Patrick Marcel

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<i>OLAP</i> users heavily rely on visualization of query answers for their interactive analysis of massive amounts of data. Very often, these answers cannot be visualized entirely and the user has to navigate through them to find relevant facts.In this paper, we propose a framework for personalizing <i>OLAP</i> queries. In this framework, the user is asked(More)
Recommending database queries is an emerging and promising field of investigation. This is of particular interest in the domain of OLAP systems where the user is left with the tedious process of navigating large datacubes. In this paper we present a framework for a recommender system for OLAP users, that leverages former users' investigations to enhance(More)
OLAP queries are not normally formulated in isolation, but in the form of sequences called OLAP sessions. Recognizing that two OLAP sessions are similar would be useful for different applications, such as query recommendation and personalization; however, the problem of measuring OLAP session similarity has not been studied so far. In this paper, we aim at(More)
On-Line Analytical Processing (OLAP) provides an interactive query-driven analysis of multidimensional data based on a set of navigational operators like roll-up or slice and dice. In most cases, the analyst is expected to use these operations intuitively to find interesting patterns in a huge amount of data of high dimensionality.In this paper, we propose(More)
Answers of OLAP queries often cannot be visualized entirely and the user has to navigate through them to find relevant facts. One possible way to solve this problem is to personalize the queries. The basic idea behind the process of query personalization is that different users may find and see the facts they prefer without having to navigate. In this(More)
In 1993, Rakesh Agrawal, Tomasz Imielinski and Arun N. Swami published one of the founding papers of Pattern Mining: "Mining Association Rules between Sets of Items in Large Databases". Beyond the introduction to a new problem, it introduced a new methodology in terms of resolution and evaluation. For two decades, Pattern Mining has been one of the most(More)
While OLAP has a key role in supporting effective exploration of multidimensional cubes, the huge number of aggregations and selections that can be operated on data may make the user experience disorientating. To address this issue, in the paper we propose a recommendation approach stemming from collaborative filtering. We claim that the whole sequence of(More)