دانلود مقاله ISI انگلیسی شماره 22617
عنوان فارسی مقاله

زبان مدل سازی ادغام چابک : دستور زبان مدل سازی مفهومی برای سیستم های اطلاعاتی تجاری یکپارچه چابک

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
22617 2007 19 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Agile Integration Modeling Language (AIML): A conceptual modeling grammar for agile integrative business information systems
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Decision Support Systems, Volume 44, Issue 1, November 2007, Pages 266–284

کلمات کلیدی
دستور زبان مدل سازی مفهومی - سیستم و مدل سازی - مشخصات مورد نیاز - مدل سازی مبتنی بر نقش - سیستم و مدل سازی چند عاملی - سیستم های اطلاعاتی مدل سازی تجاری یکپارچه -
پیش نمایش مقاله
پیش نمایش مقاله زبان مدل سازی ادغام چابک : دستور زبان مدل سازی مفهومی برای سیستم های اطلاعاتی تجاری یکپارچه چابک

چکیده انگلیسی

The proliferation of newer agile integrative business information systems (IBIS) environments that use the software agent and the multiagent systems paradigms has created the need for a common and well-accepted conceptual modeling grammar that can be used to efficiently, precisely, and unambiguously, model agile IBIS systems at the conceptual level. In this paper, we propose a conceptual modeling grammar termed Agile Integration Modeling Language (AIML) based on established ontological foundation for the multiagent-based integrative business information systems (MIBIS) universe. The AIML grammar provides adequate and precise constructs and semantics for modeling agile integration among participating work systems in terms of quickly building and dismantling dynamic collaboration relationships among them to respond to fast-changing market needs. The AIML grammar is defined as a formal model using Extended BNF and first order logic, and is elaborated using a running example in the paper. The grammar is also evaluated in terms of its syntactic, semantic, and pragmatic qualities and is found to exhibit a high degree of quality on all these three dimensions. In particular, the pragmatic quality of AIML measured in terms of grammar complexity evaluated using complexity metrics indicates that AIML is much easier to learn and use as compared to the Unified Modeling Language (UML) for modeling agile integration of work systems in organizations.

مقدمه انگلیسی

Information systems have been playing an important role in supporting business integration. Traditional integrative business information systems (IBIS) [18] and [19] such as ERP, EAI, and workflow management systems have brought significant benefits to businesses in terms of improved planning, timely deliveries, reduced inventories, reduced costs, and responsive and improved customer service [19]. However, they take significant amounts of time and effort to develop as all their component work systems5 are tightly coupled to each other. Tight coupling also results in difficulties in adding new and modifying or deleting existing collaborative relationships among participating work systems. On the other hand, current hypercompetitive business environment requires business organizations to quickly build as well as dismantle dynamic collaboration relationships among various participating work systems, both internally and externally, to respond to fast-changing market needs. Consequently, newer agile IBIS systems6 should allow participating work systems to integrate with each other while preserving their local autonomy and coordinating in a decentralized manner. In an agile IBIS system, we envision no overall control over participating work systems and integration occurs through dynamic coordination among the participating work systems, in addition to the necessary integration at the technology and data levels [19]. In such a scenario, business processes are rather dynamic and emergent, relying on the judgments and decisions of individual work systems. To achieve this type of integration, an IBIS system should allow participating work systems to reach agreements about service contracts on their own without central control. Further, users and developers should be able to configure various business processes and collaboration relationships among work systems dynamically as the goals and needs of the organization change. Recently, multiagent systems comprising of collaborating software agents have emerged as a new technology to solve complex problems in a distributed environment [40]. A software agent is “a computer system that is situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives” [41]. An intelligent software agent within a multiagent system is autonomous (i.e., it acts without human intervention), reactive (i.e., it responds to events in the environment), proactive (i.e., it is goal-directed), and social (i.e., it interacts with other software agents to get what it needs) [41]. The multiagent systems paradigm has a number of parallels with the IBIS paradigm as noted in earlier work [19]. Further, due to their dynamic coordination and collaboration capabilities, software agents in multiagent systems are uniquely capable of supporting integration in agile IBIS systems as they provide flexibility in resolving inconsistencies and dependencies involved in work systems coordination. Further, software agent as a modeling paradigm minimizes the semantic gap between work system coordination and information system modeling [14]. As a result, software agents have been adopted by a number of researchers in various IBIS applications such as e-commerce [e.g., [16] and [28]], business process management [e.g., [15] and [21]], supply chains management [e.g., [12] and [33]], enterprise integration [e.g., [22] and [32]], and manufacturing [e.g., [10] and [20]] to create more agile integrative environments. This proliferation of newer agile IBIS environments that use the software agent and the multiagent systems paradigms has created the need for a common and well-accepted conceptual modeling grammar that can be used to efficiently, precisely, and unambiguously, model agile IBIS systems. This is because existing modeling grammars such as ERD, object-oriented techniques, and business process modeling techniques lack requisite capabilities to model autonomy, intelligence, and agile coordination and collaboration that are central to agile IBIS systems. Most agent-oriented systems development methodologies also do not have conceptual-modeling-level constructs formally defined with unambiguous semantics. Further, researchers and designers that apply the software agent principles and concepts in various agile IBIS applications have defined their own unique and application-specific constructs and rules for conceptual modeling of agile IBIS systems which limits knowledge sharing and reuse in the agile IBIS community and may result in compatibility issues among future IBIS systems. In response to this state of craftsmanship, Kishore et al. [19] synthesized literature in the areas of IBIS systems and multiagent systems with the intent of developing a comprehensive foundation ontology for the universe of Multiagent-based Integrative Business Information Systems (MIBIS) that can become the basis for a sound conceptual modeling grammar for a variety of agile IBIS systems. We extend Kishore et al.'s work and develop a formal conceptual modeling grammar termed Agile Integration Modeling Language (AIML) 7 for modeling of agile IBIS systems that belong to the MIBIS universe and utilize the notions and principles of multiagent systems for agile integration. This grammar is defined in ISO/IEC 14977 Extended BNF [13] and formally specified in first-order logic. AIML facilitates MIBIS modeling in several ways. First, AIML serves as a foundation ontology for MIBIS knowledge representation, which can be shared and reused during the process of system analysis and design. Second, the formally defined AIML constructs serve as templates to be customized and enhanced for individual MIBIS applications. Finally, the rigorous formalism of AIML serves as an analytical tool for developers to detect and avoid possible conflicts in MIBIS modeling. The paper is organized as follows. In Section 2, we summarize the conceptual framework for the MIBIS universe described by Kishore et al. [19] and discuss the need for a conceptual modeling grammar for this universe. In Section 3, the AIML grammar is elaborated, formally defined, and explained through examples. In Section 4, we evaluate the quality of the AIML grammar. Finally, Section 5 concludes the paper with remarks on some future research directions.

نتیجه گیری انگلیسی

Large-scale use of multiagent technology in various integrative business information systems requires a special-purpose conceptual-modeling grammar to facilitate the analysis and design of MIBIS systems. In response to such needs, we have developed the AIML grammar for conceptual modeling and high-level design of agile IBIS systems in the MIBIS universe. The grammar provides formal definitions for constructs that form an ontological foundation for representing the MIBIS universe, and provides a starting point for ontology-driven MIBIS development. The grammar benefits both IBIS researchers and practitioners. On the one hand, it advances researchers' understanding of the MIBIS universe and thus forms a foundation for developing various methodologies for development of agile IBIS systems in the MIBIS universe. On the other hand, the formal definitions of the AIML constructs provide templates for IBIS practitioners to avoid developing individual applications from scratch each time, thereby facilitatating system development knowledge reuse. There are several future research directions to further this study. First, in order to enhance the reuse of MIBIS domain-specific knowledge, lower-level ontological categories for the AIML foundation constructs need to be developed (e.g., an ontology of MIBIS roles, an ontology of MIBIS goals, etc.). Second, further elaboration of the AIML grammar and axiomatic proofs are needed to increase the rigor of this grammar. Third, interactions in current study are limited to direct interactions between a pair of roles. In future work, interactions should be extended to include those between more than two roles. Fourth, knowledge in this study is limited to deductive reasoning and it is possible to extend AIML to include other types of knowledge and reasoners, such as abductive, inductive, and case-based reasoning, etc. Last, but not the least, appropriate methodologies and software tools will also need to be developed to support the analysis, design, and development of agile IBIS systems in the MIBIS universe.

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