Towards Expressive Stream Reasoning

Stream Data processing has become a popular topic in database research addressing the challenge of efficiently answering queries over continuous data streams. Meanwhile data streams have become more and more important as a basis for higher level decision processes that require complex reasoning over data streams and rich background knowledge. In previous work the foundation for complex reasoning over streams and background knowledge was laid by introducing technologies for wrapping and querying streams in the RDF data format and by supporting simple forms of reasoning in terms of incremental view maintenance. In this paper, we discuss how this existing technologies should be extended toward richer forms of reasoning using Sensor Networks as a motivating example.

[1]  Peter Gärdenfors,et al.  Belief Revision , 1995 .

[2]  Sudipto Guha,et al.  Dynamic multidimensional histograms , 2002, SIGMOD '02.

[3]  Daniele Braga,et al.  An execution environment for C-SPARQL queries , 2010, EDBT '10.

[4]  Yue Zhuge,et al.  Graph structured views and their incremental maintenance , 1998, Proceedings 14th International Conference on Data Engineering.

[5]  Ian Horrocks,et al.  Description logic programs: combining logic programs with description logic , 2003, WWW '03.

[6]  Amit P. Sheth,et al.  Semantic Sensor Web , 2008, IEEE Internet Computing.

[7]  Jennifer Widom,et al.  The CQL continuous query language: semantic foundations and query execution , 2006, The VLDB Journal.

[8]  Kyuseok Shim,et al.  Approximate query processing using wavelets , 2001, The VLDB Journal.

[9]  Bernardo Cuenca Grau,et al.  History Matters: Incremental Ontology Reasoning Using Modules , 2007, ISWC/ASWC.

[10]  Daniele Braga,et al.  C-SPARQL: SPARQL for continuous querying , 2009, WWW '09.

[11]  B. Parsia,et al.  Towards Incremental Reasoning Through Updates in OWL-DL , 2006 .

[12]  Bruno Berstel,et al.  Extending the RETE algorithm for event management , 2002, Proceedings Ninth International Symposium on Temporal Representation and Reasoning.

[13]  Daniele Braga,et al.  Incremental Reasoning on Streams and Rich Background Knowledge , 2010, ESWC.

[14]  Michael Stonebraker,et al.  Load Shedding in a Data Stream Manager , 2003, VLDB.

[15]  Heiner Stuckenschmidt,et al.  Distributed Resolution for Expressive Ontology Networks , 2009, RR.

[16]  Burton H. Bloom,et al.  Space/time trade-offs in hash coding with allowable errors , 1970, CACM.

[17]  Daniele Braga,et al.  Continuous Queries and Real-time Analysis of Social Semantic Data with C-SPARQL , 2009 .

[18]  Eduardo L. Fermé,et al.  Belief Revision , 2007, Inteligencia Artif..

[19]  Alessandro Campi,et al.  A First Step Towards Stream Reasoning , 2009, FIS.

[20]  E. Prud hommeaux,et al.  SPARQL query language for RDF , 2011 .

[21]  Frank van Harmelen,et al.  Scalable Distributed Reasoning Using MapReduce , 2009, SEMWEB.

[22]  Dieter Fensel,et al.  Towards LarKC: A Platform for Web-Scale Reasoning , 2008, 2008 IEEE International Conference on Semantic Computing.

[23]  Andre Bolles,et al.  Streaming SPARQL - Extending SPARQL to Process Data Streams , 2008, ESWC.

[24]  Mireille Régnier,et al.  An Adaptive Algorithm for Incremental Evaluation of Production Rules in Databases , 1993, VLDB.

[25]  Dieter Fensel,et al.  It's a Streaming World! Reasoning upon Rapidly Changing Information , 2009, IEEE Intelligent Systems.

[26]  Jennifer Widom,et al.  Models and issues in data stream systems , 2002, PODS.

[27]  Elke A. Rundensteiner,et al.  Incremental Maintenance of Materialized Object-Oriented Views in MultiView: Strategies and Performance Evaluation , 1998, IEEE Trans. Knowl. Data Eng..

[28]  James D. Myers,et al.  Semantic Management of Streaming Data , 2009, SSN.

[29]  Steffen Staab,et al.  Incrementally Maintaining Materializations of Ontologies Stored in Logic Databases , 2005, J. Data Semant..

[30]  Manolis Koubarakis,et al.  RDFS Reasoning and Query Answering on Top of DHTs , 2008, SEMWEB.