Browsing by Author "A. Mansour, Hesham"

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    KDPMEL: A Knowledge Discovery Process Modeling and Enacting Language
    (The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016) A. Mansour, Hesham
    Knowledge Discovery in Databases (KDD) is a complex process that is highly interactive and iterative. Managing the process and its underlying resources and interacting activities is a very complex task. Today, KDD projects are typically approached in an unstructured, ad hoc manner due to a lack of formal methods and methodologies for their development. The similarities between KDD processes and software development processes suggest that approaches used to manage the development of software processes, such as process programming and processcentered support environments, are also applicable and in fact advantageous to KDD processes. This paper proposes the Knowledge Discovery Process Modeling and Enacting Language (KDPMEL) that is designed specifically to provide both the syntax and semantics needed to capture and execute KDD processes. KDPMEL combines aspects of both process programming and KDD to represent KDD processes precisely as process programs. Along with KDPMEL, an Integrated Development Environment (IDE) for KDPMEL is proposed to enable and facilitate the development and execution of KDPMEL programs. We illustrate and evaluate KDPMEL by representing and executing illustrative examples of KDD activities and tasks
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    A Language-Based and Process-Oriented Approach for Supporting the Knowledge Discovery Processes
    (The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2015) A. Mansour, Hesham; Duchamp, Daniel; Krapp, Carl-Arndt
    Knowledge Discovery in Databases (KDD) processes are complex, highly interactive and iterative. The similarities between KDD processes and software development processes suggest that approaches used to manage the development of software processes are also applicable and in fact advantageous to KDD processes. In this paper, we examine the current approaches for supporting KDD and note to their limitations in providing comprehensive and effective process support. We propose a language-based and process-oriented approach for supporting KDD processes that is based on explicitly representing KDD processes as process programs that can be analyzed, validated, and enacted. We illustrate the proposed approach using a novel process programming language that is designed to describe general process concepts as well as specific KDD concepts. Along with the KDD process language, an IDE-style development environment is proposed to assist in modeling and enacting KDD processes. The overall approach is evaluated and illustrated by modeling and enacting a traditional KDD process.
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    PCSE-KDD: A Process-Centered Support Environment for the Knowledge Discovery Processes
    (The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016) A. Mansour, Hesham
    Current support for Knowledge Discovery in Databases (KDD) is provided only for fragments of the process, a particular KDD process model, or most recently certain process aspects. The support needed for a KDD process varies greatly based on the specifications of the concrete KDD process, and cannot be based purely on a generic process model. There is a need for a more comprehensive support approach that can cover the entire process, target concrete process specifications, and include various aspects of the process. KDD processes are similar to software processes and they can benefit from advancement of software engineering and process technology to facilitate their development, support their execution, and ultimately improve their effectiveness, utilization, and outcomes. This paper proposes the Process-Centered Support Environment for KDD (PCSE-KDD) processes that is based on explicitly representing these processes as process programs that can be developed, managed, and enacted by the environment. This approach has been successfully used to provide support for developing software processes and we propose to transplant this approach into the KDD field. With the proposed approach, KDD processes can be flexibly captured at different levels of details in a clear, precise, and explicit way that can enable reasoning about the process, insuring its correct execution, and supporting its performance