Hypotheses Aggregation for Data Analytics Efficiency
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Solution Overview
Problem
Conventional data analytics solutions face limitations due to increasing data set sizes, including inefficiencies in resource utilization and time consumption, making it difficult for business users and data scientists to execute data analytics efficiently.
Innovation Solution
The aggregation of multiple hypotheses for data analytics tasks using a hypotheses group processed together with common resources, optimizing resource consumption and reducing time costs through a hypotheses aggregation system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple hypotheses are processed separately using conventional data analytics solutions, then each hypothesis can be analyzed individually, but resource consumption increases and time consumption increases
Solution Approach 1:
The patent combines multiple hypotheses into a single aggregated hypothesis that processes multiple data analytics tasks together. Instead of executing separate analytics processes for each hypothesis, the system merges them into one unified process that shares common resources (computing resources, data access, processing logic), thereby reducing total execution time and improving productivity.
2Quantity of substance
If conventional data analytics solutions are applied to increasing data set sizes, then comprehensive analysis can be performed, but resource utilization becomes inefficient and cost calculation becomes difficult
Solution Approach 1:
The aggregated hypothesis creates a universal processing framework that handles multiple data analytics tasks through a single mechanism. This multi-functional approach allows the same computing resources and processing logic to serve multiple hypotheses simultaneously, reducing device complexity and improving resource utilization efficiency while maintaining the ability to analyze large data sets.
3Reliability
If data analytics solutions repeat processes on the same set of data, then thorough analysis can be conducted, but significant inefficiencies occur due to redundant processing
Solution Approach 1:
The system performs preliminary actions by pre-processing and preparing data once at the beginning of the aggregated hypothesis execution. Common data access patterns, transformations, and computations are performed upfront and cached or stored for reuse across multiple hypotheses within the aggregation, eliminating redundant processing while ensuring thorough analysis through repeated logical evaluation of the same prepared data.
Data Source
AI summary
Techniques for aggregating hypotheses for use in data analytics. In one example, a method comprises the following steps. A plurality of hypotheses associated with one or more data analytics tasks are stored in a storage queue. Two or more hypotheses of the plurality of hypotheses are selected for aggregation. The selected two or more hypotheses are aggregated into a hypotheses group such that the selected two or more hypotheses of the hypothesis group are processed together using one or more common resources to perform at least one of the one or more data analytics tasks.