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

A Framework for Auditing Web-Based Information Systems

The last decade has seen an unprecedented rate of development of Web-Based Information System (WBIS). Enormous investment is currently being made in WBIS systems. There is a concern about whether the true capability of WBIS is being realized. As a consequence, growing attention is being paid to assessing the inherent contribution of WBIS. In this paper, we propose a WBIS audit methodology. The latter has two main features: 1) it structures the audit process as a hierarchical evaluation tree, using an Analytic Hierarchy Process model, 2) it allows the evaluation of a WBIS according to a specific set of criteria based on quality, security and readability requirements. Unlike past approaches, our methodology allows independent auditors, companies and users to minimize the time and effort needed to evaluate WBIS. It has been applied to a real-life example which is described in the paper, allowing us to validate our WBIS audit approach

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

Security requirements analysis based on security and domain ontologies

International audienceSecurity is the discipline concerned with protecting systems from a wide range of threats (malice, error or mischief) that break the system by exploiting a vulnerability, i.e. a property of the system or its environment that, when faced with particular threats, can lead to failure[5] . Security is a multi-faceted problem; it is as much about understanding the domain in which systems operate as it is about the systems themselves. While developing security facilities such as encryption,identity control, or specific architectures is important, our attention should be drawn at looking into the sociotechnical context in which target systems will operate and threats that may arise and their potential harm, so as to uncover security requirements. Recent research has argued about the importance of considering security at the early stages of the information systems development process, and especially the need to consider security during RE. An ontology, in the field of knowledge representation, is most often defined as "a representation of a conceptualization". It should represent a shared conceptualization in order to have any useful purpose. Ontologies are useful for representing and interrelating many types of knowledge. Several security ontologies have been proposed. Domain ontologies are formal descriptions of classes of concepts and relationships between these concepts that describe a given domain. Our previous experience with RITA, a requirements elicitation method that exploits a just one threat ontology, was that "being generic, the threats in the RITA ontology are not specific to the target [bank] industry" (the case study was in the banking sector). Experts involved in the evaluation complained about "the lack of specificity of the types of threats to the industry sector and the problem domain at hand". The problem that remains open is therefore that we need to exploit both security knowledge and domain knowledge to guide the elicitation of domain-specific security requirements. Our research question is "how to combine the use of security ontologies and domain ontologies to guide requirements elicitation efficiently?" This paper presents an ongoing research project that aims to develop a method that explores the use of security and domain ontologies for SRE

Representation of Aggregation Knowledge in OLAP Systems

Decision support systems are mainly 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 OLAP operators such as roll-up and drill-down. Roll-up operators decrease the details of the measure, aggregating it along the dimension hierarchy. Conversely, drill-down operators increase the details of the measure. As a consequence, dimensions hierarchies play a central role in knowledge representation. More precisely, since aggregation hierarchies are widely used to support data aggregation, 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 and rules. Static aggregation knowledge is represented using UML class diagrams, while rules, which represent the dynamics (i.e. how aggregation may be performed depending on context), are represented using the Production Rule Representation (PRR) language. The latter allows us to incorporate dynamic aggregation knowledge. We argue that this representation of aggregation knowledge allows an early modeling of user requirements in a decision support system project. In order to illustrate the applicability and benefits of our approach, we exemplify the production rules and present an application scenario

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

Using Security and Domain ontologies for Security Requirements Analysis

International audienceRecent research has argued about the importance of considering security during Requirements Engineering (RE) stage. Literature also emphasizes the importance of using ontologies to facilitate requirements elicitation. Ontologies are known to be rich sources of knowledge, and, being structured and equipped with reasoning features, they form a powerful tool to handle requirements. We believe that security being a multi-faceted problem, a single security ontology is not enough to guide SR Engineering (SRE) efficiently. Indeed, security ontologies only focus on technical and domain independent aspects of security. Therefore, one can hypothesize that domain knowledge is needed too. Our question is "how to combine the use of security ontologies and domain ontologies to guide requirements elicitation efficiently and effectively?" We propose a method that exploits both types of ontologies dynamically through a collection of heuristic production rules. We demonstrate that the combined use of security ontologies with domain ontologies to guide SR elicitation is more effective than just relying on security ontologies. This paper presents our method and reports a preliminary evaluation conducted through critical analysis by experts. The evaluation shows that the method provides a good balance between the genericity with respect to the ontologies (which do not need to be selected in advance), and the specificity of the elicited requirements with respect to the domain at hand

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

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 /