Economics of Information
Local subject classificationactionability
data mining economics
knowledge discovery systems
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CitationA Decision-Theoretic Approach to Data Mining 2003, 33(1):1-10 IEEE Transactions on Systems, Man, and Cybernetics. Part A.
AbstractIn this paper, we develop a decision-theoretic framework for evaluating data mining systems, which employ classification methods, in terms of their utility in decision-making. The decision-theoretic model provides an economic perspective on the value of â extracted knowledge,â in terms of its payoff to the organization, and suggests a wide range of decision problems that arise from this point of view. The relation between the quality of a data mining system and the amount of investment that the decision maker is willing to make is formalized. We propose two ways by which independent data mining systems can be combined and show that the combined data mining system can be used in the decision-making process of the organization to increase payoff. Examples are provided to illustrate the various concepts, and several ways by which the proposed framework can be extended are discussed.
TypeJournal Article (Paginated)
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e-Research and the Ubiquitious Open Grid Digital Libraries of the FuturePatkar, Vivek; Chandra, Smita (2006)Libraries have traditionally facilitated each of the following elements of research: production of new knowledge, its preservation and its organization to make it accessible for use over the generations. In modern times, the library is constantly required to meet the challenges of information explosion. Assimilating resources and restructuring practices to process the large data volumes both in the print and digital form held across the globe, therefore, becomes very important. A recourse by the libraries to application of successive forms of what can be called as Digital Library Technologies (DLT) has been the imperative. The Open Archives Initiative (OAI) is one recent development that is expected to assist the libraries to partner in setting up virtual learning environment and integrating research on a near universal scale. Future extension of this concept is envisaged to be that of Grid Computing. The technologies driving the â Gridâ would let people share computing power, databases, and other on-line tools securely across institutional and geographic boundaries without sacrificing the local autonomy. Ushering an era of the ubiquitous library helping the e-research is thus on the card. This paper reviews the emerging technological changes and charts the future role for the libraries with special reference to India.
Cyberspace or Face-to-Face: The Teachable Moment and Changing Reference MediumsDesai, Christina M.; Graves, Stephanie J. (2007-03)This article considers the teaching role of reference librarians by studying the teachable moment in reference transactions, and usersâ response to that instruction. An empirical study of instruction was conducted in both virtual and traditional reference milieus, examining three services: IM (Instant Messaging), chat, and face-to-face reference. The authors used the same criteria in separate studies of all three to determine if librarians provided analogous levels of instruction and what factors influenced the likelihood of instruction. Methodology employed transcript analysis, observation, and patron surveys. Findings indicated that patrons wanted instruction in their reference transactions, regardless of medium, and librarians provided it. However, instructional techniques used by librarians in virtual reference differ somewhat from those used at the reference desk. The authors conclude that reference transactions, in any medium, represent the patronsâ point-of-need, thereby presenting the ideal teachable moment.
The Shift from Information Retrieval to Information SynthesisBlake, Catherine; Anderson, Caryn (2005)Grand challenges such as public health, security, genomics, environmental protection, education, and economics, are characterized by complexity, interdependence, globalization, and unpredictability. Although the unprecedented quantity of information surrounding these challenges can provide users with a new perspective on solutions, the data surrounding complex systems vary with respect to levels of structure and authority, and include vastly different contexts and vocabularies. To be successful in this domain we must extend our models of information science such that they operate successfully in environments where the quantity of relevant information far exceeds our human processing capacity. For example, the well-accepted precision and recall metrics break down when hundreds of thousands of documents are relevant. Solutions to grand challenges require that information scientists shift their focus from information retrieval towards information synthesis.