Towards Ontology Based Event Processing

Transcription

1 Towards Ontology Based Event Processing RISE SICS, Electrum Kista Stockholm, Sweden R. Tommasini - Politecnico di Milano 1

2 ME PhD Politecnico di Milano Research Interests: -Semantic Web & Reasoning -Stream Processing -Programming Languages -Distributed rictomm.me rictomm@gmail.com!2

3 My Advisor - Assistant Professor at DEIB Politecnico di Milano - Expert in semantic technologies and stream computing - Brander of stream reasoning - 17 years of experience in research and innovation projects - Startupper: h2p://emanueledellavalle.org h2p://streamreasoning.org h2p://fluxedo.com!3

4 What is Stream Reasoning?!4

5 Can we detect fire? *Expected Answer: YES!5

6 Can we (actually) detect fire? Expected Reaction: Perplexed Audience!6

7 !7

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11 !11

12 70%!12

13 30%!13

14 Summary Workarounds Humidity Variations (decreases) OWLED 16 Smoke Detection Temperature Variations (increases)!14

15 This is Stream Reasoning!!15

16 Structural Heterogeneity!16

17 Semantic Heterogeneity!17

18 Incomplete IBM Riccardo Tommasini - rictomm.me - rictomm@gmail.com!18

19 Vast IBM Riccardo Tommasini - rictomm.me - rictomm@gmail.com!19

20 Noisy IBM Riccardo Tommasini - rictomm.me - rictomm@gmail.com!20

21 Complex Domain IBM Riccardo Tommasini - rictomm.me - rictomm@gmail.com!21

22 Can we make sense in real-time of heterogeneous, vast, incomplete, and inevitably noisy and data streams in order to support the decision processes of extremely large numbers of concurrent users?!22

23 handle massive datasets Requirement Analysis x process data streams cope with heterogeneous data cope with incomplete data cope with noisy data provide reactive answers access fine-grained information Volume Velocity model complex domains x x x x x x Variety x x x Veracity!23

24 Stream Processing vs Semantic Technologies Requirement SP ST massive datasets data streams heterogeneous dataset incomplete data noisy data reactive answers fine-grained information access complex domain models!24

25 Stream Reasoning Stuckenschmidt, H., Ceri, S., Della Valle, E., & Van Harmelen, F. (2010). Towards expressive stream reasoning!25

26 Cascading Reasoning LOGIC OWLED 16 DL Complexity 2NEXPTime Rewriting Querying Reasoning PROGRAMMING RDF STREAM PROCESSING Matching Selection Interpretation RAW STREAM PROCESSING PTime 10 4 Hz 1 Hz Change Frequency Stuckenschmidt, H., Ceri, S., Della Valle, E., & Van Harmelen, F. (2010). Towards expressive stream reasoning!26

27 RDF Stream Processing (RSP) Continuous Data Integration!27

28 RDF Streams sequence of pairs (Gi,ti) where OWLED -An RDF Stream is an partially ordered 16 -Gi, is a [named] RDF graph and -ti is a timestamp.!28

29 An Example ( { :s1 :observes :o2 ; :o2 :value 20C }, 2) OWLED ( { :s1 :observes :o1 ; :o1 :value 20C 16 }, 1) ( { :s1 :observes :o3 ; :o3 :value 30C }, 3) ( { :s1 :observes :o4 ; :o4 :value 50C }, 4)!29

30 RSEP-QL - Extends CQL to process RDF Graphs OWLED - A Reference Model fo Continuous SPARQL 16 - Introduces the notions of Window and Event Pattern!30

31 An Example CONSTRUCT {?o a :FireObservation ; :sensedby?s.} OWLED REGISTER STREAM <fire> 16 FROM NAMED WINDOW <w1> [RANGE 5m,STEP 5m] ON STREAM <temp> WHERE { WINDOW <w1> {?s :observes?o ;?o :value?t FILTER (?t > 50C) }}!31

32 Continuous Reasoning Deductive!32

33 Ontology Streams sequence of pairs (Ai,ti) where OWLED -An Ontology Stream is an partially ordered 16 -Ai, is a set of a ABox axioms w.r.t. a static TBox T. -ti is a timestamp.!33

34 Windowed Ontology Streams of all the Abox axioms Sets Ai with o<i<c OWLED 16 -An Windowed Ontology Stream S[o,c] is the union -Continuous Reasoning can be reduced to traditional ontological reasoning over a windowed ontology stream!34

35 Ontology Based Event Processing Joint work with P.Bonte, E. Mannens, F. De Turck, F. Ongenae!35

36 Cascading Reasoning Approach DL OWLED 16 CEP Complexity 2NEXPTime Rewriting Querying Reasoning RDF STREAM PROCESSING Matching Selection Interpretation RAW STREAM PROCESSING PTime 10 4 Hz 1 Hz Change Frequency Stuckenschmidt, H., Ceri, S., Della Valle, E., & Van Harmelen, F. (2010). Towards expressive stream reasoning!36

37 Data Integration (Gi, ti) We assume RDF Stream as common data model Time!37

38 Logical Event Events! first-class objects in the language DL Reasoning `Physical Event!38

39 EVENT OfficeTemperaturEvent subclassof TemperaturEvent and (observationresult some (hasvalue >= 40)) and (haslocation some Office) Logical Modeling Logical Event Specifications!39

40 EVENT FireEvent { MATCH TemperaturEvent SEQ SmokeDetectionEvent WITHIN (5m) } Semantic Complex Event Processing Patterns!40

41 EVENT FireEvent { MATCH TemperaturEvent SEQ SmokeDetectionEvent WITHIN (5m) IF { Semantic Complex Event Processing EVENT TemperaturEvent {?loc0 hasvalue?v} EVENT SmokeDetectionEvent {?loc1 hasvalue?v In OBEP FILTER (?smokelevel == 3) }}!41

42 Future Works!42

43 Ontology Based Streaming Data Access!43

44 Cascading Reasoning DL OWLED 16 CEP Complexity 2NEXPTime Rewriting Querying Reasoning RDF STREAM PROCESSING Matching Selection Interpretation RAW STREAM PROCESSING PTime 10 4 Hz 1 Hz Change Frequency Stuckenschmidt, H., Ceri, S., Della Valle, E., & Van Harmelen, F. (2010). Towards expressive stream reasoning!44

45 Rewriting and Interpreting RDF STREAM PROCESSING RAW STREAM PROCESSING - including continuous semantics will enable continuous querying over virtual streaming sources; - including time operators like windows will enable query rewriting into continuous query languages!45

46 Stream Reasoning Applications!46

47 Anatomy of a Streaming Application WASP - Input Streams - Output Streams - Continuous Tasks Web Stream Processing Application!47

48 The Web is Streaming!48

49 VoCaLS - Vocabulary and Catalog for Linked Streams in a machine readable form OWLED 16 - VOCALS allows to describe streams and streaming endpoints - VOCALS enables stream services description, fostering interoperability between producers and consumers. - VOCALS let track stream transformation provenance describing the continuous tasks operating on streams.!49

50 d!50

51 Questions? Github: riccardotommasini Web1: riccardotommasini.com Web2: streamreasoning.org!51