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Ilyeol Song - One of the best experts on this subject based on the ideXlab platform.

  • semi automatic Conceptual Data Modeling using entity and relationship instance repositories
    International Conference on Conceptual Modeling, 2011
    Co-Authors: Ornsiri Thonggoom, Ilyeol Song
    Abstract:

    Data modelers frequently lack experience and have incomplete knowledge about the application being designed. To address this issue, we propose new types of reusable artifacts called Entity Instance Repository (EIR) and Relationship Instance Repository (RIR), which contain ER Modeling patterns from prior designs and serve as knowledge-based repositories for Conceptual Modeling. We explore the development of automated Data Modeling tools with EIR and RIR. We also select six Data Modeling rules used for identification of entities in one of the tools. Two tools 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 modelers' performance in developing Conceptual models by integrating patternbased technique and various Modeling techniques, (2) to evaluate whether those selected six Modeling rules are effective, and (3) to validate whether the proposed tools are effective in creating quality Data models. In order to evaluate the effectiveness of the tools, empirical testing was conducted on tasks of different sizes. The empirical results indicate that novice designers' overall performance increased 30.9-46.0% when using EIPW, and increased 33.5-34.9 % when using HBT, compared with the cases with no tools.

Ornsiri Thonggoom - One of the best experts on this subject based on the ideXlab platform.

  • semi automatic Conceptual Data Modeling using entity and relationship instance repositories
    International Conference on Conceptual Modeling, 2011
    Co-Authors: Ornsiri Thonggoom, Ilyeol Song
    Abstract:

    Data modelers frequently lack experience and have incomplete knowledge about the application being designed. To address this issue, we propose new types of reusable artifacts called Entity Instance Repository (EIR) and Relationship Instance Repository (RIR), which contain ER Modeling patterns from prior designs and serve as knowledge-based repositories for Conceptual Modeling. We explore the development of automated Data Modeling tools with EIR and RIR. We also select six Data Modeling rules used for identification of entities in one of the tools. Two tools 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 modelers' performance in developing Conceptual models by integrating patternbased technique and various Modeling techniques, (2) to evaluate whether those selected six Modeling rules are effective, and (3) to validate whether the proposed tools are effective in creating quality Data models. In order to evaluate the effectiveness of the tools, empirical testing was conducted on tasks of different sizes. The empirical results indicate that novice designers' overall performance increased 30.9-46.0% when using EIPW, and increased 33.5-34.9 % when using HBT, compared with the cases with no tools.

Z. M. - One of the best experts on this subject based on the ideXlab platform.

  • Conceptual design of object oriented Databases for fuzzy engineering information Modeling
    Computer-Aided Engineering, 2013
    Co-Authors: Z. M.
    Abstract:

    Conceptual Data Modeling is essential for engineering applications. IDEF1X Integration Definition for Information Modeling Data model provides a formal framework for consistent Modeling of the Data necessary for the integration of various functional areas and its basic idea has been extensively applied in current industry. Information in real-world applications is often vague or ambiguous. Fuzziness is inherent in many engineering activities. In order to completely model fuzzy engineering information at different abstraction levels of Data model, object instances and attribute values in the IDEF1X Data model, this paper introduces three levels of fuzziness into the IDEF1X Data model, and the formal descriptions and the corresponding graphical representations are hereby presented. The formal transformation from the fuzzy IDEF1X Data model to the fuzzy object-oriented Database model is investigated. The formal transformation approaches proposed in the paper are demonstrated with examples.

Catharina Maria Keet - One of the best experts on this subject based on the ideXlab platform.

  • ontology driven formal Conceptual Data Modeling for biological Data analysis
    Biological Knowledge Discovery Handbook: Preprocessing Mining and Postprocessing of Biological Data, 2013
    Co-Authors: Catharina Maria Keet
    Abstract:

    Biological Data Modeling serves many purposes, and many approaches exist that are used in this endeavor. The main topics of advanced Conceptual Data Modeling for Database and Object-Oriented software development to support biological Data analysis are included in Figure 1.1, which extend the traditional ‘waterfall’ software development methodology as depicted in bold in Figure 1.2. The scope of this chapter is to provide an overview of the ontological and logical aspects of Conceptual Data Modeling tailored to molecular biology and biological knowledge discovery. Many Databases and software applications have been and are being developed in bioinformatics, which, following good computing methodologies, are—or should have been—developed in stages, going from requirements analysis (‘what should the envisioned software do?’) and Conceptual analysis (‘what Data should it be able to manage?’) to design-level code and then to the actual implementation. It is well-known that omitting the Conceptual analysis stage by going straight to coding or scripting just adds to the pile of one-off (bioinformatics) tools that have more bugs and are much less, or not at all, maintainable and interoperable. Conversely, availing of a proper software development methodology with a represen-

Robert Meersman - One of the best experts on this subject based on the ideXlab platform.

  • on using Conceptual Data Modeling for ontology engineering
    Lecture Notes in Computer Science, 2004
    Co-Authors: Mustafa Jarrar, Jan Demey, Robert Meersman
    Abstract:

    This paper tackles two main disparities between Conceptual Data schemes and ontologies, which should be taken into account when (re)using Conceptual Data Modeling techniques for building ontologies. Firstly, Conceptual schemes are intended to be used during design phases and not at the run-time of applications, while ontologies are typically used and accessed at run-time. To handle this first difference, we define a Conceptual markup language (ORM-ML) that allows to represent ORM Conceptual diagrams in an open, textual syntax, so that ORM schemes can be shared, exchanged, and processed at the run-time of autonomous applications. Secondly, unlike ontologies that are supposed to hold application-independent domain knowledge, Conceptual schemes were developed only for the use of an enterprise application(s), i.e. “in-house” usage. Hence, we present an ontology engineering-framework that enables reusing Conceptual Modeling approaches in Modeling and representing ontologies. In this approach we prevent application-specific knowledge to enter or to be mixed with domain knowledge. To end, we present DogmaModeler: an ontology-engineering tool that implements the ideas presented in the paper.