Stream Reasoning: Where We Got So Far
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Stream Reasoning : Where We Got So Far

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The presentation I gave at NeFoRS'10 colocated with ESWC 2010 in Heraklion, Greece, on May 31st, 2010

The presentation I gave at NeFoRS'10 colocated with ESWC 2010 in Heraklion, Greece, on May 31st, 2010

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Stream Reasoning: Where We Got So Far Stream Reasoning : Where We Got So Far Presentation Transcript

  • Stream Reasoning Where We Got So Far 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
  • Agenda
    • Motivation
    • Background
    • Concept
    • Running Example
    • Achievements
    • Retrospective and Conclusions
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Motivation It‘s a streaming World! [IEEE-IS2009]
    • Sensor networks, …
    • traffic engineering, …
    • social networking, …
    • financial markets, …
    • generate streams!
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Motivation Questions People are Asking
    • Given this brand of turbine, what is the expected time to failure when the barring starts to vibrate as now detected?
    • Is a traffic jam going to happen in this highway? And is then convenient to reallocate travelers based upon the forecast?”
    • Who are the opinion makers? i.e., the users who are likely to influence the behavior of other users who follow them
    • In the financial context, can we detect any intraday correlation clusters among stock exchange? 
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Motivation Problem Statement
    • Making sense
      • in real time
      • of gigantic and inevitably noisy data streams
      • in order to support the decision process of extremely large numbers of concurrent users
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Background What are data streams anyway?
    • Formally:
      • Data streams are unbounded sequences of time-varying data elements
    • Less formally:
      • an (almost) “continuous” flow of information
      • with the recent information being more relevant as it describes the current state of a dynamic system
    time NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Background Can the Semantic Web process data stream?
    • The Semantic Web, the Web of Data is doing fine
      • RDF, RDF Schema, SPARQL, OWL, DL
      • well understood theory,
      • rapid increase in scalability
    • BUT it pretends that the world is static or at best a low change rate both in change-volume and change-frequency
      • ontology versioning
      • belief revision
      • time stamps on named graphs
    • It sticks to the traditional one-time semantics
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • 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
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Background Stream Processing
    • Continuous queries registered over streams that are observed trough windows
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010 window input stream stream of answer Registered Continuous Query
  • Background Key Optimization in Stream Processing
    • When a continuous query is registered, generate a query execution plan
      • New plan merged with existing plans
      • Global scheduler for plan execution maximizing experience gathered with previous queries.
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Background Data Stream Management Systems (DSMS)
    • Research Prototypes
      • Amazon/Cougar (Cornell) – sensors
      • Aurora (Brown/MIT) – sensor monitoring, dataflow
      • Gigascope: AT&T Labs – Network Monitoring
      • Hancock (AT&T) – Telecom streams
      • Niagara (OGI/Wisconsin) – Internet DBs & XML
      • OpenCQ (Georgia) – triggers, view maintenance
      • Stream (Stanford) – general-purpose DSMS
      • Stream Mill (UCLA) - power & extensibility
      • Tapestry (Xerox) – publish/subscribe filtering
      • Telegraph (Berkeley) – adaptive engine for sensors
      • Tribeca (Bellcore) – network monitoring
    • High-tech startups
      • Streambase, Coral8, Apama, Truviso
    • Major DBMS vendors are all adding stream extensions as well
      • Oracle http://www.oracle.com/technology/products/dataint/htdocs/streams_fo.html
      • DB2 http://www.eweek.com/c/a/Database/IBM-DB2-Turns-25-and-Prepares-for-New-Life/
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Concept Stream Reasoning [IEEE-IS2010, Dagstuhl2010]
    • Idea origination
      • Can continuous semantics be ported to reasoning?
      • This is an unexplored yet high impact research area!
    • Stream Reasoning
      • Logical reasoning in real time on gigantic and inevitably noisy data streams in order to support the decision process of extremely large numbers of concurrent users.
      • -- S. Ceri , E. Della Valle , F. van Harmelen and H. Stuckenschmidt , 2010
    • Note: making sense of streams necessarily requires processing them against rich background knowledge
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Concept Research Challenges
    • Relation with data-stream systems
      • Just as RDF relates to data-base systems?
    • Query languages for semantic streams
      • Just as SPARQL for RDF but with continuous semantics?
    • Reasoning on Streams
      • Formal representations for stream reasoning
        • Active Logic? Step Logic? Temporal Logic? None of them?
      • Notions of soundness and completeness
        • How to define these on windows?
      • Efficient incremental updates of deductive closures?
      • How to combine streams and background knowledge?
    • Dealing with incomplete & noisy data
      • Even more so than on the current Web of Data
    • Distributed and parallel processing
      • Streams are parallel in nature
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Concept Engineering challenges
    • Software architectures
    • Integration with existing systems
    • Optimization and scalability
    • Real-time computations
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Concept Evaluation and Success Criteria
    • An early attempt
      • -- S. Ceri , E. Della Valle , F. van Harmelen , Ralf Möller and H. Stuckenschmidt , 2009
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010 Efficiency Criterion Querying Answering Complex Inference Nr. of streams 10s of streams 10s of streams Speed per stream (assertions/second a/t) 1000s a/t 10s a/t Nr. of registered queries 100s of queries 100s of queries Response Latency 10s of milliseconds 1000s of milliseconds
  • 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
    • E.g.
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010 Emanuele Della Valle - visit http://streamreasoning.org LDOW2010 @ WWW 2010, Raleigh, North Carolina, April 27th, 2010
  • Running Example Example of Questions Users are Asking
    • What are the hottest topics under discussion on Twitter?
    • Which topics have my close friends discussed in the last hour?
    • Who is discussing about Italian food in northern Italy right now?
    • Which movie is my friend likely to watch next?
    • Which Tuscany red wine should I recommend to one of my friends?
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Running Example Real Social Media Stream Data: Glue NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Running Example Glue Data Model as an Ontology NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010 URL rdfs : label skos : subject owl : sameAs Object Resource links describes URL foaf : name User sioc : follows foaf : knows accesses likes dislikes data stream background knowledge URL rdfs : label
  • Achievements Stream Reasoner Inputs and Outputs NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements Explored Continuous Semantics for SeWeb
    • We gave up one-time semantics in Semantic Web and explored the benefits provided by continuous semantics when dealing with streams
    • We investigated
      • Architecture of a Simple Stream Reasoner
      • RDF streams
        • the natural extension of the RDF data model to the new continuous scenario and
      • Continuous SPARQL (or simply C-SPARQL )
        • the extension of SPARQL for querying RDF streams.
      • Efficient incremental updates of deductive closures
        • specifically considering the nature of data streams
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements Architecture of our Simple Stream Reasoner
    • Based on the LarKC conceptual framework
    • http://www.larkc.eu
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010 Select Abstract Reason Streamed Input Window Content RDF Streams Answers Streams Window RDF Graphs
  • Achievements RDF Stream [WWW2009,EDBT2010]
    • RDF Stream Data Type
      • Ordered sequence of pairs, where each pair is made of an RDF triple and its timestamp t
        • (< triple >, t)
    • E.g.,
      • (<:Giulia :likes :Twilight >, 2010-02-12T13:34:41)
      • (<:John :accesses :TheLordOfTheRings >, 2010-02-12T13:36:28)
      • (<:Alice :dislikes :Twilight >, 2010-02-12T13:36:28)
      • (<:Bob :accesses :Chianti >, 2010-02-12T13:37:18)
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements Windows on RDF Streams
    • RDF streams are intrinsically infinite
    • A window extracts the last triples
    • The extraction can be
      • physical
        • a given number of triples
      • logical
        • a variable number of triples which occur during a given time interval
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements Example Window of RDF Stream [SDOW2009]
    • A real source of social semantic data streams
      • Source: the Social Network Glue http://getglue.com
      • RDF obtained applying GRDDL to XML results of invocation of http://api.getglue.com/v2/glue/recent Glue REST service
      • Check out the current content of the window http://c-sparql.cefriel.it/sdow-demo/RDFstream.html
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements C-SPARQL [WWW2009,EDBT2010]
    • We specificied of C-SPARQL syntax
      • Incrementally, from existing specifications
        • Including windows, grouping, aggregates, timestamping
    • We gave the formal semantics of C-SPARQL
      • Query registration, handling overloads
      • Order of evaluation, pattern matching over time, …
    • We investigated efficiency of evaluation
      • Defining a suitable algebra
      • Applying optimizations
      • Efficient materialization of inferred data from streams
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements An Simple Example of C-SPARQL Query
    • What have my closest friends been visiting in the last hour?
    • REGISTER QUERY WhatMyFriendsVisitedInTheLastHour AS
    • PREFIX sioc: <http://rdfs.org/sioc/ns#>
    • PREFIX foaf: <http://xmlns.com/foaf/0.1/>
    • PREFIX glue: <http://c-sparql.cefriel.it/sdow-demo/>
    • SELECT DISTINCT ?friend ?topic
    • FROM <http://c-sparql.cefriel.it/sdow-demo/glueusers.rdf>
    • FROM STREAM <http://c-sparql.cefriel.it/sdow-demo/interactions.trdf>
    • [ RANGE 60m STEP 5m ]
    • WHERE { glue:id1 foaf:knows ?friend .
    • ?post sioc:has_creator ?friend .
    • ?post rdf:type sioc:Post .
    • ?post sioc:topic ?topic . }
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements An Simple Example of C-SPARQL Query
    • What have my closest friends been visiting in the last hour?
    • REGISTER QUERY WhatMyFriendsVisitedInTheLastHour AS
    • PREFIX sioc: <http://rdfs.org/sioc/ns#>
    • PREFIX foaf: <http://xmlns.com/foaf/0.1/>
    • PREFIX glue: <http://c-sparql.cefriel.it/sdow-demo/>
    • SELECT DISTINCT ?friend ?topic
    • FROM <http://c-sparql.cefriel.it/sdow-demo/glueusers.rdf>
    • FROM STREAM <http://c-sparql.cefriel.it/sdow-demo/interactions.trdf>
    • [ RANGE 60m STEP 5m ]
    • WHERE { glue:id1 foaf:knows ?friend .
    • ?post sioc:has_creator ?friend .
    • ?post rdf:type sioc:Post .
    • ?post sioc:topic ?topic . }
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010 Triples from a graph Combined with triples from a stream Query registration (for continuous execution) FROM STREAM clause WINDOW
  • Achievements An Advance Example of C-SPARQL Query
    • Who are the opinion makers? i.e., the users who are likely to influence the behavior of other users who follow them
    • REGISTER STREAM OpinionMakers COMPUTED EVERY 5m AS
    • CONSTRUCT { ?opinionMaker sd:about ?resource }
    • FROM STREAM <http://streamingsocialdata.org/interactions> [RANGE 30m STEP 5m]
    • WHERE {
    • ?opinionMaker ?opinion ?resource .
    • ?follower sioc:follows ?opinionMaker.
    • ?follower ?opinion ?resource.
    • FILTER ( cs:timestamp (?follower) > cs:timestamp (?opinionMaker)
    • && ?opinion != sd:accesses )
    • }
    • HAVING ( COUNT(DISTINCT ?follower) > 3 )
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements C-SPARQL at Work [SDOW2009] NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010 http://c-sparql.cefriel.it/sdow-demo/C-SPARQLquery.html
  • Achievements Efficiency of Evaluation 1/3 [SDOW2009]
    • Evaluation of Window-based Selection
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements Efficiency of Evaluation 2/3 [EDBT2010]
    • Several transformations can be applied to algebraic representation of C-SPARQL
    • some recalling well known results from classical relational optimization
      • push of FILTERs and projections
    • some being more specific to the domain of streams.
      • push of aggregates.
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements Efficiency of Evaluation 3/3 [EDBT2010]
    • Push of filters and projections
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements Example of C-SPARQL and Reasoning 1/2
    • Who are the movie opinion makers ?
    • REGISTER STREAM MovieOpinionMakers COMPUTED EVERY 5m AS
    • CONSTRUCT { ?opinionMaker sd:about ?resource }
    • FROM STREAM <http://streamingsocialdata.org/interactions> [RANGE 30m STEP 5m]
    • WHERE {
    • ?opinionMaker ?opinion ?resource .
    • ?opinionMaker a sd:UserOnlyInterestInMovies .
    • ?follower sioc:follows ?opinionMaker.
    • ?follower ?opinion ?resource.
    • FILTER ( cs:timestamp(?follower) >
    • cs:timestamp(?opinionMaker)
    • && ?opinion != sd:accesses )
    • }
    • HAVING ( COUNT(DISTINCT ?follower) > 3 )
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements Example of C-SPARQL and Reasoning 2/2
    • If we define
    • sd:UserOnlyInterestInMovies rdfs:subClassOf sd:User;
    • rdfs:subClassOf [
    • a owl:Restriction;
    • owl:onProperty sd:likes;
    • owl:allValuesFrom yago:Movie;
    • ] .
    • if the current window contains the following triples,
    • ( <:Giulia sd:likes :Avatar >, 2010-02-12T13:18:05)
    • ( <:John sd:likes :StarWars >, 2010-02-12T13:36:23)
    • ( <:John sd:likes :WutheringHeights >, 2010-02-12T13:38:07)
    • ( <:Giulia sd:likes :AliceInWonderland >, 2010-02-12T13:42:07)
    • Giulia is an instance of the class UserOnlyInterestInMovies (i.e., she liked only movies), while John is not (i.e., he liked a movies and a book).
      • NOTE: we are using OWL2-RL + Negation As Failure
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements Incremental Reasoning: State-of-the-Art
    • Incremental Maintenance of Materialized Views
      • Stefano Ceri, Jennifer Widom: Deriving Incremental Production Rules for Deductive Data. Inf. Syst. 19(6): 467-490 (1994)
      • HA Kuno, EA Rundensteiner: Incremental Maintenance of Materialized Object-Oriented Views in MultiView: Strategies and Performance Evaluation. TDKE 1998
      • Raphael Volz, Steffen Staab, Boris Motik: Incrementally Maintaining Materializations of Ontologies Stored in Logic Databases. J. Data Semantics 2: 1-34 (2005)
    • Incremental Rule-based Reasoning
      • F Fabret, M Regnier, E Simon: An Adaptive Algorithm for Incremental Evaluation of Production Rules in Databases. VLDB 1993
      • B. Berster: Extending the RETE Algorithm for Event Management.TIME’02
    • Incremental DL Reasoning
      • Cuenca-Grau et al : History Matters: Incremental Ontology Reasoning Using Modules. ISWC 2007.
      • Parsia et al: Towards incremental reasoning through updates in OWL-DL. - Reasoning on the Web-Workshop at WWW-2006
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements State-of-the-Art Approach [Ceri1994,Volz2005]
    • Overestimation of deletion : Overestimates deletions by computing all direct consequences of a deletion.
    • Rederivation : Prunes those estimated deletions for which alternative derivations (via some other facts in the program) exist.
    • Insertion : Adds the new derivations that are consequences of insertions to extensional predicates.
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements our approach [ESWC2010] 1/2
    • Assuption
      • Insertions and deletions are triples respectively entering and exiting the window
      • The window size is known
    • Therefore
      • The time when each triple will expire is known and determined by the window size
        • E.g. if the window is 10s long a triple entering at time t will exit at time t+10s
      • Note: all knowledge can be annotated with an expiration time
        • i.e., background knowledge is annotated with + 
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements our approach [ESWC2010] 2/2
    • The algorithm
      • computes the entailments derived by the inserts,
      • annotates each entailed triple with a expiration time, and
      • eliminates from the current state all copies of derived triples except the one with the highest timestamp.
    • NOTE: if you like to learn more come to my presentation on 1.6.2010 in Mobility & Sensor Network I session
      • http://www.slideshare.net/emanueledellavalle/incremental-reasoning-on-streams-andrich-background-knowledge
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Achievements Comparative Evaluation [ESWC2010]
    • Hypothesis
      • Background knowledge do not change and it is 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]
      • Our approach
    • Results at increasing % of the triples updated
    • .
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Retrospective and Conclusions Wrap Up
    • RDF Streams
      • Notion defined
      • Examples of RDF streams can be easily created
        • e.g., http://c-sparql.cefriel.it/sdow-demo/RDFstream.html
    • C-SPARQL
      • Syntax and semantics defined as a SPARQL extension
      • Engine designed
      • 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
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Retrospective and Conclusions Achievements vs. Research Challenges
    • Relation with data-stream systems
      • Notion of RDF stream :-|
    • Query languages for semantic streams
      • C-SPARQL :-D
    • Reasoning on Streams
      • Formal representations for stream reasoning
        • :-P
      • Notions of soundness and completeness
        • :-P
      • 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
      • :-P
    • Distributed and parallel processing
      • :-P
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Retrospective and Conclusions Achievements vs. Engineering challenges
    • Software architectures
      • C-SPARQL engine :-| only very preliminary results
    • Integration with existing systems
      • :-) C-SPARQL prototype based on esper
    • Optimization
      • :-) EDBT 2010 and ESWC 2010 paper, but still a lot of investigation is possible
    • Scalability
      • :-D for Query Answering in C-SPARQL under simple RDF entailment regime
      • :-| for Query Answering in C-SPARQL under OWL-RL entailment regime
      • :-P for Complex Reasoning
    • Real-time computations
      • :-P
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Retrospective and Conclusions A Key Problem to Investigate [Dagstuhl2010]
    • Reasoning on Data streams has a huge potential impact
    • Current Reasoning methods are not suited to work on data with a high change frequency.
    • A promising approach?
      • -- H. Stuckenschmidt, S. Ceri, E. Della Valle and F. van Harmelen, 2010
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010 Raw Stream Processing RDF Streams Logic Programs DL Complexity Reasoning Querying Rewriting Abstraction Selection Interpretation Change Frequency PTIME 2NEXPTIME 10 4 Hz 1 Hz Dynamics and Scale vs. Complexity Evolving Knowlege
  • References (selection)
    • Vision
      • [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) bibtex
    • 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 bibtex
      • [SDOW2009] 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
    • Stream Reasoning
      • [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)
      • [Dagstuhl2010] Heiner Stuckenschmidt, Stefano Ceri, Emanuele Della Valle and Frank van Harmelen. Towards Expressive Stream Reasoning. Proceedings of the Dagstuhl Seminar on Semantic Aspects of Sensor Networks, 2010.
    NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010
  • Thank You! Questions? Much More to Come! Keep an eye on http://www.streamreasoning.org NeFoRS'10 @ ESWC 2010, Heraklion, Greece, May 31st, 2010