Stream Reasoning: State of the Art and Beyond
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Stream Reasoning: State of the Art and Beyond

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The talk about "Stream Reasoning" for INQUEST -- INnovative QUErying of STreams 2012 -- (http://games.cs.ox.ac.uk/inquest12/) organized in Oxford, United Kingdom, September 25-27 2012. ...

The talk about "Stream Reasoning" for INQUEST -- INnovative QUErying of STreams 2012 -- (http://games.cs.ox.ac.uk/inquest12/) organized in Oxford, United Kingdom, September 25-27 2012.

The talks presents a comprehensive view on "Stream Reasoning" -- reasoning on rapidly flowing information. It illustrates the challenges, presents the achievements of the database group of Politecnico di Milano on the topic, reviews the challenges pointing to results and ongoing work in the Semantic Web community and proposes how to go beyond the current Stream Reasoning concept. It particular, it points out that "orders matters" when processing massive data and it proposes to investigate streaming algorithms for automated reasoning that can be applied not only to data streams that are "naturally" ordered (by recency) but to any sortable data source.

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Stream Reasoning: State of the Art and Beyond Stream Reasoning: State of the Art and Beyond Presentation Transcript

  • Stream Reasoning:State of the Art and Beyond http://streamreasoning.org Emanuele Della Valle DEI - Politecnico di Milano emanuele.dellavalle@polimi.it http://emanueledellavalle.org Emanuele Della Valle - visit http://streamreasoning.org
  • Agenda • Motivation • Concept • Running Example • Achievements • Challenges vs. Achievements • Beyond Stream Reasoning • Conclusions Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 2
  • MotivationIt‘s a streaming World! [IEEE-IS2009] 1/3 • Oil operations • Traffic • Financial markets • Social networks • Generate data streams! Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 3
  • MotivationIt‘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 ? Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 4
  • MotivationWhat are data streams anyway? • Formally: – Data streams are unbounded sequences of time- varying data elements time • 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 5
  • MotivationThe 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] Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 6
  • Motivation – The continuous nature of streamsContinuous Semantics • Continuous queries registered over streams that, in most of the cases, are observed trough windows window input streams Registered streams of answer Continuous Query Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 7
  • Motivation – The continuous nature of streamsTools 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 8
  • MotivationNew Requirements  New Challenges Typical Requirements • Processing Streams • Continuous semantics • Large datasets • Scalable processing • Reactivity • Real-time systems • Fine-grained information • Powerful query access languages • Modeling complex • Rich ontology application domains languages Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 9
  • MotivationAre DSMS/CEP ready to address them? Typical Requirements DSMS/CEP • Processing Streams • Continuous semantics • Large datasets • Scalable processing • Reactivity • Real-time systems • Fine-grained information • Powerful query access languages • Modeling complex • Rich ontology application domains languages Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 10
  • MotivationIs 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 11
  • MotivationNew Requirements  New Challenges Typical Requirements Semantic Web • Processing Streams • Continuous semantics • Large datasets • Scalable processing • Reactivity • Real-time systems • Fine-grained information • Powerful query access languages • Modeling complex • Rich ontology application domains languages Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 12
  • MotivationNew Requirements call for Stream Reasoning Typical Requirements • Processing Streams • Continuous semantics • Large datasets • Scalable processing • Reactivity • Real-time systems • Fine-grained information • Powerful query access languages • Modeling complex • Rich ontology application domains languages Stream Reasoning Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 13
  • ConceptStream 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 14
  • ConceptResearch 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 15
  • Running ExampleSocial Media Analytics in BOTTARI [JWS2012] http://streamreasoning.org/demos/bottari Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 16
  • Running Example Data Model Used in BOTTARI sr:following sr:follower sioc:UserAccount sr:TwitterUser sioc:id(xsd:string) sr:screenName(xsd:string) twd:post sr:retweetsioc:creator_of sioc:has_creator twd:discuss sr:reply sr:Tweet sioc:Post sr:messageID(xsd:string) sioc:content(xsd:string) sr:messageTimeStamp(xsd:string) sr:talksAboutPositively sr:talksAbout sr:talksAboutNeutrally sr:talksAboutNegatively geo:SpatialThing sr:NamedPlace geo:lat (xsd:float)geo:long(xsd :float) Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 17
  • Running Example Streaming vs. Background Information User related background knowledgedata stream Point of Interest related background knowledge Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 18
  • 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 – 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 19
  • AchievementsOutline • 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 – 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 20
  • AchievementsRDF 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., (<:Alice :posts :post1 >, 2010-02-12T13:34:41) (<:post1 :talksAboutPositively :LaScala>, 2010-02-12T13:34:41) (<:Bob :posts :post2 >, 2010-02-12T13:36:28) (<:post2 :talksAboutNegatively :Duomo>, 2010-02-12T13:36:28) Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 21
  • AchievementsOutline • 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 – 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 22
  • MEMO: SPARQL Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 23
  • AchievementsWhere C-SPARQL Extends SPARQL Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 24
  • AchievementsAn 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 ) Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 25
  • AchievementsAn Example of C-SPARQL Query Who are the registration makers? i.e., the users who are likely Query opinion RDF Stream added as to influence the behavior of other users who ouput format (for continuous execution) new follow them REGISTER STREAM OpinionMakers COMPUTED EVERY 5m AS CONSTRUCT { ?opinionMaker sd:about ?resource } FROM STREAM <http://streamingsocialdata.org/interactions>clause FROM STREAM [RANGE 30m STEP 5m] WHERE { WINDOW ?opinionMaker ?opinion ?resource . Builtin to ?follower sioc:follows ?opinionMaker.access timestamps ?follower ?opinion ?resource. FILTER ( cs:timestamp(?follower) > cs:timestamp(?opinionMaker) Aggregates as in SPARQL 1.1 && ?opinion != sd:accesses ) } HAVING ( COUNT(DISTINCT ?follower) > 3 ) Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 26
  • AchievementsOutline • 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 – 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 27
  • AchievementsFROM 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 28
  • AchievementsEfficiency of Evaluation [IEEE-IS2010] • window based selection of C-SPARQL outperforms the standard FILTER based selection Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 29
  • AchievementsOutline • 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 – 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 30
  • AchievementsAlgebraic 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 31
  • Achievementsalgebraic optimizations of C-SPARQL [EDBT2010] • Push of filters and projections 125 100 75 ms 50 25 0 10 100 1000 10000 100000 Window Size None Static Only Streaming Only Both Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 32
  • AchievementsOutline • 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 – 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 33
  • AchievementsHigh Throughputs [JWS2012] Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 34
  • AchievementsOutline • 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 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 35
  • AchievementsWhere’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 reply reply reply t2 t4 t7 retweet reply reply t1 t3 t5 t8 retweet t6 50 min ago 40 min ago 30 min ago 20 min ago 10 min ago now Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 36
  • AchievementsExample of C-SPARQL and Reasoning 1/2What impact have I been creating with my tweets in the last hour?Let’s count them …REGISTER STREAM OpinionSpreading COMPUTED EVERY 30s ASSELECT ?tweet (count(?tweet) AS ?impactFROM STREAM <http://ex.org> [RANGE 60m STEP 10m]WHERE { :reply rdfs:subPropertyOf :discuss . :retweet rdfs:subPropertyOf :discuss . :t1 sr:discuss ?tweet :discuss a owl:TransitiveProperty .} discuss reply discuss reply discuss reply t2 t4 t7 discuss retweet discuss reply discuss reply t1 t3 t5 t8 retweet discuss t6 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 37
  • AchievementsOur 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. Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 38
  • ImplementationComparative Evaluation on Materialization • base-line: re-computing the materialization from scratch • state-of-the-art [Ceri1994,Volz2005] • our approach [ESWC2010] 10000 1000 ms. 100 10 0,0% 2,0% 4,0% 6,0% 8,0% 10,0% 12,0% 14,0% 16,0% 18,0% 20,0% % of the materialization changed when theslides % of the materialization changed when the window window slides incremental-volz incremental-stream Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 39
  • AchievementsComparative 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 20 15 ms. 10 5 0 forward reasoning Backward reasoning naive approach incremental-stream query 5,82 1,61 1,61 materialization 0 15,91 0,28 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 40
  • AchievementsLocation Based Social Media Analytics [JWS2012]Live demonstration at http://streamreasoning.org/demos/bottari 41 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org
  • AchievementsSensor Networks - Weather phenomenon detection Live Oxford, 2012-9-25 demonstration at http://streamreasoning.org/demos/ Emanuele Della Valle - visit http://streamreasoning.org 42
  • 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 :-) work in progress in Potsdam&DERI [9,10]  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  Demonstrative applications in Social Media and Sensor Networks :-)  Development Environment :-) work in progress in UPM [7]  Benchmarks :-P work in progress in CWI&UPM [8] Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 43
  • Beyond Stream Reasoning [SWJ2012]Positioning Stream Reasoning Types of orders Natural DSMS CEP Stream reasoning No ordering Scalable reasoning Types of reasoning No reasoning Data-driven Query-driven Combinations Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 44
  • Beyond Stream Reasoning [SWJ2012]Architecture of Stream Reasoner • Continuous reasoning tasks registered over streams that, in most of the cases, are observed trough windows window Registered input streams streams of answer Continuous Reasoning Tasks Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 45
  • Beyond Stream Reasoning [SWJ2012]Order-aware Data Management Types of orders Combinations data management Order-aware Expensive to enforce Cheap to enforce Natural Stream reasoning No ordering Scalable reasoning Types of reasoning No reasoning Data-driven Query-driven Combinations Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 46
  • Beyond Stream Reasoning [SWJ2012]Order-aware Data Management • When treating massive data order matters! Data as a where we can e.g., order by sortable entity enforce orderings • sortable literals easily and logically • popularity • uncertainty • trust Most relevant streaming answers first algorithms • If N is the size of the input, a problem is considered to be “well- solved” if a streaming algorithm exists which requires at most O(poly(log(N)) space and time [Henzinger98, Babcock2002] Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 47
  • Beyond Stream Reasoning [SWJ2012]A first attempt: SPARQL-Rank [ISWC2012] An extended SPARQL algebra where ORDER is a first class citizen FROM TO Materialize then sort Split and interleave Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 48
  • Beyond Stream Reasoning [SWJ2012]A first attempt: SPARQL-Rank [ISWC2012] Performance increase up to 2 orders of magnitude for K<100 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 49
  • Beyond Stream Reasoning [SWJ2012]A new space of investigation Types of orders Combinations Order-aware data management reasoning Order-aware Expensive to enforce Top-k Cheap to enforce Reasoning Natural Stream reasoning No ordering Scalable reasoning Types of reasoning No reasoning Data-driven Query-driven Combinations Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 50
  • Conclusions • The Semantic Web community positively answered to the call at investigating stream reasoning • A number of work in progress are rapidly developing this field, but we are only at the very beginning • It may be the right time to move from naturally ordered data to other types of ordering by deepening the study of streaming algorithms for automatic reasoning Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 51
  • Credits • 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 – Sara Magliacane and Alessandro Bozzon who started the exploration of Order-aware reasoning working on SPARQL-RANK • People I directly worked with on the topic – CEFRIEL: Irene Celino, and Danile Dell’Aglio – Saltlux: Seonho Kim, and Tony Lee – SIEMENS: Yi Huang, and Volker Tresp – STI-Innsbruck: prof. Dieter Fensel and Srdjan Komazec – UO: prof. Ian Horrocks and Markus Krötzsch – VUA: prof. Frank van Harmelen and Stefan Schlobach • The broader research community that showed interest in stream reasoning 52 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org
  • Downloads • C-SPARQL Engine (no reasoning support) – A ready to go pack for eclipse • http://streamreasoning.org/download – Source code available on request • SPARQL-Rank Engine (ARQ-Rank) – Source code and experimental data • http://sparqlrank.search-computing.org/ Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 53
  • ReferencesMy papers • [IEEE-IS2009] E. Della Valle, S. Ceri, F. van Harmelen, D. Fensel Its a Streaming World! Reasoning upon Rapidly Changing Information. IEEE Intelligent Systems 24(6): 83-89 (2009) • [EDBT2010] D.F. Barbieri, D.Braga, S. Ceri and M. Grossniklaus. An Execution Environment for C- SPARQL Queries. EDBT 2010 • [WWW2009] D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, M. Grossniklaus: C-SPARQL: SPARQL for continuous querying. WWW 2009: 1061-1062 • [SIGMODRec2010] D.F. Barbieri, D.Braga, S. Ceri and M. Grossniklaus. : Querying RDF streams with C-SPARQL. SIGMOD Record 39(1): 20-26 (2010) • [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. • [JWS2012] M. Balduini; I.Celino; E. Della Valle; D.DellAglio; 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. IN PRESS. • [ESWC2010] D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, M. Grossniklaus. Incremental Reasoning on Streams and Rich Background Knowledge. ESWC 2010 • [SWJ2012] E. Della Valle, S.Schlobach, M. Krötzsch, A. Bozzon, S. Ceri, I. Horrocks. Order Matters! Harnessing a World of Orderings for Reasoning over Massive Data. Accepted with minor revision to SWJ • [ISWC2012] S. Magliacane, A. Bozzon, E. Della Valle. Efficient Execution of Top-k SPARQL Queries. ISWC 2012. IN PRESS 54 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org
  • ReferencesOther 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] Ying Zhang, Minh-Duc Pham, Oscar Corcho and Jean Paul Calbimonte. SRBench: A Streaming RDF/SPARQL Benchmark ISWC 2012: IN PRESS [9] Gebser, M., Sabuncu, O., & Schaub, T. (2011). An incremental answer set programming based system for finite model computation. AI Commun., 24(2), 195–212. [10] Gebser, M., Grote, T., Kaminski, R., Obermeier, P., Sabuncu, O., & Schaub, T. (2012). Stream Reasoning with Answer Set Programming: Preliminary Report. In KR’12, pages 613–617. AAAI Press. 55 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org
  • ReferencesBackground papers • [Babcock2002] Brian Babcock, Shivnath Babu, Mayur Datar, Rajeev Motwani, Jennifer Widom: Models and Issues in Data Stream Systems. PODS 2002: 1-16 • [Ceri1994] Stefano Ceri, Jennifer Widom: Deriving Incremental Production Rules for Deductive Data. Inf. Syst. 19(6): 467-490 (1994) • [Cugola2011] Alessandro Margara, Gianpaolo Cugola: Processing flows of information: from data stream to complex event processing. DEBS 2011: 359-360 • [Henzinger98] Henzinger, M. R. & Raghavan, P. (1998). Computing on data streams. Systems Research. • [Volz2005] Raphael Volz, Steffen Staab, Boris Motik: Incrementally Maintaining Materializations of Ontologies Stored in Logic Databases. J. Data Semantics 2: 1-34 (2005) 56 Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org
  • Thank You! Questions? Much More to Come! Keep an eye on http://www.streamreasoning.org Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 57
  • Call for Papers The International Journal on Semantic Web and Information Systems IJSWIS seeks contributions to a Special Issue on Stream reasoning Editors:Emanuele Della Valle, Stefano Ceri, Frank van Harmelen Important Dates: Abstract Submission: 30th September 2012 Submission Deadline: 31th October 2012Oxford, 2012-9-25 Emanuele Della Valle - visit http://streamreasoning.org 58