Data Snippet Generation for Event Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for reviewing and investigating data files, such as video feeds, audio clips, and images, are cumbersome and time-consuming, especially when significant events occur, as users need to pause the files to document events, and existing approaches inefficiently store large files with mostly irrelevant information.
Innovation Solution
A system and method that allows users to interact with data files through an application, tagging entities during playback, generating snippets of relevant data, and storing them with metadata, which can be analyzed and visualized on an enterprise data platform, including geospatial and transactional data integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users review and document events in data files using current approaches, then they can identify significant events, but the process becomes laborious and time-consuming requiring manual pausing and documentation
Solution Approach 1:
The system performs preliminary actions by automatically generating snippets and organizing them with metadata (timestamps, locations, entities) before the user needs to review events. This pre-processing eliminates the need for manual pausing and documentation during review, significantly reducing time loss while maintaining event identification accuracy.
Solution Approach 2:
The system creates simplified copies of relevant portions of data files in the form of snippets with associated metadata. These copies capture essential event information without requiring users to examine the entire original data file, thereby reducing review time while preserving the ability to accurately identify significant events.
2Loss of information
If users store complete data files for investigation, then all information is preserved, but storage requirements increase and retrieval efficiency decreases
Solution Approach 1:
The system extracts only the relevant portions of data files that contain significant events and stores them as snippets with metadata. This extraction process eliminates unnecessary data from storage while preserving all critical information about events, thereby reducing storage space requirements without causing loss of important information.
Solution Approach 2:
The system segments complete data files into smaller, manageable snippets based on detected events and their temporal-spatial context. This segmentation allows the system to store only necessary portions of data rather than complete files, reducing overall storage requirements while maintaining information completeness for investigation purposes.
3Loss of information
If users manually track timelines of events in data files, then event sequences are documented, but the process becomes cumbersome and ineffective
Solution Approach 1:
The system performs timeline tracking automatically without requiring manual user intervention. By analyzing snippets and their metadata, the system self-generates chronological sequences of events, eliminating the cumbersome manual tracking process while ensuring complete and accurate timeline information is preserved.
Solution Approach 2:
The system creates structured copies of event information from raw data files, organizing them into timeline formats with automatic sequencing based on timestamps and spatial information. This copying and reorganization process makes timeline information easily accessible and analyzable without requiring manual tracking operations.
4Measurement precision
If users review entire data files to find significant events, then comprehensive analysis is possible, but the process is ineffective and time-consuming
Solution Approach 1:
The system extracts and isolates only the portions of data files that contain significant events, presenting them as discrete snippets to users for review. This extraction eliminates the need to review entire data files while maintaining comprehensive event detection accuracy, as all significant events are captured in the extracted snippets with their contextual metadata.
Solution Approach 2:
The system segments data files into event-based snippets that are individually reviewable and analyzable. This segmentation transforms the ineffective process of reviewing entire large files into an efficient process of examining smaller, event-focused units, thereby maintaining detection accuracy while dramatically improving review productivity.
Data Source
AI summary
Systems and methods are provided for analyzing data snippets. One or more snippets can be associated with a data object of an enterprise data platform. The one or more snippets can be organized based on metadata information associated with the one or more snippets. The organized snippets can be analyzed to determine an activity relating to an entity depicted in the one or more snippets.