Verónika Peralta

Describing analytical sessions using a multidimensional algebra

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.

Assessment and analysis of information quality: a multidimensional model and case studies

Information quality is a complex and multidimensional notion. In the context of information system engineering, it is also a transversal notion and to be fully understood, it needs to be evaluated jointly considering the quality of data, the quality of the underlying conceptual data model and the quality of the software system that manages these data. This paper presents a multidimensional model for exploring information in a multidimensional way, which aids in the navigation, filtering, and interpretation of quality measures, and thus in the identification of the most appropriate actions to improve information quality. Two application scenarios are presented to illustrate and validate the multidimensional approach: the first one concerns the quality of customer information at Electricite de France, a French Electricity Company, and the second concerns the quality of patient records at Curie Institute, a well-known medical institute in France. The instantiation of our multidimensional model in these contexts shows first illustrations of its applicability.

Qbox-Services: Towards a Service-Oriented Quality Platform

The data quality market is characterized by a sparse offer of tools, providing individual functionalities which have their own interest with respect to quality assessment. But interoperating among these tools remains a technical challenge because of the heterogeneity of their models and access patterns. On the other side, quality analysts require more and more integration facilities that allow them to consolidate and aggregate multiple quality measures acquired from different observations. The QBox platform, developed within the ANR Quadris project, aims at filling this gap by supplying a service-based integration infrastructure that allows interoperability among several quality tools and provides an OLAP-based quality model to support multidimensional analysis. This paper focuses on the architectural principles of this infrastructure and illustrates its use through specific examples of quality services.

Analyzing and Evaluating Data Freshness in Data Integration Systems

Data freshness has been identified as one of the most important data quality attributes in information systems. This importance increases particularly in the context of systems composed of a large set of autonomous data sources where integrating data having different freshness may lead to semantic problems. This paper addresses the problem of evaluating data freshness in a data integration system and presents a taxonomy to classify different scenarios where data freshness can be evaluated. We propose a framework for modeling the data integration system and performing freshness evaluation and we illustrate the approach for a particular scenario.

Materializing Baseline Views for Deviation Detection Exploratory OLAP

Alert-raising and deviation detection in OLAP and explora-tory search concerns calling the user’s attention to variations and non-uniform data distributions, or directing the user to the most interesting exploration of the data. In this paper, we are interested in the ability of a data warehouse to monitor continuously new data, and to update accordingly a particular type of materialized views recording statistics, called baselines. It should be possible to detect deviations at various levels of aggregation, and baselines should be fully integrated into the database. We propose Multi-level Baseline Materialized Views (BMV), including the mechanisms to build, refresh and detect deviations. We also propose an incremental approach and formula for refreshing baselines efficiently. An experimental setup proves the concept and shows its efficiency.

User Interests Clustering in Business Intelligence Interactions

It is quite common these days for experts, casual analysts, executives or data enthusiasts, to analyze large datasets using user-friendly interfaces on top of Business Intelligence (BI) systems. However, current BI systems do not adequately detect and characterize user interests, which may lead to tedious and unproductive interactions. In this paper, we propose to identify such user interests by characterizing the intent of the interaction with the BI system. With an eye on user modeling for proactive search systems, we identify a set of features for an adequate description of intents, and a similarity measure for grouping intents into coherent interests. We validate experimentally our approach with a user study, where we analyze traces of BI navigation. We show that our similarity measure outperforms a state-of-the-art query similarity measure and yields a very good precision with respect to expressed user interests.

Detecting User Focus in OLAP Analyses

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.

Benchmarking Exploratory OLAP

Supporting interactive database exploration (IDE) is a problem that attracts lots of attention these days. Exploratory OLAP (On-Line 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 industry-strength benchmark are listed in the conclusion.

Reliability Models for Data Integration Systems

Data integration systems (DIS) are devoted to providing information by integrating and transforming data extracted from external sources. Examples of DIS are the mediators, data warehouses, federations of databases, and web portals. Data quality is an essential issue in DIS as it concerns the confidence of users in the supplied information. One of the main challenges in this field is to offer rigorous and practical means to evaluate the quality of DIS. In this sense, DIS reliability intends to represent its capability for providing data with a certain level of quality, taking into account not only current quality values but also the changes that may occur in data quality at the external sources. Simulation techniques constitute a non-traditional approach to data quality evaluation, and more specifically for DIS reliability. This chapter presents techniques for DIS reliability evaluation by applying simulation techniques in addition to exact computation models. Simulation enables some important drawbacks of exact techniques to be addressed: the scalability of the reliability computation when the set of data sources grows, and modeling data sources with inter-related (non independent) quality properties.

Service retrieval based on behavioral specifications and quality requirements

The capability to easily find useful services becomes increasingly critical in several fields. In this paper we argue that, in many situations, the service discovery process should be based on both behavior specification (that is the process model which describes each composite service) and quality features of services. The idea behind is to develop matching techniques that operate on process models and allow delivery of partial matches and evaluation of semantic distance between these matches and the user requirements. To do so, we reduce the problem of service behavioral matching to a graph matching problem and we adapt existing algorithms for this purpose. The matching algorithm is extended by a flexible quality evaluation procedure which checks whether a given service is worth to be delivered or not.