Data Usage Analysis Engine for Database Access Monitoring
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Solution Overview
Problem
Conventional database tools lack the capability to efficiently and accurately analyze data usage patterns and trends, making it difficult for database owners to monitor access and usage effectively for change and incident management.
Innovation Solution
A method and system that uses a processor to access and analyze data sets, identify users, determine access frequencies, and output usage patterns, predicting anomalies and providing notifications for deviations in data access, implemented through a Data Usage Analysis Engine (DUAE) in a network environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional database tools are used to monitor database systems, then database performance monitoring is achieved, but data usage pattern analysis capability is insufficient
Solution Approach 1:
The system segments data usage analysis into multiple independent modules: data collection module, data storage module, data processing module, and visualization module. Each module handles specific aspects of data usage analysis independently, enabling the system to handle large volumes of data efficiently while providing comprehensive analysis capabilities that conventional database tools lack.
Solution Approach 2:
The patent introduces a data usage analysis engine as an intermediary layer between database systems and users. This engine collects data from multiple sources, processes it through various analysis algorithms, and presents results in meaningful visualizations, thereby bridging the gap between raw database data and actionable insights without requiring users to directly query complex database structures.
2Measurement precision
If comprehensive data usage monitoring is implemented, then access tracking accuracy is improved, but system complexity increases
Solution Approach 1:
The data usage analysis engine is designed as a universal system that can monitor multiple types of data access patterns across different databases and data sources using a unified architecture. The same core processing algorithms and visualization techniques apply to various data types and access patterns, reducing overall system complexity while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
The system implements self-service functionality where the data usage analysis engine automatically collects data from databases, processes it through built-in algorithms, generates visualizations, and updates dashboards without requiring manual intervention. This automation reduces the operational complexity of maintaining comprehensive monitoring systems while ensuring continuous and accurate data usage tracking.
Data Source
AI summary
A method and a computing apparatus for analyzing data usage are provided. The method includes: accessing a data set; identifying at least one user that has accessed the data set within a predetermined time interval; determining a number of times that the identified user accessed the data set during the predetermined time interval; and outputting an identification of the user in conjunction with information identifying the data set and information indicating the determined number of times of accessing the data set. The method may further include determining a data set-specific data usage pattern that indicates usage frequency information that relates to the data set, and outputting information that relates to the determined data set-specific data usage pattern.