Knowledge that Transforms

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsEric K. ClemonsEric K. Clemons is Professor of Operations and Information Management at The Wharton School of the University of Pennsylvania His research and teaching interests include strategic information systems, the use of information systems in international securities markets, the impact of systems on consumer packaged goods distribution, and the impact of information technology on procurement and the use of interfirm markets for intermediate goods and services. He specializes both in assessing the competitive implications of information technology and in managing the risk of large-scale implementation efforts. In addition, Dr. Clemons is Project Director for the Reginald H. Jones Center's Sponsored Research Project on Information, Industry Structure, and Competitive Strategy.His education includes an S.B. in physics from MIT and an M.S. and Ph.D. in operations research from Cornell University. In addition to eighteen years on the faculties of Wharton, Cornell, and Harvard, Dr. Clemons has professional experience as a consultant in both the private and public sectors, and in executive education both domestically and abroad.Dr. Clemons is currently a member of the editorial board of the Journal of Management Information Systems, Information & Management, and the Computing Practices section of the Communications of the ACM, and serves on the Quality of Markets Advisory Board of the London Stock Exchange. Recent publications include "Information Technology and Industrial Cooperation: The Changing Economics of Coordination and Ownership," "Managing the Risks of Large Scale Systems Projects: Why So Many Reengineering Projects Fail Unnecessarily," and "Evaluating Strategic Opportunities in Information Technology."Michael C. RowMichael C. Row is a Research Associate and Ph.D. candidate in Operations and Information Management at The Wharton School of the University of Pennsylvania. Mr. Row also works with the Reginald H. Jones Center's Project in Information Systems, Telecommunications, and Business Strategy. His research interests include the impacts of information technology on business strategy, industry structure, and interorganizational relationships. Mr. Row's education includes a B.B.A. in accounting and computer science from the College of William and Mary, and an M.B.A. in strategic planning from The Wharton School. He has been a director of information systems at Management Analysis Center, Inc. and a senior analyst in the management information consulting division of Arthur Andersen & Company.

Jay F. Nunamaker is Regents Professor and Director of the Center for Management of Information at the University of Arizona. He received his Ph.D. in systems engineering and operations research from Case Institute of Technology, an M.S. and a B.S. in engineering from the University of Pittsburgh, and a B.S. from CarnegieMellon University. He was an original member of the ISDOS project (PSL/PS A) under the direction of Professor Daniel Teichroew at Case and the University of Michigan from 1965 to 1968. He was a faculty member at Purdue University prior to founding the MIS department at the University of Arizona in 1974. Under his leadership for eighteen years, the department has become known for its expertise in collaboration technology and the technical aspects of MIS. In 1 992 he received the Arthur Andersen Consulting Professor of the Year award. At the GroupWare '93 Conference in San Jose, he received the GroupWare Achievement Award along with recognition of GroupSystems as best of show in the GDSS category.

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsJack BaroudiJack Baroudi is an associate professor and undergraduate director in the Infonnation Systems Department at New York University’s Stem School of Business. He received his bachelor’s degree in quantitative methods from Boston University, and his M.Phil. and Ph.D. in computer applications and infonnation systems from the Stem School of Business. Professor Baroudi’s research interests center on the management of infonnation systems personnel and their careers. His articles have appeared in Information Systems Research, Journal of management Information Systems, MIS Quarterly, Communications of the ACM, Office: Technology and People, and Computer Personnel.Henry C. LucasHenry C. Lucas, JR. is currently Research Professor of Infonnation Systems at the Leonard N. Stem School of Business, New York University. He received his B.S. from Yale University and his M.S. and Ph.D. from the Sloan School of Management at M.I.T. Professor Lucas has also been on the faculty of the Graduate School of Business at Stanford and has spent leaves at the IBM European Systems Research Institute in La Hulpe, Belgium, on the faculty of INSEAD in Fountainebleau, France, and at Bell Cummunications Research in Morristown, New Jersey. His research interests include the management if information technology, implementation, systems analysis, expert systems, and the impact of technology. He has published articles in information systems and management journals including Management Science, Decision Science, Communications of the ACM, Journal of Management Information Systems, MIS Quarterly, Sloan Management Review, and The Computer Journal, and monographs, including Information Systems Implementation: Testing a Structural Model (Ablex, 1990), of which he was coauthor.

:Graph matching, set covering, and partitioning problems are both theoretically and practically important in decision support systems. Numerous decision support applications have been modeled as graph matching, set covering, and partitioning problems. These include applications in the areas of task assignment, marriage problem. airline crew scheduling. truck deliveries or vehicle routing. political redistricting, and the like. Currently. these applications are supported through programs written in external (to the database system) programming languages that use only the data in the database system. Thus, decision makers or application programmers require extra effort to get required decision support information. As opposed to database systems, the purpose of a model management system (MMS) is to make a wide variety of models available to decision makers so they can apply these models without having to become involved in technical and/or procedural aspects of implementation. The goal of this research is to integrate database systems and model systems for zero-one integer programming problems especially concerning graph matching. set covering, and partitioning problems. Users utilize a single integrated language for both problem formulation and model execution. In order to achieve this goal, we extend relationl operators and the query language SQL. The relational operators are extended with six operators, namely, match, maxmatch, cover, mincover, partition, and minpartition. Some of these operators may take a very long time to find an optimal solution. In this article, we adopted genetic algorithms to find a near-optimal solution of this kind of operators. We found they performed well both on the computational effort and on the quality of the solutions through a variety of test problems. These algorithms are bounded by a polynomial time. therefore, they enable a DBMS to respond to queries involving proposed operators in a restricted amount of time.

We describe a methodology for processing data retrieval and update queries using a form-based natural language interface. For the purpose of illustration, we use computer integrated manufacturing (CIM) as the application domain. The interface consists of a set of fourth-generation interface tools (SQL forms), a set of form definitions, a lexicon, and a parser. The forms are developed from the functional and data models of the system. A form definition consists of a form name, a form object, a set of form fields, and a set of fragment grammars. A form object is a single or composite entity that uniquely identifies a form. Form fields consist of database fields whose values can be entered by users (user-dermed), and others whose values can be derived by the system (system-dermed). Fragment grammars are templates that identify the information requested by user queries. The lexicon consists of all words recognized by the system, their grammatical categories, synonyms, and associations (if any) with database objects and forms. The parser scans a natural language query to identify a form in a bottom–up fashion. The information requested by the user query is determined in a top–down manner by matching the fragment grammars associated with a form against the user query. Extragrammatical inputs with limited deviations from the grammar rules are supported. Elliptical queries are supported by deriving the missing information from those specified in previous queries and forms. Combining a natural language processor with SQL forms allows update queries and prevents violation of database integrity constraints, duplication of records, and invalid data entry.Key Words and Phrases:: database management systems4GLnatural language interfacequery processing Additional informationNotes on contributorsNabil R. AdamNabil R. Adam is a Professor of Computers and Information Systems and Chair of the MS/CIS Department at Rutgers University. He is also a member of the Rutgers Graduate Program in Industrial and Systems Engineering. Dr. Adam received a B.S. degree from Cairo University, and M.S., M.Phil. and Ph.D. degrees from Columbia University. Dr. Adam edited or coedited five books and three special journal issues. He has published papers in a wide range of areas, including simulation, scheduling, query optimization, database modeling, object-oriented databases, and concurrency and consistency control in distributed database systems. He has contributed to such journals as IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering, ACM Computing Surveys, Journal of Management Information Systems, Information Systems, Management Science, and European Journal of Operational Research. He serves on the editorial board of the Journal of Management Information Systems and is currently serving as the Program Chair of the 1994 International Conference on Information and Knowledge Management.Aryya GangopadhyayJames Clifford is an Associate Professor of Information Systems at the Leonard N. Stem School of Business at New York University, where he has been a member of the faculty since 1982. He earned a B.A. in music from Yale University in 1975, and an M.S. and Ph.D. in computer science (in 1979 and 1982, respectively) from the State University of New York at Stony Brook. His major areas of research have been the development of data models and query languages to support the temporal dimension of data, and the application of artificial intelligence to decision-making problems in business. His latest research interest lies in the area of knowledge discovery in large databases. He has authored or edited numerous articles and several books on these subjects.James CliffordAryya Gangopadhyay is an Assistant Professor in the Department of Information Sciences and Systems of the School of Business and Management at Morgan State University. He received his Ph.D. in computer information systems from Rutgers University in 1993. His research interests include knowledge-based query processing, natural language interfaces, and conceptual database design. Dr. Gangopadhyay has contributed to the International Journal of Intelligent and Cooperative Information Systems, and to several conference proceedings.

:Mathematical modeling and analysis are a valuable tool in decision support contexts. Consequently, model management system(s) have become a key component of decision support system generators. Model management systems support modelers in various phases of the modeling life cycle including model representation, formulation, selection, integration, and execution. An important phase of the modeling life cycle involves analyzing and explaining the behavior of the formulated model. Such an analysis is necessary to understand the structure and behavior of the model, and to verify the appropriateness of the model to the problem. Traditional computer-based methods of analysis use the model solution as a basis to explain the model structure that caused the solution. This article presents an alternate approach that explains model behavior using the model structure and commonsense mathematical rules. The approach builds upon the qualitative reasoning methodology, developed in the artificial intelligence area to explain the behaviors of physical devices. A benefit of the qualitative reasoning approach is that it may describe the causes of modeling errors in terms of model structure. I also describe an implemented prototype model preprocessor that uses qualitative reasoning to provide qualitative explanations of the model behavior prior to solving the model. While I do not claim the sufficiency of the qualitative reasoning approach to detect and describe all types of modeling behavior, the article presents several examples demonstrating the utility of the approach.

:Expert systems and multimedia are being incorporated into organizations at increasing rates because, while being independently developed, they possess capabilities appropriate for integration with each other. This paper examines the potential for such integration, providing support to users beyond the capabilities of each independently used technology. An integrated system, essential to the city of Los Angeles during the 1992 riots and the 1994 earthquake, is examined to demonstrate the usefulness and benefits of expert systems and multimedia integration. The potential for this integration is further explored by examining issues related to an integration framework where the two technologies support each other. Three integrated prototype systems are reviewed to promote the legitimacy of expert systems and multimedia integration. Preliminary findings conclude that this integration will offer benefits to various applications.

:Research suggests the practice of self-management can be beneficial to service operations. However, the self-management concept has received relatively little use in the service industry. This article discusses the concept of self-management in the service environment, identifies some of the critical information requirements unique to this environment, and proposes a conceptual model of an information system that supports the developmental functions and business activities of the self-managed, working in the service industry. The proposed self-management information system (SMlS) promotes self-managed behavior by facilitating: scanning and analyzing the environment, setting and contracting goals, monitoring and evaluating work, rewarding or punishing performance, learning new skills, and system maintenance. The SMIS also mitigates some of the barriers to self-management by providing executive management with a control system and by helping to improve the decision-making process of line workers.

:During business analysis, business activities are modeled and analyzed. Redefined models become the blueprints for improved business activities. The cost to produce models of the organization is high and model accuracy is important. Involvement from knowledgeable participants and stakeholders is desirable during business modeling and analysis. Traditional modeling approaches limit direct participation to a small handful of participants. This paper discusses the development and evaluation of an electronic meeting system (EMS) based activity modeling tool. Modeling efforts supported by this new approach are compared with modeling efforts supported by analysts with a single-user tool. The results of this comparison reveal that the EMS-based modeling tool allows a greater number of individuals to participate efficiently in model development. Models are developed between 175 percent and 251 percent faster with the new approach than with the traditional approach. Specific features are discussed that help relatively novice modelers work with analysts to develop models of reasonable quality. Measures are set forth that can be used to assess modeling efficiency and quality.

:In 1991, a series of computer-assisted, industrywide planning meetings were held in New Zealand to develop opportunities for enhancing the country’s competitiveness in world markets. The short-term results of these meetings are reported in (16]. This paper presents the results of a followup study based primarily on interviews with participants one to two years after their meeting. The action plans and participants’ implementation activities are reported as well as their opinions, in retrospect, of the role of the meetings and the impact of electronic meeting technology. The followup results show that the electronically assisted meetings promoted interorganizational learning, and were effective catalysts of industrywide change in situations previously characterized by dysfunctional conflict.