Personalized Query Expansion Using Co-occurrence Statistics

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Solution Overview

Problem

The increasing complexity and volume of data in data warehouses hinder effective exploration and query creation for analytical purposes, as existing recommender systems lack selective approaches to suggest relevant queries.

Innovation Solution

A personalized query expansion system that utilizes semantics from multi-dimensional domain models, user profiles, and collaborative usage statistics to suggest measures and dimensions, leveraging co-occurrence values and social network information to iteratively build consistent queries.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If the volume and complexity of data in data warehouses increases, then more data is available for analysis, but exploration and query creation become hindered

Engineering Contradiction:

Improvedata volumeVSAvoidquery creation

Core Design Contradiction:

Quantity of substanceVSEase of operation

Solution Approach 1:

The system enables self-service by automatically generating query suggestions based on user behavior patterns and document co-occurrence statistics, allowing users to explore data warehouses without manually constructing complex queries

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback mechanisms by analyzing user interactions with query results and document repositories, continuously refining query suggestions to better match user needs and preferences

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If existing recommender systems are used to suggest data warehouse queries, then some query suggestions are provided, but the approach lacks selectivity and personalization

Engineering Contradiction:

Improvequery suggestion capabilityVSAvoidsuggestion relevance

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system applies local quality by personalizing query suggestions for each user based on their specific preferences, role, and interaction history, rather than providing generic recommendations to all users

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system changes parameters by dynamically adjusting query suggestion criteria based on user profiles, document co-occurrence statistics, and contextual factors to improve suggestion precision

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS9690848B2Multi-dimensional query expansion employing semantics and usage statistics

Publication Date: 2017.06.27 SAP SE

AI summary

Embodiments relate to systems and methods employing personalized query expansion to suggest measures and dimensions allowing iterative building of consistent queries over a data warehouse. Embodiments may leverage one or more of: semantics defined in multi-dimensional domain models, user profiles defining preferences, and collaborative usage statistics derived from existing repositories of Business Intelligence (BI) documents (e.g. dashboards, reports). Embodiments may utilize a collaborative co-occurrence value derived from profiles of users or social network information of a user.