Knowledge discovery objects and queries in Distributed Knowledge Systems

Author(s):  
Zbigniew W. RaŚ ◽  
Jiyun Zheng
Author(s):  
О. Z. Mintser ◽  
О. Ye. Stryzhak ◽  
S. V. Denysenko

<p class="a0">Approaches, facilities and technologies of forming of the personalized electronic grounds of management in the educational- informative environment knowledge are described. The ontological aspects of model scenario construction in doctor’s education post-graduate training accompaniment are considered with the use of the network systems of knowledge. It supposes the decision of increasing of efficiency medical educating of doctors on application of modern network technologies of the distance access to t h e distributed knowledge systems.</p>


Author(s):  
Antonio Congiusta ◽  
Domenico Talia ◽  
Paolo Trunfio

Knowledge discovery is a compute and data intensive process that allows for finding patterns, trends, and models in large datasets. The Grid can be effectively exploited for deploying knowledge discovery applications because of the high-performance it can offer and its distributed infrastructure. For effective use of Grids in knowledge discovery, the development of middleware is critical to support data management, data transfer, data mining and knowledge representation. To such purpose, we designed the Knowledge Grid, a high-level environment providing for Grid-based knowledge discovery tools and services. Such services allow users to create and manage complex knowledge discovery applications, composed as workflows that integrate data sources and data mining tools provided as distributed Grid services. This chapter describes the Knowledge Grid architecture and describes how its components can be used to design and implement distributed knowledge discovery applications. Then, the chapter describes how the Knowledge Grid services can be made accessible using the Open Grid Services Architecture (OGSA) model.


Author(s):  
Andrew J. Cowell ◽  
Alan R. Chappell ◽  
David A. Thurmanb

Battelle is working in partnership with Stanford University's Knowledge Systems Laboratory (KSL) and IBM's T.J. Watson Research Center to develop a suite of technologies for knowledge discovery, knowledge extraction, knowledge representation, automated reasoning, and human information interaction, in unison entitled “Knowledge Associates for Novel Intelligence” (KANI). We have developed an integrated analytic environment composed of a collection of analyst associates, software components that aid the analyst at different stages of the analytical process. In this paper, we discuss our efforts in the research, design and implementation of the question answering elements of the Information Interaction Associate. Specifically, we focus on the techniques employed to produce an effective user interface to these elements. In addition, we touch upon the methodologies we intend to use to empirically evaluate our approach with active intelligence analysts.


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