Semi-automatic conceptual data modeling using entity and relationship instance repositories

Conceptual modeling is the foundation of analysis and design methodologies for the development of information systems. It is challenging because it requires a clear understanding of an application domain and an ability to translate the requirement specifications into a data model. However, novice designers frequently lack experience and have incomplete knowledge about the application being designed. We propose new types of reusable artifacts called Entity Instance Repository (EIR) and Relationship Instance Repository (RIR), which contain ER (Entity-Relationship) modeling patterns from prior designs and serve as knowledge-based repositories for conceptual modeling. We also select six data modeling rules to check whether they are comprehensive enough in creating quality conceptual models. This research aims to develop effective knowledge-based systems (KBSs) with EIR and RIR. Our proposed artifacts are likely to be useful for conceptual designs in the following aspects: (1) they contain knowledge about a domain; (2) automatic generation of EIR and RIR overcomes a major problem ofinefficient manual approaches that depend on experienced modeling designers and domain experts; and (3) they are domain-specific and therefore easier to understand and reuse. Two KBSs were developed in this study: Heuristic-Based Technique (HBT) and Entity Instance Pattern WordNet (EIPW). The goals of this study are (1) to find effective approaches that can improve the novice designers’ performance in developing conceptual models by integrating pattern-based technique and various modeling techniques, (2) to evaluate whether those selected six modeling rules are effective in HBT, and (3) to validate whether the proposed KBSs are effective in creating quality conceptual models. To assess the potential of the KBSs to benefit practice, empirical testing was conductedon tasks of different sizes. The empirical results indicate that novice designers’ overall performance increased by 30.9~46.0 % when using EIPW, and increased by 33.5~34.9% when using HBT, compared with the cases of no tools.Ph.D., Information Studies -- Drexel University, 201

Representing knowledge patterns in a conceptual database design aid : a dual-base knowledge model

The current status of the Knowledge-Based Database Design Systems (KBDDSs) is reviewed. It is shown that they do not resolve the problems of the identification of the relevant objects (relations) and the interpretation of the identified objects from the semantic-rich reality. Consequently, a theoretical architecture is developed to alleviate these problems by reusing the finished conceptual data schemata. By taking account of the essence of the reality and the problem-solving behaviour of experts, a new knowledge model called the Dual-Base Knowledge Model (DBKM), which involves two syngeristic knowledge structures, the concept and case bases, is constructed by the theories of conceptual knowledge in the psychological realm and the notions of relation and function from set theory. The aim is to provide rational and valid grounds for the support and interplay of these two bases in order to reuse the relevant old cases and facilitate the acquisition of new cases. Thus, the process model, which involves two process mechanisms, the case retrieval and knowledge accumulation mechanisms, is analysed according to the theory of the proposed DBKM. In this way, the feasibility of reusing the relevant schemata or part of them can be established in the DBKM architecture. The functionality of the DBKM architecture is tested by a simulated example to show how the relevant cases are recalled in the knowledge pool and the new knowledge is stored in the knowledge repository. The distinctions between the DBKM architecture and the frameworks of current KBDDSs and Case-Based Reasoning (CBR) systems (from the knowledge-based system view), and between the DBKM and those knowledge models in current KBDDSs and rule-based data modelling approaches (from the knowledge-modelling view) are investigated to contrast the current levels of progress of the conceptual data modelling. This research establishes the feasibility of the DBKM architecture, although it demonstrates the need to accommodate the dynamic and functional aspects of the Universe of Discourse (UoD). The main contributions of the DBKM are (1) to provide a valid basis for complementing the environments supported by the current KBDDSs and a rational basis for creating the symbiosis of humans and computer; and (2) to moderate the beliefs underlying the fact-based school and provide a hermeneutic environment, so that the confusion of the current conceptualising work can be alleviated and the difficulty of the conceptualising task can be eased to some degree