Spatial and Spatio-Temporal Multidimensional Data Modelling: A Survey

TL;DR

This survey reviews the development and integration of spatial and spatio-temporal multidimensional models in data warehouses, highlighting trends, challenges, and future research directions in spatial data management for decision support.

Contribution

It provides a comprehensive review of existing models and benchmarks their effectiveness, identifying key trends and gaps in spatial and spatio-temporal data warehouse modeling.

Findings

Spatial data integration enhances decision support systems.

Benchmarking reveals current strengths and limitations of models.

Identifies need for further research in spatio-temporal data modeling.

Abstract

Data warehouse store and provide access to large volume of historical data supporting the strategic decisions of organisations. Data warehouse is based on a multidimensional model which allow to express user's needs for supporting the decision making process. Since it is estimated that 80% of data used for decision making has a spatial or location component [1, 2], spatial data have been widely integrated in Data Warehouses and in OLAP systems. Extending a multidimensional data model by the inclusion of spatial data provides a concise and organised spatial datawarehouse representation. This paper aims to provide a comprehensive review of litterature on developed and suggested spatial and spatio-temporel multidimensional models. A benchmarking study of the proposed models is presented. Several evaluation criterias are used to identify the existence of trends as well as potential needs for further investigations.