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The incremental adoption of electronic media in U.S. health care has created increased risk of security and privacy violations in provider organizations. Protective regulatory efforts have been proposed to address ineffective security of patient information, with severe noncompliance penalties. Using data from a nationwide survey of health information managers, this study examines how industry-wide knowledge management trends may influence the degree of security program adoption in health-care organizations. Results suggest that significant nonadoption of mandated security measures continues to occur across the health-care industry. Paper-based systems still prevail, and computerized settings tend to have less security measures. Implications for document management and knowledge policy are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a generic library of problem-solving methods for scheduling applications. Although some attempts have been made in the past at developing the libraries of scheduling problem-solvers, these only provide limited coverage. Many lack generality, as they subscribe to a particular scheduling domain. Others simply implement a particular problem-solving technique, which may be applicable only to a subset of the space of scheduling problems. In addition, most of these libraries fail to provide the required degree of depth and precision. In our approach, we subscribe to the Task-Method-Domain-Application knowledge modeling framework which provides a structured organization for the different components of the library. At the task level, we construct a generic scheduling task ontology to formalize the space of scheduling problems. At the method level, we construct a generic problem-solving model of scheduling that generalizes from the variety of approaches to scheduling problem-solving, which can be found in the literature. The generic nature of this model is demonstrated by constructing seven methods for scheduling as an alternative specialization of the model. Finally, we validated our library on a number of applications to demonstrate its generic nature and effective support for developing scheduling applications. [ABSTRACT FROM AUTHOR]

Support Vector Machines (SVMs) have been adopted by many data mining and information-retrieval applications for learning a mining or query concept, and then retrieving the ‘top-k’ best matches to the concept. However, when the data set is large, naively scanning the entire data set to find the top matches is not scalable. In this work, we propose a kernel indexing strategy to substantially prune the search space and, thus, improve the performance of top-k queries. Our kernel indexer (KDX) takes advantage of the underlying geometric properties and quickly converges on an approximate set of top-k instances of interest. More importantly, once the kernel (e.g., Gaussian kernel) has been selected and the indexer has been constructed, the indexer can work with different kernel-parameter settings (e.g., γ and σ) without performance compromise. Through theoretical analysis and empirical studies on a wide variety of data sets, we demonstrate KDX to be very effective. An earlier version of this paper appeared in the 2005 SIAM International Conference on Data Mining [24]. This version differs from the previous submission in providing a detailed cost analysis under different scenarios, specifically designed to meet the varying needs of accuracy, speed, and space requirements, developing an approach for insertion and deletion of instances, presenting the specific computations as well as the geometric properties used in performing the same, and providing detailed algorithms for each of the operations necessary to create and use the index structure. [ABSTRACT FROM AUTHOR]

The article presents the changes that could lead to a smarter federal information technology (IT) operation in the U.S. It notes that the changes could easily improve IT service for both government employees and American citizens. It states that the first change was to increase the government's in-house technical expertise, and the second one is to increase the leveraging of the skills and operations. It also mentions that the main reason for the promulgation of the changes was the call of U.S. President Barack Obama.

Using the knowledge-based view of the firm as our theoretical underpinning, we study partnerships between industrial firms and university research centers (URCs) in order to identify facilitators of knowledge transfer, particularly focusing on how firms learn from URCs. Data for this study were obtained from senior-level executives from a random sample of one hundred and seventy-three firms located in the northeastern US. Results indicate that social connectedness, trust, URC technology transfer-intellectual property policies, technological relatedness and technological capability are significant facilitators of knowledge transfer. We also found that the type of knowledge transferred, i.e., explicit versus tacit, moderated these relationships. We conclude with implications for future research and R&D policy. [ABSTRACT FROM AUTHOR]