Samira Si-Said Cherfi

Conceptual Modeling Quality - From EER to UML Schemas Evaluation

This exploratory research investigates the evaluation process of conceptual specifications developed using either Extended Entity-Relationship (EER) or Unified Modeling Language (UML) conceptual models. In this paper, we provide a comprehensive framework for evaluating EER and UML conceptual schemas. Furthermore, we define classes of metrics facilitating the evaluation process and leading to the choice of the appropriate representation among several schemas describing the same reality. Based on quality criteria proposed in the literature, we select a subset of criteria relevant to conceptual EER schema quality evaluation. For each criterion we define one or several metrics allowing the designer to measure the schema quality. We evaluate alternative EER conceptual schemas representing the same universe of discourse using the appropriate criteria and their associated metrics. Finally, we extrapolate this evaluation process to UML schemas. Following the development of our framework, we analyze a case study and provide evidence in the support of the usefulness of the framework.

Multidimensional Schemas Quality: Assessing and Balancing Analyzability and Simplicity

A data warehouse is a database focused on decision making. Decision makers typically access data warehouses through OLAP tools, based on a multidimensional representation of data. In the past, the key issue of data warehouse quality has often been centered on data quality. However, since OLAP tool users directly access multidimensional schemas, multidimensional schema quality evaluation is also crucial. This paper focuses on the quality of multidimensional schemas, more specifically on the analyzability and simplicity criteria. We present the underlying multidimensional model and address the problem of measuring and finding the right balance between analyzability and simplicity of multidimensional schemas. Analyzability and simplicity are assessed using quality metrics which are described and illustrated based on a case study. The main objective of our approach is to provide the data warehouse designer with precise measures to support him in the choice among several alternative multidimensional schemas.

Evaluating the Functionality of Conceptual Models

Conceptual models serve as the blueprints of information systems and their quality plays decisive role in the success of the end system. It has been witnessed that majority of the IS change-requests results due to deficient functionalities in the information systems. Therefore, a good analysis and design method should ensure that conceptual models are functionally correct and complete, as they are the communicating mediator between the users and the development team. Conceptual model is said to be functionally complete if it represents all the relevant features of the application domain and covers all the specified requirements. Our approach evaluates the functional aspects on multiple levels of granularity in addition to providing the corrective actions or transformation for improvement. This approach has been empirically validated by practitioners through a survey.

Use case modeling and refinement: a quality-based approach

In this paper, we propose a quality-based use case refinement approach. It consists of a step by step refinement process that combines quality metrics with use case transformation rules. We propose several quality metrics, based on complexity concepts, aimed at measuring the complexity of use cases. Starting from an initial use case, we apply successively a set of transformation rules and measure the resulting use case based on the quality metrics. Our approach is embedded in a general framework allowing us to guide software designers by the mean of quality metrics.

Aligning Business Process Models and Domain Knowledge: A Meta-modeling Approach

In recent years the problems related to modeling and improving business processes have been of growing interest. Indeed, companies are realizing the undeniable impact of a better understanding and management of business processes (BP) on the effectiveness, consistency, and transparency of their business operations. BP modeling aims at a better understanding of processes, allowing deciders to achieve strategic goals of the company. However, inexperienced systems analysts often lack domain knowledge leading and this affects the quality of models they produce. In this paper we propose to support this modeling effort with an approach that uses domain knowledge to improve the semantic quality of BP models. This approach relies on domain ontologies as a mean to capture domain knowledge and on meta-modeling techniques. The main contribution of this paper is threefold: 1) the metamodels describing both a domain ontology and a BP model are described, 2) the alignment between the concepts of both meta-models is defined and illustrated, 3) a set of OCL mapping rules is provided. A simple case study illustrates the process.

Improving Business Process Model Quality Using Domain Ontologies

This paper addresses the issue of improving quality of business process (BP) models by exploiting domain knowledge. Indeed, business process models reflect the business processes of companies. The success of these processes has a direct and undeniable impact on business operations success. Managing them through their underlying models helps improving their effectiveness, consistency, and transparency. BP modeling aims at a better understanding of processes, allowing deciders to achieve strategic goals of the company. However, several studies from the literature showed that in experienced system analysts often produce low-level quality. This situation is partly due to lack of domain knowledge. In this paper, we propose to support this modeling effort with an approach that uses domain knowledge to improve the semantic quality of BP models. We suggest to use ontologies as a mean to capture domain knowledge and meta-modeling techniques to deal with BP models independently of languages in which they are expressed. Our contribution is threefold: (1) the meta-models describing both a domain ontology and a BP model are described, (2) the alignment between the concepts of both meta-models is defined and illustrated, (3) a set of Object Constraint Language mapping rules is provided. A simple case study illustrates the process.

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.

Data quality through model quality: a quality model for measuring and improving the understandability of conceptual models

Data quality has emerged as an important and challenging topic in recent years. This article addresses the conceptual model quality as it has been widely accepted that better conceptual models produce better information systems and thus implicitly improve the data quality. Conceptual Models are designed as part of the analysis phase and serve as a communicating mediator between the users and the development team. Consequently, their understandability is a real challenge to avoid the propagation of inaccurate interpretation of the user requirements to the underlying system design and implementation. In this paper, we propose an adaptive quality model. We illustrate its usefulness by describing how it can be used to model and evaluate the understandability of conceptual models. Our quality evaluation is enriched with corrective actions provided to the designer, leading to a guidance modeling process. A first validation based on a survey is proposed.

Quality of conceptual schemas an experimental comparison

Frequently the behaviour of an information system is functionally correct, but it does not meet some quality criteria, such as completeness, consistency, and usability. One way to enhance the capability of an information system is to consider its conceptual model quality as well as its functional behaviour. Conceptual model quality can be defined as a set of perceivable characteristics expressed with quantifiable parameters. The aim of this empirical investigation is to evaluate the quality of different potential conceptual models of the same universe of discourse by different Information Systems (IS) stakeholders. This paper describes: a) a set of quality factors (clarity, simplicity, expressiveness, minimality) applied to different versions of entity-relationship (ER) conceptual schemas, b) an approach enabling a comprehensive comparison of the conceptual schemas, c) an experimentation leading to the evaluation of the same schemas by IS stakeholders such as designers, end-users, and students, based on a sample of about 120 observations using different statistical methods. First results indicate that there exists a strong independence between the IS stakeholders and the quality factors used. A second result reveals a significant difference between groups of respondents in their ways to perceive conceptual schemaspsila quality. Based on our experiment, we are able to identify quality factors relevant to different groups of stakeholders, depending on several dimensions, such as their professional experience, and/or their specialization degree.

A pattern-oriented methodology for conceptual modeling evaluation and improvement

Conceptual models are of prime importance to ensure a high level of quality in designing information systems. It has been witnessed that the majority of information systems (IS) change requests result due to deficient functionalities in the information systems. Therefore, a good analysis and design method should guarantee that conceptual models are correct and complete and easy to understand, as they are the communicating mediator between the users and the development team. Similarly, if models are complex then their extension or the incorporation of missing requirements gets very difficult for the designers. Our approach evaluates the conceptual models on multiple levels of granularity in addition to providing the corrective actions or transformations for improvement. We propose quality patterns to help the non-expert users in evaluating their models with respect to their quality goal. This paper also illustrates our approach by describing an evaluation and improvement process using a case study.