Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems

Les entrepôts de données reposent sur la modélisation multidimensionnelle. A l'aide d'outils OLAP, les décideurs analysent les données à différents niveaux d'agrégation. Il est donc nécessaire de représenter les connaissances d'agrégation dans les modèles conceptuels multidimensionnels, puis de les traduire dans les modèles logiques et physiques. Cependant, les modèles conceptuels multidimensionnels actuels représentent imparfaitement les connaissances d'agrégation, qui (1) ont une structure et une dynamique complexes et (2) sont fortement contextuelles. Afin de prendre en compte les caractéristiques de ces connaissances, nous proposons de les représenter avec des objets (diagrammes de classes UML) et des règles en langage PRR (Production Rule Representation). Les connaissances d'agrégation statiques sont représentées dans les digrammes de classes, tandis que les règles représentent la dynamique (c'est-à-dire comment l'agrégation peut être effectuée en fonction du contexte). Nous présentons les diagrammes de classes, ainsi qu'une typologie et des exemples de règles associées.Agrégation ; Entrepôt de données ; Modèle conceptuel multidimensionnel ; OLAP ; Règle de production ; UML

Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems

Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels (using roll-up and drill-down functions). Therefore, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects (UML class diagrams) and rules in Production Rule Representation (PRR) language. Static aggregation knowledge is represented in the class diagrams, while rules represent the dynamics (i.e. how aggregation may be performed depending on context). We present the class diagrams, and a typology and examples of associated rules. We argue that this representation of aggregation knowledge allows an early modeling of user requirements in a data warehouse project.Aggregation; Conceptual Multidimensional Model; Data Warehouse; On-line Analytical Processing (OLAP); Production Rule; UML

Towards a semantic quality based approach for business process models improvement

Business process (BP) modeling aims at a better understanding of processes, allowing deciders to improve them. We propose to support this modeling with an approach encompassing methods and tools for BP models quality measurement and improvement. In this paper we focus on semantic quality. The latter is evaluated by aligning BP model concepts with domain knowledge. The alignment is conducted thanks to meta-models. We also define validation rules for checking the completeness of BP models. A medical case study illustrates the main steps of our approach.<br /

Roundtrip engineering of NoSQL databases

International audienceIn this article we present a framework describing a roundtrip engineering process for NoSQLdatabase systems. This framework, based on the Model Driven Engineering approach, is composed of aknowledge base guiding the roundtrip process. Starting from a roundtrip generic scenario, we proposeseveral roundtrip scenarios combining forward and reverse engineering processes. We illustrate ourapproach with an example related to a property graph database. The illustrative scenario consists ofsuccessive steps of model enrichment combined with forward and reverse engineering processes. Futureresearch will consist in designing and implementing the main components of the knowledge base

ARTIFACT EVALUATION IN INFORMATION SYSTEMS DESIGN-SCIENCE RESEARCH – A HOLISTIC VIEW

Design science in Information Systems (IS) research pertains to the creation of artifacts to solve reallife problems. Research on IS artifact evaluation remains at an early stage. In the design-science research literature, evaluation criteria are presented in a fragmented or incomplete manner. This paper addresses the following research questions: which criteria are proposed in the literature to evaluate IS artifacts? Which ones are actually used in published research? How can we structure these criteria? Finally, which evaluation methods emerge as generic means to assess IS artifacts? The artifact resulting from our research comprises three main components: a hierarchy of evaluation criteria for IS artifacts organized according to the dimensions of a system (goal, environment, structure, activity, and evolution), a model providing a high-level abstraction of evaluation methods, and finally, a set of generic evaluation methods which are instantiations of this model. These methods result from an inductive study of twenty-six recently published papers

Évaluation de la gouvernance de l'information : une approche holistique

National audiencePlusieurs facteurs expliquent l'acuité prise au fil de ces dernières années par la question de la gouvernance de l'information. Parmi ceux-ci mentionnons : (i) la nécessaire maîtrise des coûts liés à l'acquisition, l'utilisation et la diffusion de l'information au sein des entreprises, (ii) le respect des normes et règlementations instituées depuis plusieurs années, (iii) les exigences de sécurité face à la multiplication des risques informatiques, et (iv) l'évolution des exigences métiers qui poussent à actualiser les services offerts au moyen de l'information face à la pression de la concurrence. La maîtrise de cette situation requiert des entreprises la définition d'une politique de la gouvernance de l'information. Toutefois, la recherche sur la gouvernance de l'information et sur son évaluation en est encore à ses débuts. A notre connaissance, il n'existe pas de démarche structurée d'évaluation de cette gouvernance. L'objectif de cet article est précisément de combler cette lacune. La gouvernance de l'information constitue un artefact que nous analysons à l'aide de la théorie des systèmes. Nous présentons les facteurs, tant exogènes qu'endogènes, qui servent de base à l'évaluation systémique de la gouvernance de l'information. Enfin, nous proposons une hiérarchie de critères associés à la méthode d'évaluation que nous appliquons à un cas réel

Taxonomy Development for Complex Emerging Technologies - The Case of Business Intelligence and Analytics on the Cloud

Taxonomies are essential in science. By classifying objects or phenomena, they facilitate understanding and decision making. In this paper, we focus on the development of taxonomies for complex emerging technologies. This development raises specific challenges. More specifically, complex emerging technologies are often at the intersection of several areas, and the conceptual body of knowledge about them is often just emerging, hence the key role of empirical sources of information in taxonomy building. One particular issue is deciding when enough sources have been examined. In this paper, we use Nickerson et al’s methodology for taxonomy development. Based on the identified limitations of this method, we extend it for the development of taxonomies for complex emerging technologies. We identify three types of information sources for taxonomies, and present a set of guidelines for selecting the sources, drawing on systematic literature review. The taxonomy development process iteratively examines sources, performing operations on taxonomies (e.g. addition of a dimension, splitting of a dimension…) as required to take new information into account. We characterize operations on taxonomies. We use this characterization, along with the typology of sources, to help decide when the process of source examination may be stopped. We illustrate our extension of Nickerson et al’s method to the development of a taxonomy for business intelligence and analytics on the cloud

A Taxonomy Development Method to Define the Vocabulary for Rule-Based Guidance in Complex Emerging Technologies

Emerging technologies are characterized by their uncertainty and potential impact. Decisions about these technologies are therefore crucial and difficult. The problem is particularly acute for complex emerging technologies, which combine several technologies. Guidance on emerging technologies is often lacking, even more for complex ones. In this research, methods and models to guide practitioners (members of the IT personnel) in the adoption of complex emerging technologies are defined. Guidance is provided by means of productions rules, requiring a controlled vocabulary organized as a taxonomy. The rules, and the vocabulary for the rules, are defined by researchers for a specific complex emerging technology (e.g., business intelligence and analytics in the cloud). They may then be applied by practitioners to decide on the adoption of the emerging technology in a specific organizational context. The approach is based on systematic literature review, thereby contributing to evidence-based practice. This paper focuses on the method to define the controlled vocabulary for the production rules. This taxonomy development method is built by combining systematic literature review with a method for taxonomy development, considering the specificities of rule-based guidance and complex emerging technologies. It is demonstrated on business intelligence and analytics in the cloud and evaluated in a government agency

A pragmatic approach for identifying and managing design science research goals and evaluation criteria

International audienceThe effectiveness of a Design Science Research (DSR) project is judged both by the fitness of the designed artifact as a solution in the application environment and by the level of new research contributions. An important and understudied challenge is how to translate DSR project research goals into discrete and measurable evaluation criteria for use in the DSR processes. This position paper proposes an inclusive approach for articulating DSR goals and then identifying project evaluation criteria for these goals. The goals are organized hierarchically as utilitarian goals, safety goals, interaction and communication goals, cognitive and aesthetic goals, innovation goals, and evolution goals. Goals in a DSR project are identified pragmatically by considering the components of the context coupled with the hierarchy of goals. Based on the identified goals, the associated evaluation criteria are determined and organized along the same hierarchy. These criteria measure the ability of the artifact to meet its goals in itscontext (immediate fitness). Moreover, our approach also supports the innovation and research contributions of the project. The apex of the goal hierarchy addresses the identification of criteria measuring the fitness for evolution of the designed artifact, to accommodate for changes in goals or context

Conceptual Modeling of Prosopographic Databases Integrating Quality Dimensions

International audienceProsopographic databases, which allow the study of social groups through their bibliography, are used today by a significant number of historians. Computerization has allowed intensive and large-scale exploitation of these databases. The modeling of these proposopographic databases has given rise to several data models. An important problem is to ensure a level of quality of the stored information. In this article , we propose a generic data model allowing to describe most of the existing prosopographic databases and to enrich them by integrating several quality concepts such as uncertainty, reliability, accuracy or completeness