Detecting User Focus in OLAP Analyses

Abstract

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.

A Framework for Learning Cell Interestingness from Cube Explorations

“…In our experiments, we reuse the dataset described in [5], consisting of navigation traces collected in the context of a French project on energy vulnerability. Traces consist of logged OLAP sessions 4 of volunteer students of a Master degree in Business Intelligence, answering some high-level information needs defined by their lecturer, using Saiku 5 to ask the queries and see the results.…”

“…Queries were assigned a binary label regarding their focus on the phenomenon analyzed by the student during the session. The term focus is used as in [5]: "When focused, an analyst would expect more precise queries, related to what she is currently analyzing. On the contrary, when exploring the data, the analyst would prefer more diverse queries, for a better data space coverage.…”

“…Djedaini et al use supervised classification techniques for learning two interest measures for OLAP queries: focus, that indicates to what extent a query is well detailed and related to other queries in an exploration, indicating that the user investigates in details precise facts and learns from this investigation [5], and contribution, that highlights to what extent a query is important for an exploration, contributing to its interest and quality [4].…”

Detecting coherent explorations in SQL workloads

“…In one of our previous works [18], we proposed an approach for detecting OLAP analyses phrased in SQL, by converting SQL queries into OLAP queries and then checking if two consecutive queries are sufficiently close in terms of OLAP operations. In our more recent work, we used supervised learning to identify a set of query features allowing to characterize focus zones in OLAP explorations [5], or to identify queries that better contribute to an exploration [4]. The present work can be seen as a continuation of those previous works, since we have the same objective as [18] and use the same technique as [4].…”

“…For each query, we extract a set of simple features computed from the query text and its relationship with other queries in a session. The set of features is inspired from our previous work [5,4], which models OLAP queries as a set of features capturing typical OLAP navigation.…”