be compiled for the first analytics engine. The second set of characteristic data may be compiled for the second analytics engine. A set of distinct attributes related to the first and second analytics engines may be determined based on the first and second sets of characteristic data. An analytics engine selection operation may be executed.