雲端環境上的複合式知識融合與推論

張景棠

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Basic & Applied Sciences > Information Science
College of Electrical Engineering & Computer Science > Department of Information Engineering and Computer Science

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雲端環境上的複合式知識融合與推論

Hybrid Knowledge Fusion and Inference on Cloud Environment

張景棠 , Masters　　Advisor：張玉山;戴志華　　

英文

知識融合；知識推論；機率模型； Knowledge fusion ； Knowledge inference ； Probabilistic model

Abstract │ Reference (115) │ Altmetrics

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In the age of big data, an incredible amount of knowledge is produced everywhere everyday through various ways.
Knowledge fusion and inference thus has become an important issue for better utilization of knowledge.
In this paper, we focus on the hybrid knowledge that can be represented in the form of association rules (with categorical and/or numerical information), and address the problem of such hybrid knowledge fusion and inference on cloud environment.
In light of the use of the problem, we specify six issues of knowledge fusion and inference and propose a HyKFICE (Hybrid Knowledge Fusion and Inference on Cloud Environment) system as an effective solution. HyKFICE is capable of inferring the possibilities of the happening of events at a given condition through knowledge fusion and inference based on the probability theory.
HyKFICE can also perform the computation in parallel on clouds by grouping and summing up similar knowledge in 3-layer bipartite graphs.
Experiments conducted on real data sets demonstrate the efficiency of HyKFICE and show that
it is not only the amount of knowledge but also the associations between knowledge dominating the execution time of the hybrid knowledge fusion and inference.

Reference ( 115 ) 〈TOP〉

[2] Martin Hilbert. Big data for development: From information-to knowledge societies.
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[3] Molly Engle. Qualitative data analysis: An expanded sourcebook (2nd ed.). The American
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knowledge discovery in databases. AI Magazine, 17(3):37–54, 1996.
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[6] Karine Zeitouni. A survey of spatial data mining methods databases and statistics point of
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[7] Thiago Christiano Silva and Liang Zhao. Machine Learning in Complex Networks.
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