No abstract

OLAP 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 OLAP queries. In this framework, the user is asked to give his (her) preferences and a visualization constraint, that can be for instance the limitations imposed by the device used to display the answer to a query. Given this, for each query, our method computes the part of the answer that respects both the user preferences and the visualization constraint. In addition, a personalized structure for the visualization is proposed.

Recommending database queries is an emerging and promising field of research and 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, the authors present a framework for a recommender system for OLAP users that leverages former users’ investigations to enhance discovery-driven analysis. This framework recommends the discoveries detected in former sessions that investigated the same unexpected data as the current session. This task is accomplished by (1) analysing the query log to discover pairs of cells at various levels of detail for which the measure values differ significantly, and (2) analysing a current query to detect if a particular pair of cells for which the measure values differ significantly can be related to what is discovered in the log. This framework is implemented in a system that uses the open source Mondrian server and recommends MDX queries. Preliminary experiments were conducted to assess the quality of the recommendations in terms of precision and recall, as well as the efficiency of their on-line computation.

An OLAP analysis session can be defined as an interactive session during which a user launches queries to navigate within a cube. Very often choosing which part of the cube to navigate further, and thus designing the forthcoming query, is a difficult task. In this paper, we propose to use what the OLAP users did during their former exploration of the cube as a basis for recommending OLAP queries to the user. We present a generic framework that allows to recommend OLAP queries based on the OLAP server query log. This framework is generic in the sense that changing its parameters changes the way the recommendations are computed. We show how to use this framework for recommending simple MDX queries and we provide some experimental results to validate our approach.

Many applications rely on distributed databases. However, only few discovery methods exist to extract patterns without centralizing the data. In fact, this centralization is often less expensive than the communication of extracted patterns from the different nodes. To circumvent this difficulty, this paper revisits the problem of pattern mining in distributed databases by benefiting from pattern sampling. Specifically, we propose the algorithm DDSampling that randomly draws a pattern from a distributed database with a probability proportional to its interest. We demonstrate the soundness of DDSampling and analyze its time complexity. Finally, experiments on benchmark datasets highlight its low communication cost and its robustness. We also illustrate its interest on real-world data from the Semantic Web for detecting outlier entities in DBpedia and Wikidata.