Abstract: De nos jours, les entrepôts de données et les outils d’analyse OLAP sont très utilisés dans les entreprises qui ont besoin de systèmes décisionnels qui s’adaptent à toutes les situations particulières du monde réel, pour éviter les erreurs d’analyse (plus connus dans la littérature sous le nom de problèmes d’additivité ou summarizability issues en Anglais). Dans cet article, nous présentons un état de l’art des travaux sur les problèmes d’additivité dus à la présence des hiérarchies complexes dans un modèle multidimensionnel et discutons des travaux restant à mener dans ce domaine.

Ref HAL: hal-00783638_v1
Abstract: Dans cet article, nous portons notre regard sur l'aide à la décision du point de vue des systèmes décisionnels au sens des entrepôts de données et de l'analyse en ligne. Après avoir défini les concepts qui sous-tendent ces systèmes, nous nous proposons d'aborder les problématiques de recherche qui leur sont liées selon quatre points de vue : les données, les environnements de stockage, les utilisateurs et la sécurité. Nous abordons finalement les problèmes qui restent ouverts dans le domaine des entrepôts de données.

Abstract: The business intelligence and decision-support systems used in many application domains casually rely on data warehouses, which are decision-oriented data repositories modeled as multidimensional (MD) structures. MD structures help navigate data through hierarchical levels of detail. In many real-world situations, hierarchies in MD models are complex, which causes data aggregation issues, collectively known as the summarizability problem. This problem leads to incorrect analyses and critically aects decision making. To enforce summarizability, existing approaches alter either MD models or data, and must be applied a priori, on a case-by-case basis, by an expert. To alter neither models nor data, a few query-time approaches have been proposed recently, but they only detect summarizability issues without solving them. Thus, we propose in this paper a novel approach that automatically detects and processes summarizability issues at query time, without requiring any particular expertise from the user. Moreover, while most existing approaches are based on the relational model, our approach focus on an XML MD model, since XML data is customarily used to represent business data and its format better copes with complex hierarchies than the relational model. Finally, our experiments show that our method is likely to scale better than a reference approach for addressing the summarizability problem in the MD context.

Ref HAL: hal-00727331_v1
DOI: 10.1145/2347673.2347683
Abstract: Business intelligence (BI) is a broad field related to integrating, storing and analyzing data to help decision-makers in many domains (from actual business fields to administration, health and environment) make better decisions. Front-end analytics methods include reporting, on-line analytical processing (OLAP), and data mining. With the increasing success of cloud computing, cloud business intelligence “as a service” offerings have sparkled widely, both from cloud start-ups and major BI industry vendors. Beyond porting BI features into the cloud, which already implies numerous issues (e.g., BigData/NoSQL database modeling and storage, data localization, security and privacy, performance, cost and usage models…), this trend also poses new, broader challenges for making data analytics available to small and middle-size enterprises (SMEs), non-governmental organizations, web communities (e.g., supported by social networks), and even the average citizen; this vision presumably requiring a mixture of both private and open data. Thus, Cloud Intelligence is not only a current technological and research challenge, but also an important societal stake, since people increasingly demand open data (e.g., the Spanish indignados), which they need to access easily from the Web, to possibly mix them with private data, to analyze them with intelligible on-line tools with advanced collaborative features that enable users to share and re-use business intelligence concepts and analyses in large scale fashion, and to share the results world-wide. The aim of this workshop is to become an interdisciplinary, regular exchange forum for researchers, industry and practitioners, as well as all potential users of Cloud Intelligence. The submission of research, industrial, position, visionary and student papers are encouraged to fuel up the discussion.

Comments: ISBN: 978-1-4503-1596-8 http://dl.acm.org/citation.cfm?id=2347683

Abstract: Business Intelligence plays an important role in decision making. Based on data warehouses and Online Analytical Processing, a business intelligence tool can be used to analyze complex data. Still, summarizability issues in data warehouses cause ineffective analyses that may become critical problems to businesses. To settle this issue, many researchers have studied and proposed various solutions, both in relational and XML data warehouses. However, they find difficulty in evaluating the performance of their proposals since the available benchmarks lack complex hierarchies. In order to contribute to summarizability analysis, this paper proposes an extension to the XML warehouse benchmark (XWeB) with complex hierarchies. The benchmark enables us to generate XML data warehouses with scalable complex hierarchies as well as summarizability processing. We experimentally demonstrated that complex hierarchies can definitely be included into a benchmark dataset, and that our benchmark is able to compare two alternative approaches dealing with summarizability issues.

Ref HAL: hal-00718865_v1
DOI: 10.4018/jdwm.2013040104
Abstract: Self-service business intelligence is about enabling non-expert users to make well-informed decisions by enriching the decision process with situational data, i.e., data that have a narrow focus on a specific business problem and, typically, a short lifespan for a small group of users. Often, these data are not owned and controlled by the decision maker; their search, extraction, integration, and storage for reuse or sharing should be accomplished by decision makers without any intervention by designers or programmers. The goal of this paper is to present the framework we envision to support self-service business intelligence and the related research challenges; the underlying core idea is the notion of fusion cubes, i.e., multidimensional cubes that can be dynamically extended both in their schema and their instances, and in which situational data and metadata are associated with quality and provenance annotations.

Ref HAL: hal-00712547_v1
DOI: 10.1007/s10796-012-9405-6
Abstract: Today, the Web is the largest source of information in the world. There is currently an increasing demand that decision-making applications such as Data Warehousing (DW) and Business Intelligence (BI) move onto the Web, especially in the cloud. Integrating data into the DW/BI applications is still the most critical and time-consuming task. To make better decisions in DW/BI applications, next generation data integration poses new requirements to data integration systems, over those posed by traditional data integration. From these requirements is to integrate data in real-time and autonomously with minimal user intervention. Therefore, we propose in this paper a metadata-based, event-driven, and service-oriented framework for integrating real-time data autonomously. The framework utilizes Web standards to integrate data, from heterogeneous and distributed sources. It exploits the XML language to address data heterogeneity, Web services to tackle data distribution, and Active XML (AXML) to store integrated data. The framework is also subscribed to Web services for real-time change data capture. Moreover, beside logging different framework events into a specified repository for on-line analysis and reporting, we propose a novel Frequency XML-based Tree (FXT) structure for mining association rules from logged event streams using XQuery. The framework is also incorporates active rules to automate and activate different integration services. Finally, we have a web application prototype as a proof of concept.

Comments: Special issue on Business Intelligence and the Web