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Titlebook: Journal on Data Semantics II; Stefano Spaccapietra,Elisa Bertino,Leon Strous Book 2005 Springer-Verlag Berlin Heidelberg 2005 DOM.artifici

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書目名稱Journal on Data Semantics II
編輯Stefano Spaccapietra,Elisa Bertino,Leon Strous
視頻videohttp://file.papertrans.cn/502/501236/501236.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Journal on Data Semantics II;  Stefano Spaccapietra,Elisa Bertino,Leon Strous Book 2005 Springer-Verlag Berlin Heidelberg 2005 DOM.artifici
描述.The LNCS Journal on Data Semantics is devoted to the presentation of notable work that, in one way or another, addresses research and development on issues related to data semantics. Based on the highly visible publication platform Lecture Notes in Computer Science, this new journal is widely disseminated and available worldwide. ..The scope of the journal ranges from theories supporting the formal definition of semantic content to innovative domain-specific applications of semantic knowledge. The journal addresses researchers and advanced practitioners working on the semantic web, interoperability, mobile information services, data warehousing, knowledge representation and reasoning, conceptual database modeling, ontologies, and artificial intelligence..
出版日期Book 2005
關(guān)鍵詞DOM; artificial intelligence; data warehouse; database; intelligence; knowledge representation; ontology; s
版次1
doihttps://doi.org/10.1007/b104761
isbn_softcover978-3-540-24208-6
isbn_ebook978-3-540-30567-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2005
The information of publication is updating

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Minsoo Lee,Stanley Y. W. Su,Herman Laminite state space, even if the state space of the real Erlang system is infinite. In this paper we illustrate this by presenting a case-study based on a piece of software in Ericsson’s AXD 301 switch, which implements a distributed resource locker algorithm. Some of the key properties we proved are
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Dynamic Pattern Mining: An Incremental Data Clustering Approach news events, and news topics) while reducing the amount of computations by maintaining cluster structure incrementally. In addition, to overcome the lack of topical relations in conceptual ontologies, we propose a topic ontology learning framework that utilizes the obtained document hierarchy. Expe
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