Federation Data Lake Recommendation Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current search engine technologies in federation business data lake environments fail to effectively facilitate data reuse and exploration across organizational silos, leading to inefficient collaboration and manual efforts in identifying relevant data assets, as they rely solely on keyword-based searches that do not leverage user relationships and interactions.
Innovation Solution
Implementing a search and recommendation engine that utilizes user interaction data to provide personalized suggestions for data assets by calculating similarity metrics based on past behavior and contextual information, enabling users to access relevant data assets through collaborative filtering and natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword-based search engine is used to find data assets, then simple search functionality is provided, but relevant data assets cannot be efficiently identified and users must manually evaluate each returned asset
Solution Approach 1:
The system automatically generates personalized recommendations for data assets based on user profiles and interaction history, eliminating the need for users to manually evaluate multiple returned assets. The search engine serves itself by intelligently filtering and ranking assets before presentation to the user.
Solution Approach 2:
The system continuously monitors user interactions with data assets and uses this feedback to refine user profiles and improve recommendation accuracy over time, creating a self-improving search system that increasingly delivers more relevant results.
2Adaptability or versatility
If users rely on familiar data and old data warehousing practices, then users feel comfortable with existing systems, but collaboration across organizational silos is limited and data reuse is inefficient
Solution Approach 1:
The system serves multiple functions: it provides personalized recommendations, discovers data assets across organizational boundaries, identifies relevant data based on user context, and facilitates collaboration between different departments, replacing multiple manual processes with a single intelligent system.
Solution Approach 2:
The system adds the dimension of personalization and contextual awareness to traditional search, moving from simple keyword matching to multi-dimensional filtering based on user profiles, interaction history, and organizational structure, thereby enabling cross-silo collaboration.
3Quantity of substance
If simple metadata search is provided for data assets, then basic data location is enabled, but users cannot effectively leverage relationships between users and data assets to improve search results
Solution Approach 1:
The system performs preliminary analysis of user profiles, interaction histories, and data asset metadata before a search query is executed, pre-computing recommendations and rankings so that when a user searches, relevant results are already prepared and presented immediately.
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 by monitoring and recording all of the interactions of users interacting with data assets in the FBDL system, providing all of the user interactions to a recommendation engine, calculating relevance of information in the FBDL system to each user, and recommending one or more new data assets to a target user based on the relevance of the information. The relevance comprises the target user's past interactions with the data assets based and the cumulative interactions of other users with the data assets, such that if one or more of the other users has similar interaction behavior to the target user, then knowledge of the one or more other users can impact the relevance of the information with regard to the one or more new data assets suggested to the target user.