Stream Reasoning: Where we got so far. Oxford 2010.1.18Presentation Transcript
Stream Reasoning Where We Got So Far Oxford - 2010.1.18 http://streamreasoning.org Emanuele Della Valle DEI - Politecnico di Milano [email_address] http://emanueledellavalle.org Joint work with: Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, and Michael Grossniklaus
Retrospective and Conclusions
Motivation It ‘s a streaming World! [IEEE-IS2009]
Sensor networks, …
traffic engineering, …
social networking, …
financial markets, …
Running Example Real-Time Streams on the Web
Streams are appearing more and more often on the Web in sites that distribute and present information in real-time streams.
Checkout http://activitystrea.ms/ for a standard API
Oxford, 2011-1-18 Emanuele Della Valle - visit http://streamreasoning.org
Running Example Examples of Questions Users are Asking
Which topics have my close friends discussed in the last hour ?
Which book is my friend likely to read next ?
What impact have I been creating with my tweets in the last day ?
<query> … <time dimension> ?
Motivation Problem Statement
in real time
of gigantic and inevitably noisy data streams
in order to support the decision process of extremely large numbers of concurrent user
Background What are data streams anyway?
Data streams are unbounded sequences of time-varying data elements
an (almost) “continuous” flow of information
with the recent information being more relevant as it describes the current state of a dynamic system
time Oxford, 2011-1-18
Background Continuous Semantics
Processing data streams in the space of one-time semantics is difficult because of the very nature of the underlying data
Innovative * assumption: continuous semantics!
streams can be consumed on the fly rather than being stored forever and
queries are registered and continuously produce answers
* This innovation arose in DB community in ’90s
Background Stream Processing
Continuous queries registered over streams that are observed trough windows
Background knowledge do not change and it is fully materialized
Changes only take place in the window
An experiment comparing the time required to compute a new materialization using
Re-computing from scratch (i.e.,1250 ms in our setting)
State of the art incremental approach [Volz, 2005]
Results at increasing % of the materialization changed when the window slides
Achievements Comparative Evaluation 2/2
Comparison of the average time needed to answer a C-SPARQL query using
a forward reasoner,
the naive approach of re-computing the materialization
Retrospective and Conclusions Wrap Up
Syntax and semantics defined as a SPARQL extension
Engine implemented based on the decision to keep stream management and query evaluation separated
Experiments with C-SPARQL under simple RDF entailment regimes
window based selection of C-SPARQL outperforms the standard FILTER based selection
having formally defined C-SPARQL semantics algebraic optimizations are possible
Experiment with C-SPARQL under OWL-RL entailment regimes
efficient incremental updates of deductive closures investigated
our approach outperform state-of-the-art when updates comes as stream
Retrospective and Conclusions Achievements vs. Research Challenges
Relation with data-stream systems
Notion of RDF stream :-|
Query languages for semantic streams
Reasoning on Streams
Formal representations for stream reasoning
Notions of soundness and completeness
Efficient incremental updates of deductive closures
ESWC 2010 paper :-) ... but much more work is needed!
How to combine streams and background knowledge
ESWC 2010 paper :-| ... but a lot needs to be studied ...
Dealing with incomplete & noisy data
Distributed and parallel processing
[IEEE-IS2009] 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)
Continuous SPARQL (C-SPARQL)
[EDBT2010] Davide Francesco Barbieri, Daniele Braga, Stefano Ceri and Michael Grossniklaus. An Execution Environment for C-SPARQL Queries . EDBT 2010
[WWW2009] Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus: C-SPARQL: SPARQL for continuous querying . WWW 2009: 1061-1062
[IJSC2010] Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus: C-SPARQL: a Continuous Query Language for RDF Data Streams. Int. J. Semantic Computing 4(1): 3-25 (2010)
[IEEE-IS2010] Davide Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Yi Huang, Volker Tresp, Achim Rettinger, Hendrik Wermser, "Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics," IEEE Intelligent Systems, 30 Aug. 2010.
[ESWC2010] 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)
[Ceri1994] Stefano Ceri, Jennifer Widom: Deriving Incremental Production Rules for Deductive Data. Inf. Syst. 19(6): 467-490 (1994)
[Volz2005] Raphael Volz, Steffen Staab, Boris Motik: Incrementally Maintaining Materializations of Ontologies Stored in Logic Databases. J. Data Semantics 2: 1-34 (2005)
Thank You! Questions? Much More to Come! Keep an eye on http://www.streamreasoning.org Oxford, 2011-1-18