Challenges and future trends in querying semantic web data streams

Sven Groppe, Jinghua Groppe*

*Corresponding author for this work

Abstract

The Semantic Web provides the simple but powful machnisms to describe information in a machine-understandable way and to enable automatic inference of new knowledge. These Semantic Web techniques are being widely applied in complex real-world applications, and more and more data, including data streams, are described using the Semantic Web technology. Streaming query engines evaluate queries on streams of data, and can discard irrelevant input earilier, build indices only on the data needed for the evaluation of the query, and determine partial results of a query as soon as they are available, in order to save processing costs and space costs, and to enable more efficient processing of queries. As well as querying the given data, querying Semantic Web data streams is involved with inferable data, which are not contained in the data streams. While the optimization techniques for querying data streams has been well studied, the optimization techniques for querying Semantic Web data streams is still an open research topic. We will address its challenges and future trends in this contribution.

Original languageEnglish
Title of host publicationData Mining and Management
Number of pages15
PublisherNova Science Publishers, Inc.
Publication date01.12.2010
Pages225-239
ISBN (Print)9781607412892
Publication statusPublished - 01.12.2010

Research Areas and Centers

  • Research Area: Intelligent Systems
  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

  • 409-04 Operating, Communication, Database and Distributed Systems

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