• Automatic Concept Classification of Text From Electronic Meetings

      Chen, Hsinchun; Hsu, P.; Orwig, Richard E.; Hoopes, L.; Nunamaker, Jay F. (ACM, 1994-10)
      In this research we adopted an artificial intelligence (AI) approach to designing an automatic concept classification tool for electronic brainstorming output. The role of AI techniques such as machine learning and neural networks computing in groupware development can be significant. Through extensive content analysis, concept space generation, and neural network-based concept classification, our system can generate a tentative list of the important ideas and topics represented in meeting comments. Participants then can examine the systemâ s suggested list and the underlying comments. They can also revise or augment the list to produce their final consensus list. Allowing the system to act as an â intelligentâ aide for idea organization can alleviate some of the burdens of convergent tasks.
    • Education and training in electronic records management (ERM): The need for partnership building

      Johare, Rusnah; Khoo, C.; Singh, D.; Chaudhry, A.S. (School of Communication & Information, Nanyang Technological University, 2006)
      The use of computers within the electronic environment has led to rapid and dynamic changes in the way governments and businesses operate. One of the significant outcomes of computerization is that managing electronic records now relies on IT and it needs to be integrated into the business processes of an organization. Therefore electronic records management (ERM) not only requires the involvement of key players in recordkeeping, such as records managers and archivists, but also IT personnel and administrators under a common shared responsibility to establish a credible electronic records management programme. According to McLeod, Hare and Johare (2004) managing records in the electronic environment is not only a major challenge but also increasingly a strategic issue for organizations in both the public and private sectors. They suggested that “a key factor in meeting both the challenge and addressing the strategic management is the provision of education and/or training for employees and potential employees (i.e students). In particular, providing this at the appropriate level of detail and in the appropriate areas of the subject, commensurate with roles and responsibilities so that these people can discharge, both effectively and efficiently, their responsibilities for managing records in the electronic environment”. Within this context, this paper examines the education and training opportunities on ERM worldwide and in Asia.
    • HCI and MIS: shared concerns (Editorial)

      Zhang, Ping; Dillon, Andrew; Zhang, Ping; Dillon, Andrew (Elsevier, 2003)
      The fields of HCI and MIS share many concerns but have traditionally not shared literatures, theories and results. This special issue is a first attempt at bridging the disciplinary divide. In this paper, the history of both fields is briefly outlined and reasons for the independence of eachare examined. The criteria for paper inclusion are outlined and each paper is briefly introduced.
    • Intranet, Extranet and Internet: Information Management and sharing in Libraries

      Ghosh, Maitrayee; Avasia, Maya; Parthan, S.; Jeevan, V. (Allied Publisher, New Delhi, 2002)
      The advances in library net working technology has brought an inexpensive way of distributing and sharing information within the organization as well as libraries located in remote areas.The advantages of Intranet, Extranet and Internet being numerous, includes streamlining of the information processing and management, facilitating information dissemination and enriching communications and collaborations. Attempts have been made to discuss opportunities provided by these three advanced networks enabling librarians and information professionals in efficient collection development, management and serving users with value added information at ease.
    • A Multi-Agent View of Strategic Planning Using Group Support Systems and Artificial Intelligence

      Orwig, Richard E.; Chen, Hsinchun; Vogel, D.; Nunamaker, Jay F. (Kluwer, 1996)
      The strategic planning process is dynamic and complex. Including a Group Support System (GSS) in the problem-solving process can improve the content quality of the strategic plan by allowing increased participation by more members of the organization. However, it can also add to the complexity of the problem by increasing the quantity of textual information that can result from group activity. Added complexity increases cognitive overload and frustrations of those participants negotiating the contents of the strategic plan. This article takes a multi-agent view of the strategic planning process. It considers group participants as multiple agents concerned with the content quality of the strategic plan. The facilitator agent is responsible for guiding groups in the strategic plan construction process as well as for solving process problems such as cognitive overload. We introduce an AI Concept Categorizer agent, a software tool that supports the facilitator in addressing the process problem of cognitive overload associated with convergent group activities by synthesizing group textual output into conceptual clusters. The implementation of this tool reduces frustrations which groups encounter in the process of classifying textual output and provides more time for discussion of the concepts themselves. Because of the large amount of convergent activity necessary for strategic planning, the addition of the AI Concept Categorizer to the strategic planning process should increase the quality of the strategic plan and the buy-in of the participants in the strategic planning process.
    • Redesign of Library Workflows: Experimental Models for Electronic Resource Description

      Calhoun, Karen (the Library of Congress, 2000)
      This paper explores the potential for and progress of a gradual transition from a highly centralized model for cataloging to an iterative, collaborative, and broadly distributed model for electronic resource description. The author's purpose is to alert library managers to some experiments underway and to help them conceptualize new methods for defining, planning, and leading the e-resource description process under moderate to severe time and staffing constraints. To build a coherent library system for discovery and retrieval of networked resources, librarians and technologists are experimenting with team-based efforts and new workflows for metadata creation. In an emerging new service model for e-resource description, metadata can come from selectors, public service librarians, information technology staff, authors, vendors, publishers, and catalogers. Arguing that e-resource description demands a level of cross-functional collaboration and creative problem-solving that is often constrained by libraries' functional organizational structures, the author calls for reuniting functional groups into virtual teams that can integrate the e-resource description process, speed up operations, and provide better service. The paper includes an examination of the traditional division of labor for producing catalogs and bibliographies, a discussion of experiments that deploy a widely distributed e-resource description process (e.g., the use of CORC at Cornell and Brown), and an exploration of the results of a brief study of selected ARL libraries' e-resource discovery systems.
    • Verifying the proximity and size hypothesis for self-organizing maps

      Lin, Chienting; Chen, Hsinchun; Nunamaker, Jay F. (M.E. Sharpe, Inc., 2000-12)
      The Kohonen Self-Organizing Map (SOM) is an unsupervised learning technique for summarizing high-dimensional data so that similar inputs are, in general, mapped close to one another. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection and to present major topics within the collection with larger regions. Research in which properties of SOM were validated, called the Proximity and Size Hypotheses,is presented through a user evaluation study. Building upon the previous research in automatic concept generation and classification, it is demonstrated that the Kohonen SOM was able to perform concept clustering effectively, based on its concept precision and recall7 scores as judged by human experts. A positive relationship between the size of an SOM region and the number of documents contained in the region is also demonstrated.