Time Series Exploration System for Unstructured Data Analysis
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Solution Overview
Problem
Organizations face challenges in performing time series analysis on unstructured data sets due to their non-hierarchical structure, which prevents existing time series analysis operators from functioning effectively.
Innovation Solution
A system and method that analyzes unstructured time-stamped data to identify potential hierarchical structures, recommends suitable structures based on selected time series analysis functions, and structures the data accordingly to facilitate hierarchical time series analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If time series analysis operators are configured to operate on hierarchically organized time series data, then the analysis accuracy and effectiveness are improved, but the data structure complexity increases
Solution Approach 1:
The system performs preliminary analysis of unstructured time-stamped data to identify potential hierarchical structures before the actual time series analysis is executed. This includes analyzing data distributions, identifying temporal patterns, and proposing hierarchical configurations in advance, so that when analysis operators are applied, the data is already optimally structured for their operation
Solution Approach 2:
The system introduces an intermediary processing layer between raw unstructured data and hierarchical time series analysis operators. This intermediary automatically structures unstructured time-stamped data into hierarchical formats by identifying temporal patterns and relationships, thereby enabling standard analysis operators to function effectively without requiring manual data restructuring
2Adaptability or versatility
If unstructured time stamped data is transformed into structured hierarchical form, then the compatibility with analysis operators is improved, but the data processing time increases
Solution Approach 1:
The system performs preliminary structuring of unstructured time-stamped data by analyzing distributions and identifying temporal patterns before analysis operators are applied. This advance preparation includes proposing hierarchical structures and organizing data accordingly, so that when operators are executed, the data is already in compatible format, reducing overall processing time
Solution Approach 2:
The system enables unstructured time-stamped data to self-organize into hierarchical structures through automated analysis of its own distribution patterns and temporal characteristics. The data essentially structures itself by identifying inherent patterns without requiring extensive external intervention or manual reorganization, thereby reducing processing overhead
3Measurement precision
If multiple potential hierarchical structures are identified and recommended, then the analysis accuracy is improved, but the system complexity increases
Solution Approach 1:
The system segments the hierarchical structure identification process into distinct analytical components: analyzing data distributions, identifying temporal patterns, evaluating candidate structures, and ranking recommendations. This segmentation allows each component to be optimized independently while working together to provide accurate hierarchical structure recommendations for time series analysis
Data Source
AI summary
Systems and methods are provided for analyzing unstructured time stamped data. A distribution of time-stamped data is analyzed to identify a plurality of potential time series data hierarchies for structuring the data. An analysis of a potential time series data hierarchy may be performed. The analysis of the potential time series data hierarchies may include determining an optimal time series frequency and a data sufficiency metric for each of the potential time series data hierarchies. One of the potential time series data hierarchies may be selected based on a comparison of the data sufficiency metrics. Multiple time series may be derived in a single-read pass according to the selected time series data hierarchy. A time series forecast corresponding to at least one of the derived time series may be generated.