Query recommendation systems based on the exploration of OLAP and SOLAP data cubes

Abstract

Business Intelligence systems refer to technologies and tools responsible for collecting, storing and analyzing data to improve decision-making. In BI systems, users interact with data warehouse by formulating and launching sequences of queries aimed at exploring multidimensional data cubes. However, the volumes of data stored in a data warehouse can be very large and diversified. So, a big amount of irrelevant information returned as results to the user could make the data exploration process inefficient. That’s why, it’s necessary to help the user by guiding him in his exploration. In fact, query recommendation systems play a major role in reducing the effort of decision-makers to find the most interesting information. Several works dealing with query recommendation systems were presented in the last few years. This paper aims at providing a comprehensive review of literature on a query recommendation based on the exploration of data cubes. A benchmarking study of query recommendation methods is proposed. Several evaluation criteria are used to identify the existence of new investigations and future researches.