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Stream Reasoning: a summary of ten years of research and a vision for the next decade

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Stream reasoning studies the application of inference techniques to data characterised by being highly dynamic. It can find application in several settings, from Smart Cities to Industry 4.0, from Internet of Things to Social Media analytics. This year stream reasoning turns ten, and this talk analyses its growth. In the first part, it traces the main results obtained so far, by presenting the most prominent studies. It starts by an overview of the most relevant studies developed in the context of semantic web, and then it extends the analysis to include contributions from adjacent areas, such as database and artificial intelligence. Looking at the past is useful to prepare for the future: the second part presents a set of open challenges and issues that stream reasoning will face in the next future.

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Stream Reasoning: a summary of ten years of research and a vision for the next decade

  1. 1. Stream Reasoning: a summary of ten years of research and a vision for the next decade http://content.iospress.com/articles/data-science/ds006 Emanuele Della Valle DEIB - Politecnico di Milano @manudellavalle emanuele.dellavalle@polimi.it http://emanueledellavalle.org Linköping University, Sweden - 5.10.2017
  2. 2. Me • Assistant Professor at DEIB Politecnico di Milano • Expert in semantic technologies and stream computing • Brander of stream reasoning: an approach to master the velocity and variety dimension of Big Data • https://scholar.google.com/scholar?hl= en&q="stream+reasoning" • 16 years of experience in research and innovation projects • Startupper: • http://www.fluxedo.com 2
  3. 3. INTRODUCTION SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 3
  4. 4. It's a streaming world … • Off-shore oil operations • Smart Cities • Power turbine • Social networks • Generate data streams! 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) SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 4
  5. 5. … looking for reactive answers … • When a sensor on a drill in an oil-rig indicates that it is about to get stuck, how long can I keep drilling? • Where am I likely going to run into a traffic jam during my commute tonight? • Which electricity-producing turbine has sensor readings similar to any turbine that subsequently had a critical failure? • Who is driving the discussion about the top 10 emerging topics ? • Require continuous processing and reactive answer SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 5
  6. 6. … and many more conflicting requirements A system able to answer those queries must be able to • handle volume • handle velocity • handle variety • cope with incompleteness • cope with noise • provide reactive answers • support fine-grained access • integrate complex domain models • offer high-level languages SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 6
  7. 7. Grand challenge • Volume + Velocity + Variety = hard deal SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org volume months days hours min. sec. ms. velocity ZB EB PB TB GB MB KB Variety 7
  8. 8. A good reason to embrace it! • ++ Variety  ++ value SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org value ms. sec. min. hours days months years velocity Variety 8
  9. 9. From challenges to opportunities • Formally data streams are : – unbounded sequences of time-varying data elements • Less formally, in many application domains, they are: – a “continuous” flow of information – where recent information is more relevant as it describes the current state of a dynamic system • Opportunities – Forget old enough information – Exploit the implicit ordering (by recency) in the data time SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 9
  10. 10. State-of-the-art: DSMS and CEP • A paradigmatic change! • Continuous queries registered over streams that are observed trough windows window input streams streams of answerRegistered Continuous Query Dynamic System SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 10
  11. 11. DSMS and CEP vs. requirements Requirement DSMS CEP volume velocity variety incompleteness noise reactive answers fine-grained information access complex domain models high-level languages SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 11
  12. 12. State of the art: Semantic Web • Given ontology O and query Q, use O to rewrite Q as Q’ so that, for any set of ground facts A contained in multiple databases: – answer(Q,O,A) = answer(Q’,,A) The answer of the query Q using the ontology O for any set of ground facts A is equal to answer of a query Q’ without considering the ontology O • Use mapping M to map Q’ to multiple SQL queries to the various databases Rewrite O Q Q’ Map SQL M answer A UiO, Norway - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 12
  13. 13. Solutions vs. requirements Requirement DSMS CEP Sem Web volume velocity variety incompleteness noise reactive answers fine-grained information access complex domain models high-level languages SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 13
  14. 14. Stream Reasoning • Research question – is it possible to make sense in real time of multiple, heterogeneous, gigantic and inevitably noisy and incomplete data streams in order to support the decision processes of extremely large numbers of concurrent users? SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 14 Emanuele Della Valle: On Stream Reasoning. PhD thesis, Vrije Universiteit Amsterdam, 2015. Available online at http://dare.ubvu.vu.nl/handle/1871/53293 .
  15. 15. Is this feasible? • Proposed approach: cascading Stream Reasoning Complexity Raw Stream Processing Semantic Streams DL-Lite DLAbstraction Selection Interpretation Reasoning Querying Re-writing Change Frequency PTIME NEXPTIME 104 Hz 1 Hz Complexity vs. Dynamics AC0 H. Stuckenschmidt, S. Ceri, E. Della Valle, F. van Harmelen: Towards Expressive Stream Reasoning. Proceedings of the Dagstuhl Seminar on Semantic Aspects of Sensor Networks, 2010. SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 15
  16. 16. A model to describe stream reasoning SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 16 D. Dell’Aglio, On Unified Stream Reasoning, PhD thesis, Politecnico di Milano, 2016. Stream Processing (DSMS) Event Processing (CEP)Window merge Window operator Streams Graph-level entailment Window-level entailment Stream-level entailment Application StreamReasoning
  17. 17. GRAPH LEVEL UNDER SIMPLE ENTAILMENT REGIME SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 17
  18. 18. State-of-the-art: RDF model • RDF: Resource Description Framework – It allows to make statements about resources in the form of subject-predicate-object expressions • In RDF terminology triples • E.g. @TimBernersLee posts "This is for everyone" – A collection of RDF statements represents a labelled, directed graph • In RDF terminology a graph • E.g., the tweet above by Tim Berners Lee is connected to – Thousands of twitter user profiles via retweets – Thousands of twitter user profiles via favorite – … subject predicate object UiO, Norway - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 18
  19. 19. RDF stream Models • RDF Stream (the C-SPARQL way) – Unbound sequence of time-varying triples – each represented by a pair made of an RDF triple and its timestamp – Timestamp are non-decreasing (allowing for simultaneity) … TimBernersLee posts "This is for everyone", 10:16.55 PM 22 Aug 2012 @Alice posts "RT: This is for everyone", 10:17.03 PM 22 Aug 2012 … D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, M. Grossniklaus: Querying RDF streams with C-SPARQL. SIGMOD Record 39(1): 20-26 (2010) subject predicate object timestamp SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 19
  20. 20. RDF stream Models • RDF Stream (the Streaming Linked Data way) – Unbound sequence of time-varying graphs – each represented by a pair made of an RDF graph and its timestamp (all triples in a graph are simultaneous) – Timestamps (if present) are monotonically increasing – Graphs act as a form of punctuation SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org A. Mauri, J-P Calbimonte, D.Dell'Aglio, M.Balduini, M.Brambilla, E.Della Valle, K. Aberer: TripleWave: Spreading RDF Streams on the Web. ISWC (2) 2016: 140-149 20 D.F. Barbieri, E. Della Valle: A Proposal for Publishing Data Streams as Linked Data – A Position Paper. LDOW (2010)
  21. 21. Continuous-SPARQL SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 21
  22. 22. Continuous-SPARQL Who are the opinion makers? i.e., the users who are likely to influence the behavior their followers REGISTER STREAM OpinionMakers COMPUTED EVERY 5m AS CONSTRUCT { ?opinionMaker sd:about ?resource } FROM STREAM <http://…> [RANGE 30m STEP 5m] WHERE { ?opinionMaker ?opinion ?res . ?follower sioc:follows ?opinionMaker. ?follower ?opinion ?res. FILTER (cs:timestamp(?follower ?opinion ?res) > cs:timestamp(?opinionMaker ?opinion ?res) ) } HAVING ( COUNT(DISTINCT ?follower) > 3 ) SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 22D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, M. Grossniklaus: Querying RDF streams with C-SPARQL. SIGMOD Record 39(1): 20-26 (2010)
  23. 23. Continuous-SPARQL Who are the opinion makers? i.e., the users who are likely to influence the behavior their followers REGISTER STREAM OpinionMakers COMPUTED EVERY 5m AS CONSTRUCT { ?opinionMaker sd:about ?resource } FROM STREAM <http://…> [RANGE 30m STEP 5m] WHERE { ?opinionMaker ?opinion ?res . ?follower sioc:follows ?opinionMaker. ?follower ?opinion ?res. FILTER (cs:timestamp(?follower ?opinion ?res) > cs:timestamp(?opinionMaker ?opinion ?res) ) } HAVING ( COUNT(DISTINCT ?follower) > 3 ) Query registration (for continuous execution) FROM STREAM clause WINDOW RDF Stream added as new ouput format Builtin to access timestamps SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 23D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, M. Grossniklaus: Querying RDF streams with C-SPARQL. SIGMOD Record 39(1): 20-26 (2010)
  24. 24. More than modeling, it's for reactive answers! • C-SPARQL engine time window-based selection outperforms SPARQL filter-based selection (Jena-ARQ) 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. Our In-memory RDF stream processing engine SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 24
  25. 25. Alternatives to C-SPARQL • SPARQLStream – What: window in the past, focus on RDF to Stream operators – Ref: Calbimonte, J.-P., Corcho, O., & Gray, A. J. G. Enabling ontology-based access to streaming data sources. In ISWC, 2010, pages 96–111. • CQELS – What: STREAM clause, focus on new answer – Ref: Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., & Hauswirth, M. A native and adaptive approach for unified processing of linked streams and linked data. In ISWC 2011, pages 370–388. • EP-SPARQL – What: focus on event specific operators – Ref: Anicic, D., Fodor, P., Rudolph, S., & Stojanovic, N. EP-SPARQL: a unified language for event processing and stream reasoning. In WWW 2011, pages 635–644. • TEF-SPARQL – What: adds "facts" as first class elements – Ref: https://www.merlin.uzh.ch/publication/show/8467 SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 25
  26. 26. Work in progress • RSP-QL – Syntax • https://github.com/streamreasoning/RSP-QL/blob/master/RSP- QL%20Sample%20Queries.md – Proposed semantics • D.Dell'Aglio, E.Della Valle, J.-P.Calbimonte, Ó. Corcho: RSP-QL Semantics: A Unifying Query Model to Explain Heterogeneity of RDF Stream Processing Systems. Int. J. Semantic Web Inf. Syst. 10(4): 17-44 (2014) – Semantics (work in progress) • https://github.com/streamreasoning/RSP- QL/blob/master/Semantics.md – Quick ref. • D. Dell'Aglio, J.-P. Calbimonte, E. Della Valle, Ó. Corcho: Towards a Unified Language for RDF Stream Query Processing. ESWC (Satellite Events) 2015: 353-363 SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 26
  27. 27. GRAPH LEVEL UNDER OTHER ENTAILMENT REGIME SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 27
  28. 28. continuous deductive reasoning • DL Ontology Stream ST – A ontology stream with respect to a static Tbox T is a sequence of Abox axioms ST(i) • A Windowed Ontology Stream ST(o,c] – A windowed ontology stream with respect to a static Tbox T is the union of the Abox axioms ST(i) where o<i≤c • Reasoning on a Windowed Ontology Stream ST(o,c] is as reasoning on a static DL KB SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 28 Emanuele Della Valle, Stefano Ceri, Davide Francesco Barbieri, Daniele Braga, Alessandro Campi: A First Step Towards Stream Reasoning. FIS 2008: 72-81
  29. 29. discusses discusses discusses discusses discusses discusses discusses Example of continuous deductive reasoning What impact has been my micropost p1 creating in the last hour? Let’s count the number of microposts that discuss it … REGISTER STREAM ImpactMeter AS SELECT (count(?p) AS ?impact) FROM STREAM <http://…/fb> [RANGE 60m STEP 10m] WHERE { :Alice posts [ sr:discusses ?p ] } p1 p3 p5 p8 p2 p4 p7 p6 Transitive property Alice posts p1 . SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 29
  30. 30. Not so naïve approach to stream reasoning • The problem is that materialization (the result of data-driven processing) are very difficult to decrement efficiently. – State-of-the-art: DRed algorithm • Over delete • Re-derive • Insert Reasoner Inferred data ontology window insertions deletions Incremental !!! SPARQL SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 30
  31. 31. Is DRed needed? • DRed works with random insertions and deletions • In a streaming setting, when a triple enters the window, given the size of the window, the reasoner knows already when it will be deleted! • E.g., – if the window is 40 minutes long, and, – it is 10:00, the triple(s) entering now – will exit on 10:40. • Conclusion – deletions are predictable Time Enter window Exit window Explicitly in window Infer win 10:00 AßB 10:10 BßC 10:20 AßE 10:30 EßC 10:40 AßB 10:50 BßC 11:00 AßE A B A B C A A B C E A A B C E A A C E A A B C E A C E SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 31
  32. 32. IMaRS algorithm • Idea: – add an expiration time to each triple and – use an hash table to index triples by their expiration time • The algorithm 1. deletes expired triples 2. Adds the new derivations that are consequences of insertions annotating each inferred triple with an expiration time (the min of those of the triple it is derived from), and 3. when multiple derivations occur, for each multiple derivation, it keeps the max expiration time. SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 32
  33. 33. IMaRS algorithm • Incremental Reasoning on RDF streams (IMaRS): new reasoning algorithm optimized for reactive query answering D.F. Barbieri, D. Braga, S.Ceri, E. Della Valle, M. Grossniklaus: Incremental Reasoning on Streams and Rich Background Knowledge. ESWC (1) 2010: 1-15 D. Dell'Aglio, E. Della Valle: Incremental Reasoning on RDF Streams. In A.Harth, K.Hose, R.Schenkel (Eds.) Linked Data Management, CRC Press 2014, ISBN 9781466582408 § Re-materialize after each window slide § Use DRed § IMaRS % of deletions w.r.t. the content of the window SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 33
  34. 34. IMaRS algorithm • comparison of the average time needed to answer a C-SPARQL query, when 2% of the content exits the window each time it slides, using – A backward reasoner on the window content – DRed + standard SPARQL on the materialization – IMaRS + standard SPARQL on the materialization SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 34
  35. 35. Some alternatives to IMaRS • TROWL – How: DRed in the context of approximate reasoning – Ref: Y. Ren, J. Z. Pan. Optimising ontology stream reasoning with truth maintenance system. In CIKM (2011) • Sparkwave – How: extended RETE algorithm for windows and RDFS – Ref: Sparkwave: Continuous Schema-Enhanced Pattern Matching over RDF Data Streams. Komazec S, Cerri D. DEBS 2012 • The Backward/Forward Algorithm – How: optimizing DRed – B. Motik, Y. Nenov, R.E.F. Piro, I. Horrocks: Incremental Update of Datalog Materialisation: the Backward/Forward Algorithm. AAAI 2015: 1560-1568 SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 35
  36. 36. WINDOW LEVEL ENTAILMENT SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 36
  37. 37. window level entailment • Graph-level entailment considers data item contents, but it does not use the temporal annotations • Window-level entailment applies the inference process on the non-merged stream items. • E.g., – A door cannot be open and close at the same time – A window contains: door A is open @1, door A is close @2 – At graph-level the reasoner tells that there is an inconsistency in the window because it ignore the parts in italics – At window-level the reasoner does not • Best approaches I saw so far – ÖL Özçep, R Möller. Ontology Based Data Access on Temporal and Streaming Data. Reasoning Web, 2014 – D. Dell'Aglio et al. :A Query Model to Capture Event Pattern Matching in RDF Stream Processing Query Languages EKAW 2016: 145-162 SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 37
  38. 38. STREAM LEVEL ENTAILMENT SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 38
  39. 39. Stream-level entailment • Window-level entailment only considers a recent portion of the stream • Stream-level entailment aims at considering the entire stream • This is not just a theoretical dream, CEP does so – E.g., rise C for every A that follows a B without a C in the middle • Best approaches I saw so far – ETALIS • Anicic, D., Rudolph, S., Fodor, P., & Stojanovic, N. Stream reasoning and complex event processing in ETALIS Semantic Web,3(4), 2012, 397–407. – LARS • H. Beck, M. Dao-Tran, T. Eiter, M. Fink: LARS: A Logic-Based Framework for Analyzing Reasoning over Streams. AAAI 2015: 1431-1438H. SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 39
  40. 40. OPEN PROBLEMS AND CHALLENGES FOR THE NEXT YEARS SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 40
  41. 41. Requirements vs. state-of-the-art Requirement SotA@2017Q2 volume velocity variety incompleteness noise reactive answers fine-grained information access complex domain models high-level languages SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 41
  42. 42. Towards queries that capture user needs • Capturing a wider set of tasks in queries – Descriptive analytics • Which electricity-producing turbine has sensor readings similar (i.e., Pearson correlated by at least 0.75) to any turbine that subsequently had a critical failure in the past year? – Predictive analytics (Machine Learning) • Where am I likely going to run into a traffic jam during my commute tonight and how long will it take, given current weather and traffic conditions? – Geo-spatial reasoning – Including preferences – Exploiting the graph nature of the data SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 42
  43. 43. Towards queries that capture user needs • Tightening CEP and ontological reasoning – ETALIS/EP-SPARQL is the corner stone with high- expressivity but very low efficiency – Work in progress: SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 43 CEP DL RDF STREAM PROCESSING RAW STREAM PROCESSING Abstraction Selection Annotation Reasoning Querying Rewriting R. Tommasini, P. Bonte, E. Della Valle, E. Mannens, F. De Turck, F. Ongenae: Towards Ontology-Based Event Processing. OWLED 2016: 115-127
  44. 44. Towards queries that capture user needs • Forgetting knowledge – Is IMaRS semantics what users want? – Consumption ≠ expiration • Consumed facts are not useful for processing • Expired facts are not true anymore – Should we introduce • a notion of semantic importance? • a notion of time-annotated facts? • … SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 44
  45. 45. Towards sophisticated Stream Reasoning • Extending the range of logical formalisms – Pioneers: • Metric Temporal logic – F. Heintz and P. Doherty, DyKnow: An approach to middleware for knowledge processing, Journal of Intelligent and 11 Fuzzy Systems 15(1) (2004), 3–13. • Answer Set Programming (ASP) – A. Mileo, A. Abdelrahman, S. Policarpio and M. Hauswirth, StreamRule: A nonmonotonic stream reasoning system for the semantic web, in: RR, LNCS, Vol. 7994, Springer, 2013, pp. 247–252. – More alternatives: • action logics • step logics • active logics • event calculus SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 45
  46. 46. Towards sophisticated Stream Reasoning • Integrating other types of reasoning/processing – My contribution SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 46 D.F. 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 25(6): 32-41 (2010)
  47. 47. Towards sophisticated Stream Reasoning • Integrating other types of reasoning/processing – Other options • More on inductive stream reasoning • Probabilistic reasoning • Planning • Natural Language Processing • Sentiment analysis • … SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 47
  48. 48. Towards sophisticated Stream Reasoning • Towards semantic streams – Describe data stream to make them discoverable, composable, etc. – My work in progress • Y. A. Sedira, R.Tommasini, E. Della Valle: Towards VoIS: A Vocabulary of Interlinked Streams. DeSemWeb@ISWC 2017 – There is much more to describe, e.g. • Stream of state (the door is close) vs. Stream of actions (the door was closed) SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 48
  49. 49. Towards sophisticated Stream Reasoning • Towards scalable stream reasoners – Rewriting • e.g., MorphStream – How: rewriting in DSMS languages (one at a time) – Ref: Calbimonte, J.-P., Corcho, O., & Gray, A. J. G. Enabling ontology- based access to streaming data sources. In ISWC, 2010, pages 96–111. – Using parallelization and distribution • e.g., DynamiTE – How: Truth maintenance for DF (a fragment of RDFS) – J. Urbani, A. Margara, C. J. H. Jacobs, F. van Harmelen, H.E. Bal: DynamiTE: Parallel Materialization of Dynamic RDF Data. ISWC (1) 2013: 657-672 – Using approximation • e.g., TR-OWL – How: Truth maintenance for EL++ with syntactic approximations – Ref: Y. Ren, J. Z. Pan. Optimising ontology stream reasoning with truth maintenance system. In CIKM (2011) SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 49
  50. 50. Towards robustness to imperfect data • Overcoming types of heterogeneity that do not exist in the static reasoning settings – It is not just extending OBDA to continuous queries – There are more sources of heterogeneity than in the static database settings • Different execution semantics • No possibility for a priori homogenous – Synchronisation* – discretization • … * Pioneered in F.Heintz, DyKnow: A stream Based Knowledge Processing Middleware Framework, PhD thesis,Linköping University, Sweden, 2009. SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 50
  51. 51. Towards robustness to imperfect data • Copying with noise – Data as signals instead of data as truths! – Easy to state, hard to achieve – Still of paramount importance! – Notable approaches I run into • Matthias Nickles, Alessandra Mileo: Web Stream Reasoning Using Probabilistic Answer Set Programming. RR 2014: 197-205 • Anastasios Skarlatidis, Georgios Paliouras, Alexander Artikis, George A. Vouros: Probabilistic Event Calculus for Event Recognition. ACM Trans. Comput. Log. 16(2): 11:1-11:37 (2015) • Anni-Yasmin Turhan, Erik Zenker: Towards Temporal Fuzzy Query Answering on Stream-based Data. HiDeSt@KI 2015: 56-69 SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 51
  52. 52. Conclusions SR 2015, Austria - 3.11.2015 @manudellavalle - http://emanueledellavalle.org 52 Stream Processing Event ProcessingWindow merge Window operator Streams Graph-level entailment Window-level entailment Stream-level entailment Application StreamReasoning Very well only some initial studies still very open New abstractions to capture user needs Application
  53. 53. Stream Reasoning: a summary of ten years of research and a vision for the next decade http://content.iospress.com/articles/data-science/ds006 Q/A Emanuele Della Valle DEIB - Politecnico di Milano @manudellavalle emanuele.dellavalle@polimi.it http://emanueledellavalle.org Linköping University, Sweden - 5.10.2017

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