Leslie L. Miller

Organizational Memory Management: Technological and Research Issues

As new information technologies are available to the design and implementation of organizational memory and as organizations continue to automate their business processes and collect explosive amounts of data, researchers in knowledge management need to confront new opportunities and new challenges. Some of the core issues of organizational memory management include organizational context, retention structure, knowledge taxonomy, ontology, organizational learning, and so forth. In this article, we review the literature of organizational memory management. We further present a basic framework of the technological components and their applications in organizational memory systems and discuss a number of important research issues.

Intelligent mobile agents for information retrieval and knowledge discovery from distributed data and knowledge sources

Tools for selective proactive as well as reactive information retrieval and knowledge discovery constitute some of the key enabling technologies for managing the data overload and translating recent advances in automated data acquisition, digital storage, computers and communications into fundamental advances in decision support, scientific discovery and related applications. The paper describes an implementation of intelligent, customizable mobile software agents for information retrieval and knowledge discovery from distributed data sources. These tools are part of the distributed knowledge network (DKN) toolbox that is being developed at the Iowa State Universitys Artificial Intelligence Laboratory. Experiments with retrieval of journal paper abstracts demonstrate the feasibility of using machine learning to design mobile intelligent agents for customized information retrieval. A similar approach has been successfully employed for knowledge discovery (using machine learning) from distributed data collections.

Parallel Architectures for Database Systems

Publisher Summary This chapter examines the impact of current technology on the design of special-purpose database machines (DBMs) and provides a survey of the DBMs that focuses on the adaptability of the designs to the current technology. The computer architectures fall into four groups—namely, single instruction stream-single data stream (SISD), single instruction stream-multiple data stream (SIMD), multiple instruction stream-single data stream (MISD), and multiple instruction stream-multiple data stream (MIMD). Database computer (DBC) uses two forms of parallelism: an entire cylinder is processed in parallel and the system performs queries in a pipeline fashion by separate units around two rings. The systems in high very large scale integration (VLSI)-compatible database machines are designed based on the constraints imposed by technology. In general, they are highly parallel with regular and simple architectures. The future database machine designers ought to concentrate on two important issues: (1) the investigation of the effect and benefit of a specialized database operating system on their DBM designs and (2) the optimization of the system throughput rather than improving the response time of a single request.

Design of data warehouses using metadata

Abstract Data warehouses have become an instant phenomenon in many large organizations that deal with massive amounts of information. Drawing on the experiences from the systems development field, we surmise that an effective design tool will enhance the success of warehouse implementations. Thus, we present a CASE tool designed to generate the SQL queries necessary to build a warehouse from a set of operational relational databases. The warehouse designer simply specifies a list of attribute names that will appear in the warehouse, conditions if any are desired, and a description of the operational databases. The tool returns the queries needed to populate the warehouse table.

Parallel sorting algorithms

Publisher Summary This chapter presents a survey on various parallel sorting algorithms. Sorting is a nontrivial problem and has widespread commercial and business applications. Serial algorithms for sorting have been available since the days of punched-card machines. At present, there is a considerable body of literature on serial sorting algorithms. Parallel algorithms for sorting are of a recent origin and came into existence over the past decade. The chapter presents a unified treatment of various parallel sorting algorithms by bringing out clearly the relation between the architecture of parallel computers and the structure of algorithms. In the design of parallel algorithms in general, and of parallel sorting algorithms in particular, two models have been widely used: (1) models based on fixed interconnection networks such as the same or single instruction on multiple data (SIMD) machine mesh-connected network and (2) models based on a global memory, which is shared by various processors. The special-purpose network-sorting algorithms are described. Algorithms for SIMD machines are given.

METNET: SYSTEMS BIOLOGY TOOLS FOR ARABIDOPSIS

platform features graph visualization, interactive displays, graph theoretic computations for determining biological distances, a unique multivariate display and statistical analysis tool, graph modeling using the open source statistical analysis language, R, and versatile text mining. The use of these tools is

Geospatial IT for mobile field data collection

Federal statistical agencies generate critical data about the nations population, economy, and natural resources. This data is gathered largely by mobile field data collection. Although geospatial information is an essential reference material in the field and serves as a base for recording spatially linked data, it is nearly always used in printed form due to limitations in mobile computing systems and tools for handling geospatial data. The ability to interact with digital geospatial data in the field offers significant enhancements for data quality and operational efficiencies. However, basic research is needed on extensible infrastructure designs for limited field computing environments and appropriate field tools for mobile data gatherers.

Object-oriented data warehouse for information fusion from heterogeneous distributed data and knowledge sources

Tools for accessing, transforming, organizing and assimilating data and knowledge from multiple heterogeneous, distributed data and knowledge sources in a form that is suitable for further analysis constitute some of the key enabling technologies for translating recent advances in automated data acquisition, digital storage, computers and communications into fundamental advances in organizational decision support, data analysis, and related applications. The design, implementation, and evaluation of a data warehouse using object-oriented views and mediator agents for bridging the mismatch between data sources and clients has been a major focus of research as part of the Distributed Knowledge Networks Project at the Iowa State University Artificial Intelligence Research Laboratory over the past two years. This paper describes the design, implementation, and population of a data warehouse component of distributed knowledge networks.

Organizational decision support systems: the design and implementation of a data extraction scheme to facilitate model-database communication

Abstract The concept of organizational decision support and the criteria for designing such support systems (ODSS) have recently received a great deal of attention. In spite of major differences among the decision processes at the organizational, group, and individual levels, the requirements for securing data from the corporate database in an ODSS still remains a significant issue. In addition, the computer and communication technologies that integrate the subsystems of knowledge, data, and models assume increased relevance in the ODSS. Indeed, the richness of problems of making the most of such technologies becomes even more important in the ODSS environment. In the present work, the issues of integrating models and databases through the use of a data extraction scheme are examined. In particular, certain aspects of the query generation algorithms that allow the user or model to view the database as a single relation are described. The generated query is then translated into TQUEL, resulting in a temporal query reflecting the historical nature of the corporate database.

Design of organizational decision support systems: the use of a data extraction scheme to facilitate model-database communication

The concept of organizational decision support and the criteria for designing such support systems (ODSS) have recently received a great deal of attention. While the organizational decision processes differ considerably from individual and group decision making, the requirements for securing data from the corporate database in an ODSS are not much different. The computer and communication technologies that integrate the subsystems of knowledge, data, and models are still essential for ODSS. The authors examine the issues of integrating models and databases through the use of a data extraction scheme. In particular, they describe certain aspects of the query generation algorithms that allow the user or model to view the database as a single relation. The generated query is then translated into TQUEL, a temporal query language reflecting the historical nature of corporate databases.<<ETX>>