Query Log Data Classification for Important Data Catalogues
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Solution Overview
Problem
Managers face challenges in accurately, efficiently, and dynamically classifying data based on usage and access patterns in electronic networks, leading to difficulties in ensuring important data is readily accessible, catalogued correctly, and stored correctly.
Innovation Solution
A data classification system that determines data classifications based on usage and access patterns by generating data identifier totals, comparing them, and creating data catalogues and databases for important and unimportant classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data classification methods are used, then data can be catalogued and stored, but the accuracy and efficiency of classification deteriorates due to difficulty in tracking usage and access patterns
Solution Approach 1:
The system enables data to classify itself automatically by analyzing its own usage and access patterns without human intervention. The data classification system continuously monitors how data is accessed and used, then automatically assigns appropriate classification levels based on this self-generated information about its own importance and sensitivity.
Solution Approach 2:
The system implements continuous feedback loops where data access patterns and usage information are constantly monitored and fed back into the classification system. This feedback mechanism allows the system to dynamically adjust classifications based on real-time observations of how data is actually being used, improving both accuracy and efficiency.
2Productivity
If automated data classification systems are implemented, then classification speed improves, but computing resource consumption increases
Solution Approach 1:
The system applies partial automation by focusing computational resources only on the most critical classification tasks and high-value data sets. Rather than attempting to classify every single data element with equal computational intensity, the system selectively applies automated classification where it provides the most benefit, reducing overall resource consumption while maintaining high classification speeds for important data.
3Ease of operation
If dynamic data classification based on usage patterns is implemented, then data accessibility improves, but system complexity increases
Solution Approach 1:
The system segments data into distinct classification categories based on usage patterns and access requirements. By dividing the data landscape into manageable classification segments (such as high-value, frequently accessed, sensitive, etc.), the system makes complex data environments more accessible and easier to navigate while maintaining the benefits of dynamic classification.
Data Source
AI summary
Systems, computer program products, and methods are described herein for automatically classifying data based on data usage and accessing patterns in an electronic network. The present invention is configured to receive at least one query log comprising a plurality of data identifiers; generate a data identifier total based on each data identifier of the plurality of data identifiers; determine a data classification for each data identifier based on the data identifier total, wherein the data classification comprises at least one of an important classification or an unimportant classification; and generate a data catalogue comprising at least one data identifier associated with the important classification.