Towards the Next Generation of Data Warehouse Personalization System: A Survey and a Comparative Study

TL;DR

This paper reviews existing OLAP personalization approaches to improve data warehouse decision-making, providing a comparative analysis and identifying trends and gaps for future research.

Contribution

It offers a comprehensive survey of OLAP personalization methods and introduces a benchmarking study to evaluate and compare these approaches.

Findings

Identification of current trends in OLAP personalization

Highlighting gaps and potential areas for future research

Benchmarking results showing performance differences among methods

Abstract

Multidimensional databases are a great asset for decision making. Their users express complex OLAP (On-Line Analytical Processing) queries, often returning huge volumes of facts, sometimes providing little or no information. Furthermore, due to the huge volume of historical data stored in DWs, the OLAP applications may return a big amount of irrelevant information that could make the data exploration process not efficient and tardy. OLAP personalization systems play a major role in reducing the effort of decision-makers to find the most interesting information. Several works dealing with OLAP personalization were presented in the last few years. This paper aims to provide a comprehensive review of literature on OLAP personalization approaches. A benchmarking study of OLAP personalization methods is proposed. Several evaluation criteria are used to identify the existence of trends as well as potential needs for further investigations.