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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.

The understanding of daily human activity is an active research topic. Thanks to GPS and smartphones, human movements can be monitored and analyzed. In addition, by exploiting Linked Open Data and user personal data, semantic labels and annotations can be added to movements. Thus, semantic trajectories can be considered as sequences of timestamped activities where each activity is described by a semantic label. In this context, a major challenge is the comparison of such semantic trajectories, looking to extract and learning similar human mobility behaviors. We propose CED (Contextual Edit Distance), a generic similarity measure for semantic sequences comparison which improve the Edit Distance to take into account the context similarity between elements in the sequence. CED is configurable to any sequence data and business needs. CCS CONCEPTS• Information systems → Similarity measures; Geographic information systems.

Abstract. Recent efforts to support analytical tasks over relational sources have pointed out the necessity to come up with flexible, powerful means for analyzing the issued queries and exploit them in decisionoriented processes (such as query recommendation or physical tuning). Issued queries should be decomposed, stored and manipulated in a dedicated subsystem. With this aim, we present a novel approach for representing SQL analytical queries in terms of a multidimensional algebra, which better characterizes the analytical efforts of the user. In this paper we discuss how an SQL query can be formulated as a multidimensional algebraic characterization. Then, we discuss how to normalize them in order to bridge (i.e., collapse) several SQL queries into a single characterization (representing the analytical session), according to their logical connections.