Pattern Matching Over Linked Data Streams

This chapter leverages semantic technologies, such as Linked Data, which can facilitate machine-to-machine (M2M) communications to build an efficient information dissemination system for semantic IoT. The system integrates Linked Data streams generated from various data collectors and disseminates matched data to relevant data consumers based on triple pattern queries registered in the system by the consumers. We also design two new data structures, TP-automata and CTP-automata, to meet the high performance needs of Linked Data dissemination. We evaluate our system using a real-world dataset generated from a Smart Building Project. With the new data structures, the proposed system can disseminate Linked Data faster than the existing approach with thousands of registered queries.

[1]  Edward Curry,et al.  Thematic event processing , 2014, Middleware.

[2]  Athanasios V. Vasilakos,et al.  When things matter: A survey on data-centric internet of things , 2016, J. Netw. Comput. Appl..

[3]  Danh Le Phuoc,et al.  A Native and Adaptive Approach for Unified Processing of Linked Streams and Linked Data , 2011, SEMWEB.

[4]  Neil Immerman,et al.  Efficient pattern matching over event streams , 2008, SIGMOD Conference.

[5]  Jürgen Umbrich,et al.  Data summaries for on-demand queries over linked data , 2010, WWW '10.

[6]  Amit P. Sheth,et al.  From Data to Actionable Knowledge: Big Data Challenges in the Web of Things , 2013, IEEE Intell. Syst..

[7]  Edward Curry,et al.  Approximate Semantic Matching of Events for the Internet of Things , 2014, ACM Trans. Internet Techn..

[8]  Marco Gruteser,et al.  ParkNet: drive-by sensing of road-side parking statistics , 2010, MobiSys '10.

[9]  Edward Curry,et al.  Enterprise energy management using a linked dataspace for Energy Intelligence , 2012, 2012 Sustainable Internet and ICT for Sustainability (SustainIT).

[10]  Maria-Esther Vidal,et al.  Efficiently Joining Group Patterns in SPARQL Queries , 2010, ESWC.

[11]  Manolis Koubarakis,et al.  Evaluating Conjunctive Triple Pattern Queries over Large Structured Overlay Networks , 2006, SEMWEB.

[12]  Quan Z. Sheng,et al.  Matching Over Linked Data Streams in the Internet of Things , 2015, IEEE Internet Computing.

[13]  HyeongSik Kim,et al.  An Intermediate Algebra for Optimizing RDF Graph Pattern Matching on MapReduce , 2011, ESWC.

[14]  Sebastian Rudolph,et al.  EP-SPARQL: a unified language for event processing and stream reasoning , 2011, WWW.

[15]  Gunter Saake,et al.  Research Directions in Database Architectures for the Internet of Things: A Communication of the First International Workshop on Database Architectures for the Internet of Things (DAIT 2009) , 2009, BNCOD.

[16]  Kerry L. Taylor,et al.  Semantics for the Internet of Things: Early Progress and Back to the Future , 2019 .

[17]  Leonidas J. Guibas,et al.  Distributed resource management and matching in sensor networks , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[18]  George H. L. Fletcher,et al.  Scalable indexing of RDF graphs for efficient join processing , 2009, CIKM.

[19]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[20]  Quan Z. Sheng,et al.  Towards Efficient Dissemination of Linked Data in the Internet of Things , 2014, CIKM.

[21]  Matthias Weidlich,et al.  Event Recognition Challenges and Techniques , 2014, ACM Trans. Internet Techn..

[22]  Hao Zhang,et al.  Path sharing and predicate evaluation for high-performance XML filtering , 2003, TODS.