Stream reasoning and complex event processing in ETALIS

Addressing dynamics and notifications in the Semantic Web realm has recently become an important area of research. Run time data is continuously generated by multiple social networks, sensor networks, various on-line services and so forth. How to get advantage of this continuously arriving data events remains a challenge --that is, how to integrate heterogeneous event streams, combine them with background knowledge e.g., an ontology, and perform event processing and stream reasoning. In this paper we describe ETALIS --a system which enables specification and monitoring of changes in near real time. Changes can be specified as complex event patterns, and ETALIS can detect them in real time. Moreover the system can perform reasoning over streaming events with respect to background knowledge. ETALIS implements two languages for specification of event patterns: ETALIS Language for Events, and Event Processing SPARQL. ETALIS has various applicabilities in capturing changes in semantic networks, broadcasting notifications to interested parties, and creating further changes based on processing of the temporal, static, or slowly evolving knowledge.

[1]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.

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

[3]  Sharma Chakravarthy,et al.  SnoopIB: Interval-based event specification and detection for active databases , 2003, Data Knowl. Eng..

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

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

[6]  Sharma Chakravarthy,et al.  Composite Events for Active Databases: Semantics, Contexts and Detection , 1994, VLDB.

[7]  Jonathan Goldstein,et al.  Consistent Streaming Through Time: A Vision for Event Stream Processing , 2006, CIDR.

[8]  Opher Etzion,et al.  Event Processing in Action , 2010 .

[9]  Frederick Reiss,et al.  TelegraphCQ: Continuous Dataflow Processing for an Uncertain World , 2003, CIDR.

[10]  Sebastian Rudolph,et al.  A Rule-Based Language for Complex Event Processing and Reasoning , 2010, RR.

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

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

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

[14]  Onkar B. Walavalkar,et al.  Streaming Knowledge Bases , 2007 .

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

[16]  JÜRGEN KRÄMER,et al.  Semantics and implementation of continuous sliding window queries over data streams , 2009, TODS.