Audit Log-Based Document Recommendation Engine for Enterprise BI
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Solution Overview
Problem
Business intelligence enterprise systems face challenges in efficiently recommending relevant documents to users due to the vast amount of data and varying access rights, making it difficult for users to determine which data to access and utilize effectively.
Innovation Solution
A computer-implemented method that analyzes audit log entries associated with a user's group to generate recommendations based on accessed files, including search information, most viewed documents, and scheduled events, ensuring secure and relevant document suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all users are granted access to all business artifacts through user management, then users can access data needed to perform tasks, but the quantity of data becomes burdensome for users to determine what to access
Solution Approach 1:
The system automatically generates document recommendations by analyzing audit logs and user characteristics without requiring manual curation. The recommendation engine serves itself by continuously learning from user behavior patterns, automatically filtering and prioritizing documents based on relevance to each user's role and recent activities.
Solution Approach 2:
The system implements feedback loops by analyzing user interactions with recommended documents and adjusting future recommendations accordingly. Audit logs capture user behavior which feeds back into the recommendation algorithm, improving the accuracy of document suggestions over time and helping users navigate the large volume of accessible data more effectively.
2Reliability
If user management limits the data a user can access, then security is improved, but users still have access to a large amount of data that is burdensome to navigate
Solution Approach 1:
The system extracts only the most relevant documents from the large set of accessible data by analyzing audit logs and user characteristics. Instead of presenting all accessible documents, the recommendation engine extracts and prioritizes a subset of documents that are most relevant to each user's current needs and historical behavior patterns.
Solution Approach 2:
The recommendation system applies local quality by providing customized document suggestions tailored to each user's specific role, department, and recent activities. Rather than a uniform approach for all users, the system adapts the recommendation quality and relevance to local user contexts and requirements.
3Loss of information
If the system provides comprehensive document access to all users, then data availability is improved, but it becomes difficult for users to determine which data to access and utilize effectively
Solution Approach 1:
The recommendation engine acts as an intermediary between the vast repository of accessible documents and the user. It mediates the information overload by filtering, ranking, and presenting documents in a manageable format, translating the comprehensive data availability into actionable, user-friendly recommendations.
Solution Approach 2:
The system performs preliminary analysis of audit logs and user characteristics before presenting documents to users. By pre-processing and evaluating documents based on relevance criteria, the system prepares and organizes information in advance, making it easier for users to identify and access relevant data without having to manually search through all available documents.
Data Source
AI summary
In one embodiment a computer-implemented method for recommending documents to a user, the method comprises determining, by a computer, audit logs entries that are associated with members of a group that includes the user as a member, each audit log entry is associated with a file that a member of the group has accessed; analyzing, by the computer, the associated audit log entries; and generating, by the computer, a recommendation of at least one file for the user based on the analyzing of the associated audit log entries.