POL: A Pattern Oriented Load-Shedding for Semantic Data Stream Processing

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• 关键词：BigData ; Semantic data stream ; Graph patterns detection ; Load ; shedding
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10042
• 期：1
• 页码：157-171
• 全文大小：780 KB
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• 作者单位：Fethi Belghaouti (19)
  Amel Bouzeghoub (19)
  Zakia Kazi-Aoul (20)
  Raja Chiky (20)
  
  19. SAMOVAR, Telecom SudParis, CNRS, Universite Paris-Saclay, 9 rue Charles Fourier, 91011, Evry Cedex, France
  20. Institut Superieur d’Electronique de Paris, 28 rue Notre-Dame des Champs, 75006, Paris, France
  
• 丛书名：Web Information Systems Engineering ¨C WISE 2016
• ISBN：978-3-319-48743-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10042

文摘

Nowadays, high volumes of data are generated and published at a very high velocity, producing heterogeneous data streams. This has led researchers to propose new systems named RDF Stream Processors (RSP), to deal with this new kind of streams. Unfortunately, these systems are fallible when their maximum supported speed is reached especially in a limited system resources environment. To overcome these problems, recent efforts have been made in the field. Some of them decrease the volume of RDF data streams using compression or load-shedding techniques, mostly according to a probabilistic approach. In this paper we propose POL: a Pattern Oriented approach to Load-shed data from RDF streams based on a deterministic approach. As a pre-processing task through a unique pass, the approach extracts the exact needed semantic data from the stream. The conducted experiments on public available datasets have demonstrated the effectiveness of our approach.