Causal Search Replacement for Unprivileged Data Asset Access
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Solution Overview
Problem
Existing data lake systems face challenges in efficiently leveraging data assets across organizational silos due to inadequate collaboration and manual efforts required to identify relevant data, leading to inefficient data utilization and reduced business efficiency.
Innovation Solution
A search and recommendation engine that analyzes user interactions and behavior to provide personalized data asset suggestions, using collaborative filtering and causal replacement to enhance data collaboration and access for both privileged and unprivileged users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a keyword-based search engine is used to search for data assets, then the search process is simple and fast, but the search results are not relevant enough and users must manually evaluate each returned data asset
Solution Approach 1:
The patent introduces an intermediary recommendation system that sits between the keyword search and the data assets. This intermediary uses collaborative filtering algorithms to analyze user interactions and behavior patterns, generating personalized recommendations that bridge the gap between simple keyword matching and highly relevant but time-consuming manual evaluation. The recommendation engine acts as a mediator that automatically filters and ranks data assets based on user profiles and historical behavior.
Solution Approach 2:
The system enables self-service by automatically generating personalized search results based on user behavior patterns and historical interactions. Instead of requiring users to manually evaluate each returned data asset, the system autonomously learns user preferences and automatically ranks relevant data assets, freeing users from manual evaluation while maintaining high relevance in search results.
2Productivity
If users are provided with access to all data assets in the data lake, then data utilization is maximized, but security and access control requirements are compromised
Solution Approach 1:
The patent applies local quality by providing personalized data asset recommendations tailored to each user's specific role, preferences, and access rights. Instead of uniform access control, the system customizes the data presentation and recommendations for each user locally, allowing maximum data utilization for authorized users while maintaining security through individualized access management based on user profiles and organizational roles.
3Reliability
If manual efforts are used to identify relevant data assets, then access control can be maintained, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary action by pre-processing and analyzing user interactions, behavior patterns, and data access histories before actual search queries are executed. The collaborative filtering algorithm continuously learns from past user behavior and pre-ranks data assets based on predicted relevance, so that when users actually need data, the system can quickly present pre-processed recommendations without requiring time-consuming manual identification or real-time analysis during the search process.
4Reliability
If organizational silos are maintained with separate data assets, then security and governance can be enforced, but data collaboration and sharing between departments is limited
Solution Approach 1:
The patent implements universality by creating a unified recommendation system that serves multiple organizational functions simultaneously. The same collaborative filtering infrastructure serves different departments and user roles, enabling cross-departmental data collaboration while maintaining governance through centralized user profile management and access control. The system universally applies collaborative filtering across all data assets while respecting organizational governance structures, breaking down silos without compromising security.
Data Source
AI summary
A search engine responding to a user query to find relevant data assets in a federation business data lake (FBDL) system. The search engine receives a search query from an unprivileged user or a user not having sufficient privileges to access the FBDL. It returns initial results to the unprivileged user including a first data asset recommendation responsive to the search query. It then determines a causal reason that the first data asset was recommended, and uses a similarity engine conditioned on the causal reason to return a replacement data asset in response to the search query.