RSP-QL Semantics: A Unifying Query Model to Explain Heterogeneity of RDF Stream Processing Systems

RDF and SPARQL are established standards for data interchange and querying on the Web. While they have been shown to be useful and applicable in many scenarios, they are not sufficiently adequate for dealing with streams of data and their intrinsic continuous nature. In the last years data and query languages have been proposed to extend both RDF and SPARQL for streams and continuous processing, under the name of RDF Stream Processing-RSP. These efforts resulted in several models and implementations that, at a first look, appear to propose alternative syntaxes but equivalent semantics. However, when asked to continuously answer the same queries on the same data streams, they provide different answers at disparate moments due to the heterogeneity of their operational semantics. These discrepancies render the process of understanding and comparing continuous query results complex and misleading. In this work, the authors propose RSP-QL, a comprehensive model that formally defines the semantics of an RSP system. RSP-QL makes explicit the hidden assumptions of currently available RSP systems, allows defining a formal notion of correctness for RSP query results and, thus, explains why available implementations provide different answers at disparate moments.

[1]  Amit P. Sheth,et al.  The SSN ontology of the W3C semantic sensor network incubator group , 2012, J. Web Semant..

[2]  Óscar Corcho,et al.  On Correctness in RDF Stream Processor Benchmarking , 2013, International Semantic Web Conference.

[3]  Vldb Endowment,et al.  The VLDB journal : the international journal on very large data bases. , 1992 .

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

[5]  Michael Stonebraker,et al.  Linear Road: A Stream Data Management Benchmark , 2004, VLDB.

[6]  Miltiadis D. Lytras,et al.  Semantic Web-Based Information Systems: State-of-the-Art Applications , 2006 .

[7]  Way Kuo,et al.  Reliability, Yield, And Stress Burn-In , 1998 .

[8]  Karl Aberer,et al.  Enabling Query Technologies for the Semantic Sensor Web , 2012, Int. J. Semantic Web Inf. Syst..

[9]  Alessandro Margara,et al.  Processing flows of information: From data stream to complex event processing , 2012, CSUR.

[10]  V. S. Subrahmanian,et al.  Scaling RDF with Time , 2008, WWW.

[11]  Way Kuo,et al.  Reliability, Yield, and Stress Burn-In: A Unified Approach for Microelectronics Systems Manufacturing & Software Development , 2014 .

[12]  Marcelo Arenas,et al.  Semantics and Complexity of SPARQL , 2006, International Semantic Web Conference.

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

[14]  Boris Motik,et al.  Representing and querying validity time in RDF and OWL: A logic-based approach , 2010, J. Web Semant..

[15]  A. Sheth International Journal on Semantic Web & Information Systems , .

[16]  Dieter Fensel,et al.  Sparkwave: continuous schema-enhanced pattern matching over RDF data streams , 2012, DEBS.

[17]  Olena Kaykova,et al.  Enabling Interoperability for Industrial Web Resources , 2005 .

[18]  Claudio Gutiérrez,et al.  Temporal RDF , 2005, ESWC.

[19]  Jennifer Widom,et al.  Continuous queries over data streams , 2001, SGMD.

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

[21]  Emanuele Della Valle,et al.  On the need to include functional testing in RDF stream engine benchmarks , 2013 .

[22]  Frank van Harmelen,et al.  DynamiTE: Parallel Materialization of Dynamic RDF Data , 2013, SEMWEB.

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

[24]  Laura M. Haas,et al.  SECRET: A Model for Analysis of the Execution Semantics of Stream Processing Systems , 2010, Proc. VLDB Endow..

[25]  T. Andrew Yang,et al.  Online Human Activity Networks (OnHANs): An Analysis Based on Activity Theory , 2010 .

[26]  Ying Zhang,et al.  SRBench: A Streaming RDF/SPARQL Benchmark , 2012, SEMWEB.

[27]  Daniele Braga,et al.  C-SPARQL: a Continuous Query Language for RDF Data Streams , 2010, Int. J. Semantic Comput..

[28]  David J. DeWitt,et al.  NiagaraCQ: a scalable continuous query system for Internet databases , 2000, SIGMOD '00.