Dynamic Data Characterization for Enterprise Search
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Solution Overview
Problem
Current data management systems struggle to efficiently identify and prioritize data of interest across multiple platforms in an enterprise, particularly in real-time, due to static access metrics and lack of dynamic characterization.
Innovation Solution
A system and method that characterizes data by content characteristics and dynamic access metrics, including data access permissions and actual access history, enabling near real-time data matching and indexing, with automatic field redefinition and prioritization based on changing access metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is characterized by multiple content characteristics and dynamic access metrics, then data identification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments data characteristics into content characteristics (structure, format, type) and access metrics (permissions, access history, ownership). This segmentation allows independent analysis and characterization of different data aspects, improving identification accuracy while managing complexity through modular processing of each characteristic type.
Solution Approach 2:
The system performs preliminary background characterization of data elements, pre-computing and storing content characteristics and access metrics before actual queries are needed. This preliminary action enables faster near real-time data matching by having data already organized and indexed according to multiple characteristics, reducing the complexity of on-demand analysis.
2Speed
If near real time data matching is performed using multiple access metrics, then response speed is improved, but processing resources are consumed
Solution Approach 1:
The system performs preliminary background characterization and indexing of data elements according to multiple access metrics and content characteristics. This pre-processing organizes data in advance, enabling near real-time queries to retrieve pre-sorted and pre-filtered data without requiring intensive processing resources during the actual query execution.
Solution Approach 2:
The system continuously monitors and updates access metrics (such as access history, permissions changes) and uses this feedback to dynamically adjust data prioritization and matching. This feedback mechanism allows the system to optimize search results based on actual usage patterns, improving response relevance while managing processing resources through adaptive querying.
3Measurement precision
If dynamic access metrics are used to redefine search fields, then data relevance is improved, but system stability decreases
Solution Approach 1:
The system implements dynamic access metrics that can change over time (such as evolving permissions, access patterns, and data ownership). The field of search is automatically redefined based on these dynamic metrics, allowing the system to adapt to changing enterprise requirements while maintaining stability through controlled, incremental updates rather than abrupt reconfigurations.
Solution Approach 2:
The system performs preliminary analysis of access metric changes and pre-computes the impact on search fields before implementing redefinitions. This preliminary action allows the system to plan and execute changes in a controlled manner, maintaining operational stability while still achieving improved data relevance through dynamic adaptation.
Data Source
AI summary
A system for identifying data of interest from among a multiplicity of data elements residing on multiple platforms in an enterprise, the system including background data characterization functionality characterizing the data of interest at least by at least one content characteristic thereof and at least one access metric thereof, the at least one access metric being selected from data access permissions and actual data access history and near real time data matching functionality selecting the data of interest by considering only data elements which have the at least one content characteristic thereof and the at least one access metric thereof from among the multiplicity of data elements.