Time Series Analytics Segmentation Categorical Analysis
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Solution Overview
Problem
Existing methods for understanding dynamic behavior in systems like database management fail to provide visibility into operational behavior over long time durations, either masking fine-grain behavior with coarse-grain numerical summarization or missing important short duration events.
Innovation Solution
The method transforms fine-grain numerical measurements into operationally meaningful categorical dimensions, using categorical analysis to detect and summarize temporal patterns over long durations, maintaining a fine-grain perspective and inheriting operational meaning from numerical to categorical transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If coarse-grain numerical statistical summarization is used over long time durations, then computational efficiency is improved, but fine-grain behavior is masked and lost
Solution Approach 1:
The patent segments the time series analysis into two distinct phases: (1) coarse-grain numerical statistical summarization for computational efficiency, and (2) fine-grain categorical analysis for preserving operational meaning. This segmentation allows the system to achieve both computational efficiency and fine-grain behavior visibility by applying appropriate methods to appropriate data representations at different stages of analysis.
Solution Approach 2:
The patent transforms numerical measurements into categorical dimensions, changing the representation parameter from continuous numerical values to discrete categorical labels. This parameter change enables the system to maintain fine-grain operational meaning while working with simplified categorical data that can be efficiently analyzed over long time durations without losing important behavioral patterns.
2Measurement precision
If fine-grain numerical techniques are used to analyze time series, then fine-grain behavior is captured, but operationally meaningful behavior is not directly discovered and short duration events are missed
Solution Approach 1:
The patent applies parameter changes by transforming numerical measurements into categorical dimensions. This transformation preserves fine-grain behavior capture while simultaneously making operationally meaningful behavior directly discoverable through categorical analysis. The categorical representation enables the system to identify operationally significant patterns that would be obscured in purely numerical analysis.
Solution Approach 2:
The patent introduces categorical dimensions as an intermediary between numerical measurements and operational meaning. This intermediary layer translates raw numerical data into operationally meaningful categories, enabling the system to discover operationally significant behavior and capture short duration events that would otherwise be lost in aggregate numerical statistics.
3Loss of information
If categorical analysis is applied to long-duration time series, then operational meaning is preserved, but computational complexity increases
Solution Approach 1:
The patent segments the analytical process into two phases: first performing coarse-grain numerical statistical summarization to reduce data volume and complexity, then applying fine-grain categorical analysis only to the summarized data. This segmentation significantly reduces computational complexity while preserving operational meaning, as the categorical analysis operates on pre-processed, less complex data.
Solution Approach 2:
The patent performs preliminary coarse-grain numerical statistical summarization before applying categorical analysis. This preliminary action reduces the amount of data that requires complex categorical processing, thereby reducing overall computational complexity while still preserving operational meaning through the subsequent categorical analysis of the summarized data.
Data Source
AI summary
Various embodiments herein each include at least one of systems, methods, and software for producing operational analytics that summarize fine-grain time scale behavior over long time durations. Some such embodiments are targeted toward understanding operationally meaningful behavior of complex dynamic systems that are often only apparent at fine-grain time scales. Such behavior occurs rarely and/or only for short durations so the analytics of some embodiments cover long time durations.