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Emanuele Della Valle - visit Challenges, Approaches, and Solutions in Stream Reasoning

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1 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Challenges, Approaches, and Solutions in Stream Reasoning http://streamreasoning.org Challenges, Approaches, and Solutions in Stream Reasoning http://streamreasoning.org http://streamreasoning.org Emanuele Della Valle DEI - Politecnico di Milano emanuele.dellavalle@polimi.it http://emanueledellavalle.org

2 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Agenda Motivation Concept Achievements Applications Conclusions Stavanger, 2012-5-92

3 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation It‘s a streaming World! [IEEE-IS2009] 1/3 Oil operations Traffic Financial markets Social networks Generate data streams! Stavanger, 2012-5-93

4 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation It‘s a streaming World! [IEEE-IS2009] 2/4 … and want to analyse data streams in real time In a well in progress to drown, how long time do I have given its historical behavior? Is public transportation where the people are? Can we detect any intra-day correlation clusters among stock exchanges? Who is driving the discussion about the top 10 emerging topics ? Stavanger, 2012-5-94

5 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation It‘s a streaming World! [IEEE-IS2009] 3/4 e.g., Real Time Rome (mobile network data streams) afternoon evening Normal dayExceptional day [source: http://senseable.mit.edu/realtimerome/ ]http://senseable.mit.edu/realtimerome/ Stavanger, 2012-5-95

6 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation It‘s a streaming World! [IEEE-IS2009] 4/4 e.g., Pulse of the Nation (social media streams) happier 12:00 23:00 [source: http://www.ccs.neu.edu/home/amislove/twittermood/ ]http://www.ccs.neu.edu/home/amislove/twittermood/ Stavanger, 2012-5-96

7 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation 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 Stavanger, 2012-5-97

8 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation The continuous nature of streams The nature of streams requires a paradigmatic change * –from persistent data to be stored and queried on demand a.k.a. one time semantics –to transient data to be consumed on the fly by continuous queries a.k.a. continuous semantics * This paradigmatic change first arose in DB community [Henzinger98] Stavanger, 2012-5-98

9 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation – The continuous nature of streams Continuous Semantics Continuous queries registered over streams that, in most of the cases, are observed trough windows window input streams streams of answer Registered Continuous Query Stavanger, 2012-5-99

10 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation – The continuous nature of streams Tools exists [Cugola2011] Types –Data Stream Management Systems –Complex Event Processors 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 –IBM InfoSphere Stream –Microsoft streaminsight –Oracle CEP Stavanger, 2012-5-910

11 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation New Requirements  New Challenges Typical Requirements Processing Streams Large datasets Reactivity Fine-grained information access Modeling complex application domains Continuous semantics Scalable processing Real-time systems Powerful query languages Rich ontology languages Stavanger, 2012-5-911

12 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation Are DSMS/CEP ready to address them? Typical Requirements Processing Streams Large datasets Reactivity Fine-grained information access Modeling complex application domains DSMS/CEP Continuous semantics Scalable processing Real-time systems Powerful query languages Rich ontology languages Stavanger, 2012-5-912

13 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation Is Semantic Web ready to address them? The Semantic Web, the Web of Data is doing fine –RDF, RDF Schema, SPARQL, OWL, RIF –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 Stavanger, 2012-5-913

14 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation New Requirements  New Challenges Typical Requirements Processing Streams Large datasets Reactivity Fine-grained information access Modeling complex application domains Semantic Web Continuous semantics Scalable processing Real-time systems Powerful query languages Rich ontology languages Stavanger, 2012-5-914

15 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Motivation New Requirements call for Stream Reasoning Typical Requirements Processing Streams Large datasets Reactivity Fine-grained information access Modeling complex application domains Continuous semantics Scalable processing Real-time systems Powerful query languages Rich ontology languages Stream Reasoning Stavanger, 2012-5-915

16 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Concept Stream Reasoning Definition [IEEE-IS2010] Making sense –in real time –of multiple, heterogeneous, gigantic and inevitably noisy data streams –in order to support the decision process of extremely large numbers of concurrent user Note: making sense of streams necessarily requires processing them against rich background knowledge, an unsolved problem in database Stavanger, 2012-5-916

17 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Concept Research Challenges Relation with DSMSs and CEPs –Just as RDF relates to data-base systems? Data types and query languages for semantic streams –Just RDF and SPARQL but with continuous semantics? Reasoning on Streams –Theory –Efficiency –Scalability Dealing with incomplete & noisy data –Even more than on the current Web of Data Distributed and parallel processing –Streams are parallel in nature, … Engineering Stream Reasoning Applications –Development Environment –Integration with other technologies –Benchmarks Stavanger, 2012-5-917

18 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements RDF Streams –Notion defined C-SPARQL –Syntax and semantics defined as a SPARQL extension –Engine designed and implemented Experiments with C-SPARQL under simple RDF entailment regimes –window based selection of C-SPARQL outperforms the standard FILTER based selection –algebraic optimizations of C-SPARQL queries are possible –Complex event can be detected using a network of C-SPARQL queries at high throughputs Experiment with C-SPARQL under RDFS++ entailment regimes –efficient incremental updates of deductive closures investigated –our approach outperform state-of-the-art when updates comes as stream Streaming Linked Data Framework prototyped Stavanger, 2012-5-918

19 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Outline RDF Streams –Notion defined C-SPARQL –Syntax and semantics defined as a SPARQL extension –Engine designed and implemented Experiments with C-SPARQL under simple RDF entailment regimes –window based selection of C-SPARQL outperforms the standard FILTER based selection –algebraic optimizations of C-SPARQL queries are possible –Complex event can be detected using a network of C-SPARQL queries at high throughputs Experiment with C-SPARQL under RDFS++ entailment regimes –efficient incremental updates of deductive closures investigated –our approach outperform state-of-the-art when updates comes as stream Streaming Linked Data Framework prototyped Stavanger, 2012-5-919

20 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Memo: Sensor Network Ontology Stavanger, 2012-5-9 20

21 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Memo: Sensor Network Ontology [ ] streaming part [ ] static part Stavanger, 2012-5-921

22 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements RDF Stream [WWW2009,EDBT2010,IJSC2010] RDF Stream Data Type –Ordered sequence of pairs, where each pair is made of an RDF triple and its timestamp  Timestamps are not required to be unique, they must be non- decreasing E.g., (, 2010-02-12T13:34:41) (, 2010-02-12T13:36:28) Stavanger, 2012-5-922

23 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Outline RDF Streams –Notion defined C-SPARQL –Syntax and semantics defined as a SPARQL extension –Engine designed and implemented Experiments with C-SPARQL under simple RDF entailment regimes –window based selection of C-SPARQL outperforms the standard FILTER based selection –algebraic optimizations of C-SPARQL queries are possible –Complex event can be detected using a network of C-SPARQL queries at high throughputs Experiment with C-SPARQL under RDFS++ entailment regimes –efficient incremental updates of deductive closures investigated –our approach outperform state-of-the-art when updates comes as stream Streaming Linked Data Framework prototyped Stavanger, 2012-5-923

24 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org MEMO: SPARQL Stavanger, 2012-5-9 24

25 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Where C-SPARQL Extends SPARQL Stavanger, 2012-5-9 25

26 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements An Example of C-SPARQL Query Which are the sensors that have observing winds above 50 mph in the last half an hour? Which is the observed average wind? REGISTER STREAM AvgWindSpeed AS CONSTRUCT { ?sens w:avgWindSpeed ?avgWindSpeed } FROM STREAM [RANGE 1h STEP 1h] WHERE { SELECT ?sens (AVG(?v) as ?avgWindSpeed) WHERE { ?sens om-owl:generatedObservation ?o. ?o a weather:WindSpeedObservation. ?o om-owl:result ?r. ?r om-owl:floatValue ?v.} GROUP BY ?sens HAVING (?avgWindSpeed > 5) } Stavanger, 2012-5-926

27 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements An Example of C-SPARQL Query Which are the sensors that have observing winds above 50 mph in the last half an hour? Which is the observed average wind? REGISTER STREAM AvgWindSpeed AS CONSTRUCT { ?sens w:avgWindSpeed ?avgWindSpeed } FROM STREAM [RANGE 1h STEP 1h] WHERE { SELECT ?sens (AVG(?v) as ?avgWindSpeed) WHERE { ?sens om-owl:generatedObservation ?o. ?o a weather:WindSpeedObservation. ?o om-owl:result ?r. ?r om-owl:floatValue ?v.} GROUP BY ?sens HAVING (?avgWindSpeed > 5) } Query registration (for continuous execution) RDF Stream added as new ouput format FROM STREAM clause WINDOW SPARQ 1.1 features Sub-queries aggregates Stavanger, 2012-5-927

28 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Outline RDF Streams –Notion defined C-SPARQL –Syntax and semantics defined as a SPARQL extension –Engine designed and implemented Experiments with C-SPARQL under simple RDF entailment regimes –window based selection of C-SPARQL outperforms the standard FILTER based selection –algebraic optimizations of C-SPARQL queries are possible –Complex event can be detected using a network of C-SPARQL queries at high throughputs Experiment with C-SPARQL under RDFS++ entailment regimes –efficient incremental updates of deductive closures investigated –our approach outperform state-of-the-art when updates comes as stream Streaming Linked Data Framework prototyped Stavanger, 2012-5-928

29 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements FROM STREAM Clause - Types of Window physical: a given number of triples logical: a variable number of triples which occur during a given time interval (e.g., 1 hour) –Sliding: they are progressively advanced of a given STEP (e.g., 5 minutes) –Tumbling: they are advanced of exactly their time interval Stavanger, 2012-5-929

30 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Efficiency of Evaluation [IEEE-IS2010] window based selection of C-SPARQL outperforms the standard FILTER based selection Stavanger, 2012-5-930

31 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Outline RDF Streams –Notion defined C-SPARQL –Syntax and semantics defined as a SPARQL extension –Engine designed and implemented Experiments with C-SPARQL under simple RDF entailment regimes –window based selection of C-SPARQL outperforms the standard FILTER based selection –algebraic optimizations of C-SPARQL queries are possible –Complex event can be detected using a network of C-SPARQL queries at high throughputs Experiment with C-SPARQL under RDFS++ entailment regimes –efficient incremental updates of deductive closures investigated –our approach outperform state-of-the-art when updates comes as stream Streaming Linked Data Framework prototyped Stavanger, 2012-5-931

32 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Algebraic optimizations of C-SPARQL [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. Stavanger, 2012-5-932

33 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements algebraic optimizations of C-SPARQL [EDBT2010] Push of filters and projections Stavanger, 2012-5-933

34 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Outline RDF Streams –Notion defined C-SPARQL –Syntax and semantics defined as a SPARQL extension –Engine designed and implemented Experiments with C-SPARQL under simple RDF entailment regimes –window based selection of C-SPARQL outperforms the standard FILTER based selection –algebraic optimizations of C-SPARQL queries are possible –Complex event can be detected using a network of C-SPARQL queries at high throughputs Experiment with C-SPARQL under RDFS++ entailment regimes –efficient incremental updates of deductive closures investigated –our approach outperform state-of-the-art when updates comes as stream Streaming Linked Data Framework prototyped Stavanger, 2012-5-934

35 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Complex Event Detection as stream compositions e.g., continuous detection of blizzards by analyzing multiple streams of data generated by weather sensors spread across a continental area Blizzard: a severe snowstorm with high winds and low visibility lasting at least three hours Stavanger, 2012-5-935

36 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Complex Event Detection as stream compositions Linked Sensor Data Adapter C-SPARQL Query LEGEND Most Liked POIs Q Q Count Snow Fall Q Q [1 HOUR] [TUMBLING] AVG Wind Speed Q Q AVG Temp Q Q [1 HOUR] [TUMBLING] [1 HOUR] [TUMBLING] [3 HOURS] [STEP 1 HOUR] Stavanger, 2012-5-936

37 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Complex Event Detection as stream compositions snowfall+ strong winds+ low temp  blizzards Live demonstration at http://streamreasoning.org/demos/blizzard-detectionhttp://streamreasoning.org/demos/blizzard-detection Stavanger, 2012-5-937

38 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements High Throughputs [JWS2012a] Stavanger, 2012-5-938

39 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Outline RDF Streams –Notion defined C-SPARQL –Syntax and semantics defined as a SPARQL extension –Engine designed and implemented Experiments with C-SPARQL under simple RDF entailment regimes –window based selection of C-SPARQL outperforms the standard FILTER based selection –algebraic optimizations of C-SPARQL queries are possible –Complex event can be detected using a network of C-SPARQL queries at high throughputs Experiment with C-SPARQL under RDFS++ entailment regimes –efficient incremental updates of deductive closures investigated –our approach outperform state-of-the-art when updates comes as stream Streaming Linked Data Framework prototyped Stavanger, 2012-5-939

40 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Where’s the Reasoning? Example: can we measure the the impact of a tweet? Twitter allows two traceable ways of discussing a tweet: reply: a user reply to a tweet of another user (it always retweet the original tweet) retweet: a user propagates to his/her followers an interesting tweet For example t1t1 t3t3 t5t5 t8t8 retweet reply t2t2 t4t4 t7t7 t6t6 retweet reply now10 min ago 20 min ago30 min ago40 min ago 50 min ago Stavanger, 2012-5-9 40

41 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Example of C-SPARQL and Reasoning 1/2 What impact have I been creating with my tweets in the last hour? Let’s count them … REGISTER STREAM OpinionSpreading COMPUTED EVERY 30s AS SELECT ?tweet (count(?tweet) AS ?impact FROM STREAM [RANGE 60m STEP 10m] WHERE { :t1 sr:discuss ?tweet } :reply rdfs:subPropertyOf :discuss. :retweet rdfs:subPropertyOf :discuss. t1t1 t3t3 t5t5 t8t8 retweet reply t2t2 t4t4 t7t7 t6t6 retweet reply discuss :discuss a owl:TransitiveProperty. 7! Stavanger, 2012-5-9 41

42 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Achievements Our approach [ESWC2010] The algorithm 1.deletes all triples (asserted or inferred) that have just expired 2.computes the entailments derived by the inserts, 3.annotates each entailed triple with a expiration time, and 4.eliminates from the current state all copies of derived triples except the one with the highest timestamp. Stavanger, 2012-5-9 42

43 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Implementation Comparative Evaluation on Materialization base-line: re-computing the materialization from scratch state-of-the-art [Ceri1994,Volz2005] our approach [ESWC2010] Stavanger, 2012-5-9 43 % of the materialization changed when the window slides

44 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Achievements Comparative Evaluation on Query Answering comparison of the average time needed to answer a C-SPARQL query using –backward reasoner –the naive approach of re-computing the materialization –our approach Backward reasoning Stavanger, 2012-5-9 44

45 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Outline RDF Streams –Notion defined C-SPARQL –Syntax and semantics defined as a SPARQL extension –Engine designed and implemented Experiments with C-SPARQL under simple RDF entailment regimes –window based selection of C-SPARQL outperforms the standard FILTER based selection –algebraic optimizations of C-SPARQL queries are possible –Complex event can be detected using a network of C-SPARQL queries at high throughputs Experiment with C-SPARQL under RDFS++ entailment regimes –efficient incremental updates of deductive closures investigated –our approach outperform state-of-the-art when updates comes as stream Streaming Linked Data Framework prototyped Stavanger, 2012-5-9 45

46 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Achievements Achievements Streaming Linked Data Framework [JWS2012a] Features Accessing raw data stream from C- SPARQL Publishing streams and C-SPARQL query results as Linked Data Connecting C- SPARQL queries in a network Recoding and replaying portions of stream Supporting fast prototyping of applications Stavanger, 2012-5-9 46

47 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Applications Location Based Social Media Analytics [JWS2012b] Stavanger, 2012-5-9 47

48 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Applications Location Based Social Media Analytics [JWS2012b] Live demonstration at http://streamreasoning.org/demos/bottarihttp://streamreasoning.org/demos/bottari http://youtu.be/XGOKe_lhSks Stavanger, 2012-5-9 48

49 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Applications Blizzard Detection [JWS2012a] Stavanger, 2012-5-9 49

50 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Applications Blizzard Detection [JWS2012a] Live demonstration at http://streamreasoning.org/demos/blizzard-detectionhttp://streamreasoning.org/demos/blizzard-detection Stavanger, 2012-5-9 50

51 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Applications Hurricane Detection [JWS2012a] Stavanger, 2012-5-9 51

52 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Applications Hurricane Detection [JWS2012a] Live demonstration at http://streamreasoning.org/demos/hurricane-detectionhttp://streamreasoning.org/demos/hurricane-detection Stavanger, 2012-5-9 52

53 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org You can try C-SPARQL out! Working prototype available for download in a “ready to go pack” http://streamreasoning.org/download http://streamreasoning.org/download The Streaming Linked Data Framework will be soon released too, ask me directly for a pre- release version. Stavanger, 2012-5-9 53

54 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Conclusions Research Challenges vs. Achievements Relation with DSMSs and CEPs Notion of RDF stream :-| alternative solutions can be investigated Data types and query languages for semantic streams C-SPARQL :-D work in progress in FZI&AIFB [1,2] DERI [3], UPM [4] Reasoning on Streams Theory :-( Efficiency :-) work in progress in ISTI-Innsbruck [5] Scalability :-| work in progress in IBM&VUA [6] Dealing with incomplete & noisy data Even more than on the current Web of Data :-( some initial joint work with SIEMENS only [IEEE-IS2010] Distributed and parallel processing Streams are parallel in nature, … :-| work in progress in IBM&VUA [6] Engineering Stream Reasoning Applications Development Environment :-) work in progress in UPM [7] Integration with other technologies :-) Benchmarks :-P work in progress in Planet Data [8] Stavanger, 2012-5-9 54

55 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.orgCredits Politecnico di Milano’s colleagues Prof. Stefano Ceri who had the initial intuition about the value of introducing data streams to the semantic Web community Marco Balduini, Davide Barbieri, Daniele Braga, Stefano Ceri and Michael Grossniklaus who helped concieving the C-SPARQL Engine and the Streaming Linked Data Framework once again to Davide Barbieri who engineered most of the C- SPARQL Engine as part of his PhD once again to Marco Balduini who engineered most of Streaming Linked Data Framework as part of his M.Sc. Thesis Politecnico di Milano’s Master students that assisted in the design and development of the prototypes Mirko Bratomi, and Marco Regaldo Colleagues that in helped in concieving, designing, and prototyping the applications CEFRIEL: Irene Celino, and Danile Dell’Aglio SIEMENS: Yi Huang, and Volker Tresp Saltlux: Seonho Kim, and Tony Lee Stavanger, 2012-5-9 55

56 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org References My papers [IEEE-IS2009] E. Della Valle, S. Ceri, F. van Harmelen, D. Fensel It's a Streaming World! Reasoning upon Rapidly Changing Information. IEEE Intelligent Systems 24(6): 83-89 (2009)It's a Streaming World! Reasoning upon Rapidly Changing Information [EDBT2010] D.F. Barbieri, D.Braga, S. Ceri and M. Grossniklaus. An Execution Environment for C-SPARQL Queries. EDBT 2010An Execution Environment for C-SPARQL Queries [WWW2009] D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, M. Grossniklaus: C-SPARQL: SPARQL for continuous querying. WWW 2009: 1061-1062 C-SPARQL: SPARQL for continuous querying [SIGMODRec2010] D.F. Barbieri, D.Braga, S. Ceri and M. Grossniklaus. : Querying RDF streams with C-SPARQL. SIGMOD Record 39(1): 20-26 (2010) Querying RDF streams with C-SPARQL [IEEE-IS2010] D. Barbieri, D. Braga, S. Ceri, E. Della Valle, Y. Huang, V. Tresp, A.Rettinger, H. Wermser: Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics IEEE Intelligent Systems, 30 Aug. 2010.Deductive and Inductive Stream Reasoning forSemantic Social Media Analytics [JWS2012a] E. Della Valle, M. Balduini: SLD: a Framework for Streaming Linked Data. JWS. 2012 Under Review [JWS2012b] M. Balduini; I.Celino; E. Della Valle; D.Dell'Aglio; Y. Huang; T. Lee; S. Kim; V. Tresp: BOTTARI: an Augmented Reality Mobile Application to deliver Personalized and Location-based Recommendations by Continuous Analysis of Social Media Streams. JWS. 2012. to appear. [ESWC2010] D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, M. Grossniklaus. Incremental Reasoning on Streams and Rich Background Knowledge. ESWC 2010 Incremental Reasoning on Streams and Rich Background Knowledge 56 Stavanger, 2012-5-9

57 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org References Other groups’ papers [1] Darko Anicic, Paul Fodor, Sebastian Rudolph, Nenad Stojanovic: EP-SPARQL: a unified language for event processing and stream reasoning. WWW 2011: 635-644 [2] Danh Le Phuoc, Minh Dao-Tran, Josiane Xavier Parreira, Manfred Hauswirth: A Native and Adaptive Approach for Unified Processing of Linked Streams and Linked Data. International Semantic Web Conference (1) 2011: 370-388 [3] D. Anicic, S. Rudolph, P. Fodor, N. Stojanovic: Real-Time Complex Event Recognition and Reasoning-a Logic Programming Approach. Applied Artificial Intelligence 26(1-2): 6-57 (2012) [4] Jean-Paul Calbimonte, Óscar Corcho, Alasdair J. G. Gray: Enabling Ontology-Based Access to Streaming Data Sources. ISWC (1) 2010: 96-111 [5] S. Komazec and D. Cerri: Towards Efficient Schema-Enhanced Pattern Matching over RDF Data Streams. First International Workshop on Ordering and Reasoning (OrdRing2011) [6] Jesper Hoeksema, Spyros Kotoulas: High-performance Distributed Stream Reasoning using S4. First International Workshop on Ordering and Reasoning (OrdRing2011) [7] A.J.G. Gray, R.Garcia-Castro, K.Kyzirakos, M.Karpathiotakis, J.Calbimonte, K.R.Page, J.Sadler, A.Frazer, I.Galpin, A.A.A. Fernandes, N.W. Paton, O.Corcho, M.Koubarakis, D.De Roure, K. Martinez, A. Gómez-Pérez: A Semantically Enabled Service Architecture for Mashups over Streaming and Stored Data. ESWC (2) 2011: 300-314 [8] PlanetData. D1.2 Benchmarking RDF Storage Engines. http://wiki.planet- data.eu/web/D1.2http://wiki.planet- data.eu/web/D1.2 57 Stavanger, 2012-5-9

58 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org References Background papers [Henzinger98] Henzinger, M. R. & Raghavan, P. (1998). Computing on data streams. Systems Research. [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) [Cugola2011] Alessandro Margara, Gianpaolo Cugola: Processing flows of information: from data stream to complex event processing. DEBS 2011: 359-360Processing flows of information: from data stream to complex event processing 58 Stavanger, 2012-5-9

59 Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Emanuele Della Valle - visit http://streamreasoning.orghttp://streamreasoning.org Thank You! Questions? Much More to Come! Keep an eye on http://www.streamreasoning.org http://www.streamreasoning.org Stavanger, 2012-5-959


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