In an attempt to streamline exploratory data analysis of multidimensional cubes, the Intentional Analytics Model ha been proposed as a way to unite OLAP and analytics by allowing users to indicate their analysis intentions and returning cubes enhanced with models. Five intention operators were envisioned to this end; in this work we focus on the predict operator, whose goal is to estimate the missing values of a cube measure starting from known values of the same measure or other measures using different regression models. Although prediction tasks such as forecasting and imputation are routinary for analysts, the added value of our approach is (i) to encapsulate them in a declarative, concise, natural language-like syntax; (ii) to automate the selection of the best measures to be used and the computation of the models, and (iii) to automate the evaluation of the interest of the models computed. First we propose a syntax and a semantics for predict and discuss how enhanced cubes are built by (i) predicting the missing values for a measure based on the available information via one or more models and (ii) highlighting the most interesting prediction. Then we test the operator implementation, proving that its performance is in line with the interactivity requirement of OLAP session and that accurate predictions can be returned.

The Intentional Analytics Model (IAM) has been devised to couple OLAP and analytics by (i) letting users express their analysis intentions on multidimensional data cubes and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of models (e.g., correlations). Five intention operators were proposed to this end; of these, describe and assess have been investigated in previous papers. In this work we enrich the IAM picture by focusing on the explain operator, whose goal is to provide an answer to the user asking “why does measure m show these values?”; specifically, we consider models that explain m in terms of one or more other measures. We propose a syntax for the operator and discuss how enhanced cubes are built by (i) finding the relationship between m and the other cube measures via regression analysis and cross-correlation, and (ii) highlighting the most interesting one. Finally, we test the operator implementation in terms of efficiency and effectiveness.

Exploratory Data Analysis (EDA) is the interactive process of gaining insights from a dataset. Comparisons are popular insights that can be specified with comparison queries, i.e., specifications of the comparison of subsets of data. In this work, we consider the problem of automatically computing sequences of comparison queries that are coherent, significant and whose overall cost is bounded. Such an automation is usually done by either generating all insights and solving a multi-criteria optimization problem, or using reinforcement learning. In the first case, a large search space has to be explored using exponential algorithms or dedicated heuristics. In the second case, a dataset-specific, time and energy-consuming training, is necessary. We contribute with a novel approach, consisting of decomposing the optimization problem in two: the original problem, that is solved over a smaller search space, and a new problem of generating comparison queries, aiming at generating only queries improving existing solutions of the first problem. This allows to explore only a portion of the search space, without resorting to reinforcement learning. We show that this approach is effective, in that it finds good solutions to the original multi-criteria optimization problem, and efficient, allowing to generate sequences of comparisons in reasonable time.

International audience

Abstract Multi-model DBMSs, which support different data models with a fully integrated backend, have been shown to be beneficial to data warehouses and OLAP systems. Indeed, they can store data according to the multidimensional model and, at the same time, let each of its elements be represented through the most appropriate model. An open challenge in this context is the lack of methods for logical design. Indeed, in a multi-model context, several alternatives emerge for the logical representation of dimensions and facts. The goal of this paper is to devise a set of guidelines for the logical design of multi-model data warehouses so that the designer can achieve the best trade-off between features such as querying, storage, and ETL. To this end, for each model considered (relational, document-based, and graph-based) and for each type of multidimensional element (e.g., non-strict hierarchy) we propose some solutions and carry out a set of intra-model and inter-model comparisons. The resulting guidelines are then tested on a case study that shows all types of multidimensional elements.

Assessment is the process of comparing the actual to the expected behavior of a business phenomenon and judging the outcome of the comparison. The assess querying operator has been recently proposed to support assessment based on the results of a query on a data cube. This operator requires (i) the specification of an OLAP query to determine a target cube; (ii) the specification of a reference cube of comparison (benchmark), which represents the expected performance; (iii) the specification of how to perform the comparison, and (iv) a labeling function that classifies the result of this comparison. Despite the adoption of a SQL-like syntax that hides the complexity of the assessment process, writing a complete assess statement is not easy. In this paper we focus on making the user experience more comfortable by letting the system suggest suitable completions for partially-specified statements. To this end we propose two interaction modes: progressive refinement and auto-completion, both starting from an assess statement partially declared by the user. These two modes are evaluated both in terms of scalability and user experience, with the support of two experiments made with real users.

International audience

Assessment is the process of comparing the actual to the expected behavior of a business phenomenon and judging the outcome of the comparison. In this paper we propose assess, a novel querying operator that supports assessment based on the results of a query on a data cube. This operator requires (1) the specification of an OLAP query over a measure of a data cube, to define the target cube to be assessed; (2) the specification of a reference cube of comparison (benchmark), which represents the expected performance of the measure; (3) the specification of how to perform the comparison between the target cube and the benchmark, and (4) a labeling function that classifies the result of this comparison using a set of labels. After introducing an SQL-like syntax for our operator, we formally define its semantics in terms of a set of logical operators. To support the computation of assess we propose a basic plan as well as some optimization strategies, then we experimentally evaluate their performance using a prototype.

OLAP is the main paradigm for flexible and effective exploration of multidimensional cubes in data warehouses. During an OLAP session the user analyzes the results of a query and determines a new query that will give her a better understanding of information. Given the huge size of the data space, this exploration process is often tedious and may leave the user disoriented and frustrated. This paper presents an OLAP tool named Falseto (Former AnalyticaL Sessions for lEss Tedious Olap), that is meant to assist query and session composition, by letting the user summarize, browse, query, and reuse former analytical sessions. Falseto's implementation on top of a formal framework is detailed. We also report the experiments we run to obtain and analyze real OLAP sessions and assess Falseto with them. Finally, we discuss how Falseto can be seen as a starting point for bridging OLAP with exploratory search, a search paradigm centered on the user and the evolution of her knowledge.

One of the problems in analyzing large multidimensional databases through OLAP sessions is that decision makers can be overwhelmed by the size of query answers, while they need a concise summary of data. Intensional query answering can help by providing a concise description of extensional answers (i.e., the sets of retrieved facts), generally relying on knowledge like integrity constraints, taxonomies, or patterns discovered from data. This paper proposes a framework for computing an intensional answer to an OLAP query by leveraging on the previous queries in the current session. Such intensional answer is concise and semantically rich, and allows the size of the extensional answers returned to be reduced, so as to achieve an effective trade-off between conciseness and informational content. After describing the general framework, we propose a specific instantiation that relies on previous contributions in cube modeling and intensional query answering.

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 discovery driven analysis. The main idea is to recommend to the user the discoveries detected in those former sessions that investigated the same unexpected data as the current session.

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.

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.

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 an approach to enhance this analysis by preparing the data set so that the analyst can explore it in a more systematic and effective manner. More precisely we define a measurement of the quality of the representation of multidimensional data and we present a framework for investigating the computation of appropriate representations. We identify the problems of computing such representations and study them w.r.t. an OLAP restructuring operator.

We study KDD (Knowledge Discovery in Databases) processes on multidimensional data from a query point of view. Focusing on association rule mining, we consider typical queries to cope with the pre-processing of multidimensional data and the post-processing of the discovered patterns as well. We use a model and a rule-based language stemming from the OLAP multidimensional representation, and demonstrate that such a language fits well for writing KDD queries on multidimensional data. Using an homogeneous data model and our language for expressing queries at every phase of the process appears as a valuable step towards a better understanding of interactivity during the whole process.