Data Aggregation Data Structure for DaaS Resource Optimization
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Solution Overview
Problem
As data volumes increase in Device-as-a-Service (DaaS) ecosystems, existing methods are inefficient in collecting and processing data to obtain aggregate measures, leading to high computational resource usage and the need for repeated aggregation operations.
Innovation Solution
A method for generating a data aggregation data structure that stores source identifiers, data category values, aggregation identifiers, and temporal indicators, allowing for the execution and storage of aggregation operations such as count, average, and variance, which reduces computational resources by pre-executing and storing aggregate measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data volumes increase in DaaS ecosystems, then more data can be collected and stored, but computational resource usage increases and processing efficiency decreases
Solution Approach 1:
The patent pre-calculates and stores aggregate measures (such as counts, sums, averages) for different data categories and time periods before they are needed for analysis. This preliminary aggregation reduces the computational burden during actual data analysis, allowing the system to efficiently handle increasing data volumes without proportionally increasing processing time or resource usage.
2Reliability
If aggregate measures are recalculated frequently, then data insights remain up-to-date, but computational resources are consumed repeatedly
Solution Approach 1:
The system pre-computes aggregate measures and stores them in an optimized data structure with temporal indicators, so that when data insights are needed, the pre-calculated values can be retrieved or incrementally updated rather than fully recalculated. This maintains data accuracy while significantly reducing repeated computational resource consumption.
3Adaptability or versatility
If detailed source data is stored and processed, then comprehensive analysis is possible, but data retrieval and processing time increase
Solution Approach 1:
The patent segments the large source data into organized categories with pre-computed aggregate measures stored in a structured format. This segmentation allows the system to retrieve only the necessary aggregated data for specific analysis needs rather than processing entire datasets, maintaining analytical comprehensiveness while dramatically reducing data retrieval and processing time.
Data Source
AI summary
A system is provided including a memory in communication with a processor. The memory is to store a data source comprising source data. The processor is to store in a row of a table: a source identifier of the data source, a data category value, and an aggregation identifier of an aggregation operation. The processor is also to execute the aggregation operation against the source data based on the data category value to obtain a measure related to the source data. In addition, the processor is to store the measure in the row, and output the table.