  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.
