Data Analysis Segmentation for Incremental Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data analysis methods are labor-intensive and inefficient, particularly for large datasets with many dimensions, as they often require manual segmentation and full reruns for incremental updates, making it difficult to quickly determine trends and correlations.
Innovation Solution
A data analysis system that classifies attributes as dimensions or measures, generates summaries, divides data into time periods, creates segments of records with unique dimension combinations, and applies algorithms to identify trends and correlations, allowing for incremental updates and pruning of inconsequential segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation and full reruns are used for incremental updates, then data analysis can be performed, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent segments the data stream into time periods and further divides each time period into segments based on dimension combinations. This segmentation allows the system to process only relevant portions of data when updates occur, rather than performing full reruns on the entire dataset. The segmented structure enables incremental updates to be applied efficiently to specific segments while maintaining analysis accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-computing and storing segment information, including dimension combinations and associated records, before updates occur. This preliminary structuring of data into segments with unique dimension combinations allows for rapid identification and updating of only the affected segments when incremental changes are made, eliminating the need for complete reruns.
2Loss of information
If all possible dimension combinations are analyzed, then comprehensive insights are obtained, but computational complexity and resource requirements increase
Solution Approach 1:
The patent divides the comprehensive data analysis into manageable segments based on dimension combinations. Each segment contains records with specific dimension values, allowing the system to process and analyze subsets of data independently. This segmentation maintains completeness of analysis by covering all relevant dimension combinations while reducing the computational burden on any single processing unit.
Solution Approach 2:
The system applies partial action by focusing computational resources on generating and analyzing segments that are relevant to the current analysis objectives. Rather than uniformly processing all possible dimension combinations with equal depth, the system can prioritize segments based on their importance, frequency, or relevance to specific business questions, thereby reducing overall computational complexity while maintaining essential analytical coverage.
3Loss of information
If segments with small record quantities are retained, then comprehensive coverage is maintained, but noise and irrelevant data increase
Solution Approach 1:
The patent introduces a threshold parameter for the minimum quantity of records required to form a valid segment. By changing this parameter, the system can dynamically adjust the balance between data coverage and noise filtering. Segments with record quantities below the threshold are pruned and discarded, automatically filtering out noisy or irrelevant data combinations while preserving segments that meet the minimum viability criteria, thus improving the signal-to-noise ratio.
4Productivity
If a single pass through the data stream is used, then processing speed increases, but the ability to generate accurate segments may be compromised
Solution Approach 1:
The system performs preliminary actions during the single pass by pre-identifying and recording dimension combinations and their associated records. During this single traversal of the data stream, the system builds a structure that captures all necessary segment information, including which records belong to which dimension combinations. This preliminary structuring during the single pass enables accurate segment generation without requiring multiple reruns, as the segment architecture is established in advance.
Solution Approach 2:
The patent maintains continuity of useful action by processing the data stream in a single continuous pass, continuously building and updating segment information as records are encountered. This continuous processing ensures that all segments are identified and recorded in one uninterrupted traversal, maintaining both processing speed and accuracy by avoiding interruptions or repeated scans while systematically capturing all relevant segment data.
Data Source
AI summary
The present disclosure provides systems and methods for data analysis. An example method may comprise receiving a data stream comprising a plurality of records. A record of said plurality of records may comprise a plurality of attributes. The method may further comprise classifying each of said plurality of attributes as a dimension or a measure. The method may further comprise dividing said plurality of records into a plurality of time periods. The method may further comprise, for a time period of said plurality of time periods, generating one or more segments of records. Each segment of the one or more segments may comprise records having a combination of dimensions with unique values as compared to other segments of said one or more segments. The method may further comprise applying an algorithm to said one or more segments to generate an output. The method may further comprise displaying, on a graphical user interface, a graphical representation of said output to a user.