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Abstract. In this paper, we propose an approach to automatically detect focused portions of data cube explorations by using different features of OLAP queries. While such a concept of focused interaction is relevant to many domains besides OLAP explorations, like web search or interactive database exploration, there is currently no precise formal, commonly agreed definition. This concept of focus phase is drawn from Exploratory Search, which is a paradigm that theorized search as a complex interaction between a user and a system. The interaction consists of two different phases: an exploratory phase where the user is progressively defining her information need, and a focused phase where she investigates in details precise facts, and learn from this investigation. Following this model, our work is, to the best of our knowledge, the first to propose a formal feature-based description of a focused query in the context of interactive data exploration. Our experiments show that we manage to identify focused queries in real navigations, and that our model is sufficiently robust and general to be applied to different OLAP navigations datasets.

Abstract. Supporting interactive database exploration (IDE) is a problem that attracts lots of attention these days. Exploratory OLAP (OnLine Analytical Processing) is an important use case where tools support navigation and analysis of the most interesting data, using the best possible perspectives. While many approaches were proposed (like query recommendation, reuse, steering, personalization or unexpected data recommendation), a recurrent problem is how to assess the effectiveness of an exploratory OLAP approach. In this paper we propose a benchmark framework to do so, that relies on an extensible set of user-centric metrics that relate to the main dimensions of exploratory analysis. Namely, we describe how to model and simulate user activity, how to formalize our metrics and how to build exploratory tasks to properly evaluate an IDE system under test (SUT). To the best of our knowledge, this is the first proposal of such a benchmark. Experiments are two-fold: first we evaluate the benchmark protocol and metrics based on synthetic SUTs whose behavior is well known. Second, we concentrate on two different recent SUTs from IDE literature that are evaluated and compared with our benchmark. Finally, potential extensions to produce an industrystrength benchmark are listed in the conclusion.

International audienc

The Bernstein polytopes-based solver is a new method developed to solve systems of nonlinear equations, which often occur in Geometric Constraint Solving Problems. The principle of this solver is to linearize nonlinear monomials and then to solve the resulting linear programming problems, through linear programming. However, without any strategy for the isolation of the many solutions of multiple-solution systems, this solver is slow in practice. To overcome this problem, we propose in this work, a study of several strategies for solution isolation, through the split of solution boxes into several subboxes, according to three main steps answering the questions: when, where, and how to perform a split? We provide a detailed benchmark evaluating both time and space complexities for the proposed splitting strategies, applied to several Geometric Constraint Solving Problems widely encountered in geometric modeling. We also compare several linear programming solvers within our Bernstein solver.