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Knowledge Grid (knowledge + grid)

Distribution by Scientific Domains

Information Science and Computing	100%

Selected Abstracts

Distributed end-host multicast algorithms for the Knowledge Grid

CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 15 2007
Wanqing Tu
Abstract The Knowledge Grid built on top of the peer-to-peer (P2P) network has been studied to implement scalable, available and sematic-based querying. In order to improve the efficiency and scalability of querying, this paper studies the problem of multicasting queries in the Knowledge Grid. An m -dimensional irregular mesh is a popular overlay topology of P2P networks. We present a set of novel distributed algorithms on top of an m -dimensional irregular mesh overlay for the short delay and low network resource consumption end-host multicast services. Our end-host multicast fully utilizes the advantages of an m -dimensional mesh to construct a two-layer architecture. Compared to previous approaches, the novelty and contribution here are: (1) cluster formation that partitions the group members into clusters in the lower layer where cluster consists of a small number of members; (2) cluster core selection that searches a core with the minimum sum of overlay hops to all other cluster members for each cluster; (3) weighted shortest path tree construction that guarantees the minimum number of shortest paths to be occupied by the multicast traffic; (4) distributed multicast routing that directs the multicast messages to be efficiently distributed along the two-layer multicast architecture in parallel, without a global control; the routing scheme enables the packets to be transmitted to the remote end hosts within short delays through some common shortest paths; and (5) multicast path maintenance that restores the normal communication once the membership alteration appears. Simulation results show that our end-host multicast can distributively achieve a shorter delay and lower network resource consumption multicast services as compared with some well-known end-host multicast systems. Copyright © 2006 John Wiley & Sons, Ltd. [source]

BUILDING A DATA-MINING GRID FOR MULTIPLE HUMAN BRAIN DATA ANALYSIS

COMPUTATIONAL INTELLIGENCE, Issue 2 2005
Ning Zhong
E-science is about global collaboration in key areas of science such as cognitive science and brain science, and the next generation of infrastructure such as the Wisdom Web and Knowledge Grids. As a case study, we investigate human multiperception mechanism by cooperatively using various psychological experiments, physiological measurements, and data mining techniques for developing artificial systems which match human ability in specific aspects. In particular, we observe fMRI (functional magnetic resonance imaging) and EEG (electroencephalogram) brain activations from the viewpoint of peculiarity oriented mining and propose a way of peculiarity oriented mining for knowledge discovery in multiple human brain data. Based on such experience and needs, we concentrate on the architectural aspect of a brain-informatics portal from the perspective of the Wisdom Web and Knowledge Grids. We describe how to build a data-mining grid on the Wisdom Web for multiaspect human brain data analysis. The proposed methodology attempts to change the perspective of cognitive scientists from a single type of experimental data analysis toward a holistic view at a long-term, global field of vision. [source]

Portal-based Knowledge Environment for Collaborative Science

CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 12 2007
Karen Schuchardt
Abstract The Knowledge Environment for Collaborative Science (KnECS) is an open-source informatics toolkit designed to enable knowledge Grids that interconnect science communities, unique facilities, data, and tools. KnECS features a Web portal with team and data collaboration tools, lightweight federation of data, provenance tracking, and multi-level support for application integration. We identify the capabilities of KnECS and discuss extensions from the Collaboratory for Multi-Scale Chemical Sciences (CMCS) which enable diverse combustion science communities to create and share verified, documented data sets and reference data, thereby demonstrating new methods of community interaction and data interoperability required by systems science approaches. Finally, we summarize the challenges we encountered and foresee for knowledge environments. Copyright © 2007 John Wiley & Sons, Ltd. [source]