Semantic Discovery & Integration of Urban Data Streams. Feng Gao, Ali Intizar and Alessandra Mileo

Transcription

1 Semantic Discovery & Integration of Urban Data Streams Feng Gao, Ali Intizar and Alessandra Mileo

2 Smart City Applications- Overview 2 *

3 Smart City Applications - IoE 3

4 Smart City Applications - Infrastructure Physical Sensors 4

5 Smart City Applications - Infrastructure Mobile/Wearable Sensors 5

6 Smart City Applications - Infrastructure Virtual Sensors (Social Media) 6

7 Smart City Applications - City Pulse 7

8 Smart City Applications - Challenges Federation of Heterogeneous Data Streams 8

9 Smart City Applications - Challenges Real-time Information Extraction and Event Detection 9

10 Smart City Applications - Challenges Reliable Information Processing 10

11 Smart City Applications - Challenges Federation of Heterogeneous Data Streams 11

12 Smart City Applications - Challenges Virtualisation Federation of heterogeneous data streams Processing user s queries in terms of requirements rather than hard-bind queries Optimal data source selection while taking users constraints and preferences into account Automated composition of primitive data services/streams into complex events Automated generations of queries from the complex event s composition plan 12

13 ACEIS - Features Automated Complex Event Implementation System Enables users to provide requirements rather than hard bind streams Automatically discovers the relevant data streams Selects optimal data stream after evaluating user s constraints and preferences On demand data federation using complex event patterns Transformation of complex event into stream queries 13

14 ACEIS - Architecture Knowledge Base Application Interface User Input QoI/QoS ACEIS Core Resource Management Event Request Resource Discovery Event Service Composer Stream Description Composition Plan Adaptation Manager Data Mgmt, Indexing, Caching Data Federation Query Results Subscription Manager Query Transformer Query Engine Constraint Validation Constraint Violation Semantic Annotation Data Store IoT Data Stream Social Data Stream 14

15 Complex Event Service Ontology Overview (1/2) The Complex Event Service Ontology (CES ontology) is an extension of OWL-S ontology. CES ontology is used together with SSN (Semantic Sensor Network) ontology, SSN is used to describe the sensor aspects An Event Service is described with a Grounding and an Event Profile. 1. Groundings specify how to access and interact with event services. 2. Event Profiles describe the events provided by the services with Patterns and Non-Functional Properties (NFP). An Event Request is specified as an incomplete Event Service description, without concrete service bindings. 15

16 Complex Event Service Ontology Overview (2/2) Constraint hasconstraint EventRequest owls:service owls:supports owls:presents owls:grounding haspreference EventService owls:serviceprofile Preference QosWeight Preference rdf:_x (contains) PrimitiveEvent Service rdf:_x (contains) ComplexEven tservice owls:presents owls-sp:serviceparameter owls-sp:serviceparameter hasweight Pattern haspattern EventProfile hasnfp NFP xsd:double rdf:_x (contains) Namespaces: default: < rdf: < owls: < owls-sp: < Legend: Class Object property subclassof Data property 16

17 Complex Event Service Ontology Event Pattern EventService rdf:_x (contains) ComplexEvent Service owls:presents EventProfile Namespaces: default: < rdf: < owls: < Sequence rdf:_x (contains) haspattern Or rdf:seq Pattern rdf:bag Repetition And hasfilter haswindow Aggregation ValueAssignment Filter SlidingWindow Legend: Class onevent onpayload hasexpression EventService EventPayload Expression Class (unimplemented) Object property subclassof 17

18 Stream Discovery Sensor Stream Annotation A sensor service description is annotated as: s desc = (t d, g, q d, P d, FoI d, f d ) type grounding QoS Listing 1. Tra Observed Properties c sensor service description P d FoI d Feature Of Iterest :sampletrafficsensor a ssn:sensor,ces:primitiveeventservice; owls:presents :sampleprofile ; owls:supports :samplegrounding; ssn:observes [ a ces:averagespeed; ssn:ispropertyfor :Seg_1], [ a ces:vehiclecount; ssn:ispropertyfor :Seg_1], [ a ces:estimatedtime; ssn:ispropertyfor :Seg_1]. :sampleprofile a ces:eventprofile ; owls:servicecategory [ a ces:trafficreportservice ; owls:servicecategoryname "traffic_report"^^xsd:string]. 18

19 ACEIS - Architecture Knowledge Base Application Interface User Input QoI/QoS ACEIS Core Resource Management Event Request Resource Discovery Event Service Composer Stream Description Composition Plan Adaptation Manager Data Mgmt, Indexing, Caching Data Federation Query Results Subscription Manager Query Transformer Query Engine Constraint Validation Constraint Violation Semantic Annotation Data Store IoT Data Stream Social Data Stream 19

20 Stream Discovery Event Request Annotation Similarly, a sensor service request is annotated: s r = (t r, P r, FoI r, f r, pref, C) type Requested Properties Listing 2. Tra Feature of P d FoI d Interest c sensor service request :samplerequest a ssn:sensor,ces:eventrequest; owls:presents :requestprofile ; ssn:observes [ a ces:estimatedtime; ssn:ispropertyfor :Seg_1]; ces:hasconstraint [ rdf:type ces:nfpconstraint; ces:onproperty ces:availability; ces:hasexpression [ emvo:greaterthan "0.9"^^xsd:double]], [ rdf:type ces:nfpconstraint; ces:onproperty ces:accuracy; ces:hasexpression [ emvo:greaterthan "0.9"^^xsd:double]]. :requestprofile a ces:eventprofile ; owls:servicecategory [ a ces:trafficreportservice ; owls:servicecategoryname "traffic_report"^^xsd:string]. 20

21 Stream Discovery Event Request Annotation Similarly, a sensor service request is annotated: s r = (t r, P r, FoI r, f r, pref, C) no grounding type Requested Properties Listing 2. Tra Feature of P d FoI d Interest c sensor service request NFP Constraint&Prefer ences :samplerequest a ssn:sensor,ces:eventrequest; owls:presents :requestprofile ; ssn:observes [ a ces:estimatedtime; ssn:ispropertyfor :Seg_1]; ces:hasconstraint [ rdf:type ces:nfpconstraint; ces:onproperty ces:availability; ces:hasexpression [ emvo:greaterthan "0.9"^^xsd:double]], [ rdf:type ces:nfpconstraint; ces:onproperty ces:accuracy; ces:hasexpression [ emvo:greaterthan "0.9"^^xsd:double]]. :requestprofile a ces:eventprofile ; owls:servicecategory [ a ces:trafficreportservice ; owls:servicecategoryname "traffic_report"^^xsd:string]. 21

22 ACEIS - Architecture Knowledge Base Application Interface User Input QoI/QoS ACEIS Core Resource Management Event Request Resource Discovery Event Service Composer Stream Description Composition Plan Adaptation Manager Data Mgmt, Indexing, Caching Data Federation Query Results Subscription Manager Query Transformer Query Engine Constraint Validation Constraint Violation Semantic Annotation Data Store IoT Data Stream Social Data Stream 22

23 Stream Discovery Matching Condition A sensor service description S d matches a service request S r iff the following three conditions are true: 1. t r subsumes t d : 2. q d satifies C: 3. p 1 P r, p 2 P d T(p 1 ) subsumes T(p 2 ) f r (p 1 ) = f d (p 2 ): 23

24 Stream Discovery Matching Condition A sensor service description S d matches a service request S r iff the following three conditions are true: 1. t r subsumes t d : Requested service type is same or a super-type of provided service type. 2. q d satifies C: Quality-of-service properties of the provided sensor service satisfy the constraints specified in the service request. 3. p 1 P r, p 2 P d T(p 1 ) subsumes T(p 2 ) f r (p 1 ) = f d (p 2 ): Provided sensor service observes all requested physical properties from the requested feature-of-interest (a geographical location or physical entity from which the observations are made). 24

25 ACEIS - Architecture Knowledge Base Application Interface User Input QoI/QoS ACEIS Core Resource Management Event Request Resource Discovery Event Service Composer Stream Description Composition Plan Adaptation Manager Data Mgmt, Indexing, Caching Data Federation Query Results Subscription Manager Query Transformer Query Engine Constraint Validation Constraint Violation Semantic Annotation Data Store IoT Data Stream Social Data Stream 25

26 Stream Integration Complex Event Service A Complex Event Service (CES) integrates different sensor streams to detect complex events based on event patterns. An Event Pattern describes the correlations of integrated streams. Listing 3. Complex event service request :SampleEventRequest a ces:eventrequest; owls:presents :SampleEventProfile. :SampleEventProfile rdf:type owls:eventprofile; ces:haspattern [ rdf:type ces:and, rdf:bag; rdf:_1 :locationrequest; rdf:_2 :seg1congestionrequest; rdf:_3 :seg2congestionrequest; rdf:_4 :seg3congestionrequest; ces:haswindow "5"^^xsd:integer]. 26

27 Stream Integration Matching condition Discovery and composition of complex event services are based on matching event patterns (and aggregated NFP values). Procedure: 1. Derive canonical forms of event patterns of CESs. 2. Apply tree isomorphism algorithms over the canonical event patterns and the requested event pattern to identify reusable or equivalent event patterns. 3. Generate all possible composition plans. 4. Aggregate NFPs based on event patterns and compare aggregated NFP values against requested constraints to filter out unsatisfied composition plans. 5. Rank the remaining composition plans based on preferences (soft constraints). 27

28 Canonical Event Pattern (1/2) Create complete event patterns ES1 Or getcompletepattern() Or ES2 ES3 And Seq ES2 And Seq ES3 e3 e4 e3 e4 28

29 Canonical Event Pattern (2/2) Create reduced event patterns SEQ Sequential Lift SEQ Sequential Merge SEQ SEQ SEQ e3 REP x2 e3 e3 AND Parallel Lift AND Parallel Merge AND AND REP x2 Repetition Lift (1) REP x6 REP x2 Repetition Lift (2) REP x2 Repetition Merge REP x2 REP x3 SEQ REP x2 29

30 Event Composition via Reusability Create event reusability hierachy Reusable relation: R(ep 1,ep 2 ) holds if R d (ep 1,ep 2 ) or R i (ep 1,ep 2 ) holds. OR SEQ e4 e3 directly reusable in-directly reusable SEQ SEQ e3 in-directly reusable e3 30

31 Stream Integration Composition Plan Example of a composition plan: Query OR Event Service 1 Event Service 2 type= loc=loc1 type= loc=loc2 SEQ e3 Event Service 3 Event Service 4 type= e3 type= e4 loc=loc3 loc=loc4 e3 SEQ Composition Plan OR e4 e3 loc=loc4 loc=loc3 31

32 ACEIS - Architecture Knowledge Base Application Interface User Input QoI/QoS ACEIS Core Resource Management Event Request Resource Discovery Event Service Composer Stream Description Composition Plan Adaptation Manager Data Mgmt, Indexing, Caching Data Federation Query Results Subscription Manager Query Transformer Query Engine Constraint Validation Constraint Violation Semantic Annotation Data Store IoT Data Stream Social Data Stream 32

33 Query Transformation Semantic Alignment Goal: transform the composition plan into a stream query which can be evaluated by a stream reasoning engine over RDF data streams Requirements: Alignments of event pattern operators to stream query operators Transformation Algorithm Alignments for CQELS query language: Event Pattern s d Sequence Repetition And Or Selection Filter Window CQELS Operator SGP - - Join Optional Projection Filter Window Sequence and Repetition not supported by CQELS. Sensor requests mapped to Stream Graph Pattern. AND operator mapped to stream join. OR operator mapped to OPTIONAL keyword (left-outer-join). 33

34 Query Transformation Transformation Example Example of composition plan AND Event textual description: user loc seg1 traffic seg2 traffic seg3 traffic Monitor the user's current location as well as the traffic conditions (estimated travel time) of all the 3 street segments on the chosen route CQELS Query Transformation Result: Select... Where { Graph < {?ob rdfs:subclassof ssn:observation} Stream <locationstreamurl > [range 5s] {?locid rdf:type?ob.?locid ssn:observedby?es4.?locid ssn:observationresult?result1.?result1 ssn:hasvalue?valu.?valu ct:haslongtitude?lon.?valu ct:haslatitude?lat.?loc ct:haslongtitude?lon. } Stream <trafficstreamurl1 > [range 5s] {?seg1id rdf:type?ob.?seg1id ssn:observedby?es1.?seg1id ssn:observationresult?result2.?result2 ssn:hasvalue?valu.?valu ssn:hasquantityvalue?eta1.} Stream <trafficstreamurl2 > [range 5s] {...} Stream <trafficstreamurl3 > [range 5s] {...} } 34

35 Conclusion Automated Complext Event Implementation System Information model for dynamic discovery and selection of sensor data streams i.e. Complex Event Ontology, Event Pattern Ontology and Event Reuest/Profile Algorithm to create optimal composition plan using event reusability heirachy Transfoormation of the composition plan into stream queries (CQELS) 35