A New Similairty Measure For Spatial Personalization

TL;DR

This paper introduces a novel similarity measure for spatial MDX queries, enhancing spatial personalization by analyzing user behavior patterns in large spatial data warehouses.

Contribution

The paper proposes a new similarity measure for spatial MDX queries that considers topology, direction, and distance, supporting personalized spatial data retrieval.

Findings

The similarity measure effectively captures user behavior patterns.

It improves the relevance of query recommendations.

Supports development of personalized spatial data systems.

Abstract

Extracting the relevant information by exploiting the spatial data warehouse becomes increasingly hard. In fact, because of the enormous amount of data stored in the spatial data warehouse, the user, usually, don't know what part of the cube contain the relevant information and what the forthcoming query should be. As a solution, we propose to study the similarity between the behaviors of the users, in term of the spatial MDX queries launched on the system, as a basis to recommend the next relevant MDX query to the current user. This paper introduces a new similarity measure for comparing spatial MDX queries. The proposed similarity measure could directly support the development of spatial personalization approaches. The proposed similarity measure takes into account the basic components of the similarity assessment models: the topology, the direction and the distance.