Special Issue on
Artificial Intelligence Applications in Enterprise Modeling IEEE Expert announces a Special Issue on the applications of Artificial Intelligence and Knowledge Based Systems to Enterprise Modeling.
This Special Issue will describe work in which a model-based approach is taken to describing an enterprise and using this for capturing, analysing, (re-)engineering and controlling the work of the organization. Topics of interest include: enterprise ontologies; sector-specific ontologies and models; enterprise modeling methods and support tools; model-based analysis of enterprises; distributed business problem solving; model-based approaches to enterprise process re-engineering; intelligent workflow management; enterprise integration techniques and architectures; and other relevant topics.
The aim of the Special Issue is to communicate the current state-of-the-art in applying AI and KBS approaches to problems in enterprise modeling and management and to show the breadth of current opportunities in this field.
IEEE Expert is a magazine of applied AI. It is not a journal for detailed technical results. The magazine is a bridge between the research community and the user community. It aims to publish original papers that transfer to the user community ideas and tools that come out of research and development. Clear, not overly formal, writing is essential. Its readers are users, developers, managers, researchers, and purchasers who are interested in databases, expert systems, and artificial intelligence, with particular emphasis on applications. They want to learn about the tools, techniques, concepts, aids, and systems that have potential for real-world applications. Conceptual or theoretical papers are welcome, provided they serve the above function of clarifying and presenting ideas of potential importance to applications.
Three different types of paper are sought for the Special Issue:
(a) reviews of some major aspect of the applications of AI and KBS to enterprise modeling and management.
Such articles should be about 9-10 printed pages, with extensive references (1 or 2 such reviews are likely to be included). The references included should all be to widely available published papers.
(b) papers describing some application of AI or KBS techniques to Enterprise Modeling. Such papers should describe both the techniques and the area in which they are being applied. References should allow the interested reader to follow-up on both techniques and the application. Papers which show the novel integration of a number of AI/KBS and conventional techniques will also be welcome.
Such articles should be about 8-9 printed pages, with about 10-12 references (4 or 5 such papers are likely to be included).
(c) case histories relating the applications experience of using nominated AI/KBS techniques in an Enterprise Modeling setting. Positive and negative experiences may be reported.
Such articles should be about 2-3 printed pages, with at least 3 references (4 or 5 such case histories are likely to be included).
Submissions should be written according the IEEE Expert style. Papers accepted on technical grounds are subject to copy editing by the Managing Editor's staff for clarity and expressiveness.
Authors proposing to submit reviews of type (a) are asked to contact the guest editor, Austin Tate, by e-mail, post or fax, and to send an extended abstract of the proposed article in advance of full submission to discuss the content. This extended abstract should be received by 1st June 1995.
Note that, by IEEE policy, such reviews are subject to the same review process as other submissions and involves three reviewers.
Austin Tate Artificial Intelligence Applications Institute University of Edinburgh 80 South Bridge Edinburgh EH1 1HN UK Tel: +44 (131) 650 2732 Fax: +44 (131) 650 6513 E-mail: A.Tate@ed.ac.uk
All authors are asked to submit six copies (hard-copy only), of their paper by 1st August 1995 to:
Steve E. Cross School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 USA Tel: (412) 268-5543 Fax: (412) 683-5348 E-mail: scross@cs.cmu.edu