Sequenced Filter Templates for Data Investigation
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Solution Overview
Problem
The increasing volume of data in databases makes it impractical for users to perform effective investigations, as inexperienced users often apply filters in the wrong order, leading to useless results and making it difficult to find target data.
Innovation Solution
The use of sequenced filter templates that apply specific filters in a predetermined order to reduce datasets effectively, allowing both expert and non-expert users to generate reduced datasets that highlight the desired target data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users apply filters to reduce datasets, then the dataset size decreases, but the risk of applying filters in the wrong order increases, leading to useless results
Solution Approach 1:
The system pre-establishes a predetermined sequence of filters based on expert knowledge before the user performs any filtering operation. This preliminary arrangement of filters in the correct order eliminates the need for users to determine the optimal sequence themselves, preventing incorrect filter application while maintaining data reduction effectiveness.
Solution Approach 2:
The system automatically applies the predetermined filter sequence without requiring user intervention in the filtering process. The query sequencer autonomously executes filters in the correct order, making the system self-sufficient and eliminating human error in filter sequencing while still achieving the desired data reduction.
2Ease of operation
If inexperienced users perform data investigations, then the ease of operation is improved, but the likelihood of analyzing data down the wrong path increases
Solution Approach 1:
The query sequencer acts as an intermediary between the user and the complex filtering process. It translates simple user requests into correctly sequenced filter applications, shielding inexperienced users from the complexity of data analysis while ensuring accurate results through its expert-programmed filter sequence.
Solution Approach 2:
The system pre-programmes the optimal filter sequence based on expert knowledge before the user interacts with it. This preliminary preparation allows inexperienced users to obtain reliable analysis results without needing to understand or determine the correct filter order, as the system has already established the proper sequence.
3Quantity of substance
If the amount of data to be analyzed increases, then the comprehensiveness of data collection is improved, but the practicality of performing investigations decreases
Solution Approach 1:
The system divides the large dataset into smaller, manageable portions by applying a sequence of filters that progressively reduce the data volume. Each filter in the predetermined sequence segments the data according to specific criteria, transforming the overwhelming large dataset into smaller, more analyzable subsets while maintaining investigation efficiency.
Solution Approach 2:
The query sequencer pre-establishes the optimal filter sequence to reduce large datasets before analysis begins. This preliminary data reduction strategy addresses the volume issue in advance, making the investigation of large datasets practical and efficient by systematically breaking them down into manageable sizes through expert-programmed filtering.
Data Source
AI summary
Systems and methods for implementing sequenced filter templates to intelligently reduce a dataset to find useful patterns and source data are disclosed. An expert investigative user may configure a filter template comprising a series of filters organized in a sequence desired by the expert user. The filter template can be customized by an end user to reduce a dataset and perform guide investigation of the reduced dataset.