C-SPARQL: SPARQL for Continuous Querying
WHY
“Is a traffic jam going to happen in this highway? And is then convenient to reallocate travelers based upon the forecast?” “By looking at the click stream coming from a given IP, can we notice the shifts of interest of the person behind the computer?” “Which contents of the news Web portal are attracting more attention? Which navigation pattern would lead readers to other news related to those contents?” “Are trends in medical records indicative of any new disease spreading in given parts of the world?” “Where are all my friends meeting?” “In the financial context, can we detect any intraday correlation clusters among stock exchange?” Although the information is often available, there’s no software system capable of computing the answers - indeed, no system enables users even to issue such queries.
See also Stream Reasoning Use Cases Days.
WHAT
C-SPARQL is an extension of SPARQL whose distinguishing feature is the support of continuous queries, i.e. queries registered over RDF data streams and then continuously executed. Queries consider windows, i.e. the most recent triples of such streams, observed while data is continuously flowing. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with evolving knowledge over time.
LarKC Plug-ins
As we proposed in A Proposal for Publishing Data Streams as Linked Data at LDOW 2010, the results of a C-SPARQL query can be published as Linked Data thanks to a Streaming Linked Data server. We have been leveraging such a Linked Data interface of our C-SPARQL Engine to realize three plug-ins for the LarKC 2.5 platform:
RDFStreamTransformer, and
In the recent months we have evolved out Streasming Linked Data proposal and we have realized a new plugin for LarKC 2.5 that connects to a remote Streaming Linked Data Server instead on using a local installation of the C-SPARQL Engine:
PUBLICATIONS
- Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus. Incremental Reasoning on Streams and Rich Background Knowledge. In. 7th Extended Semantic Web Conference (ESWC 2010) (TO APPEAR)
Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus. C-SPARQL: A Continuous Query Language for RDF Data Streams. International Journal of Semantic Computing (IJSC), 2010, World Scientific Publishing (TO APPEAR)
Davide Francesco Barbieri, Daniele Braga, Stefano Ceri and Michael Grossniklaus. An Execution Environment for C-SPARQL Queries. EDBT 2010
Emanuele Della Valle, Stefano Ceri, Frank van Harmelen, Dieter Fensel It's a Streaming World! Reasoning upon Rapidly Changing Information. IEEE Intelligent Systems 24(6): 83-89 (2009) bibtex
Davide Francesco Barbieri, Daniele Braga, Stefano Ceri and Emanuele Della Valle and Michael Grossniklaus, Continuous Queries and Real-time Analysis of Social Semantic Data with C-SPARQL, in SDoW 2009 Colocated with ISWC 2009. bibtex
Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus: C-SPARQL: SPARQL for continuous querying. WWW 2009: 1061-1062 bibtex
Emanuele Della Valle, Stefano Ceri, Davide Francesco Barbieri, Daniele Braga, Alessandro Campi: A First Step Towards Stream Reasoning. FIS 2008: 72-81 bibtex