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This document discusses ontology-based access to sensor data streams. It motivates the need to provide universal web-based access to sensor data as streams. Existing approaches are discussed for querying static sensor data and streaming data using different stream processing engines. The author proposes using ontology models to continuously query real-time sensor data streams. Several hypotheses are presented regarding representing streaming data as ontology instances, extending SPARQL for streaming queries, rewriting ontology-based streaming queries to engine-specific queries using mappings, and evaluating the overhead of query rewriting. The document concludes that the hypotheses were confirmed by enabling ontology-based access to streaming sources and characterizing sensor metadata.

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Ontology-based Access to
Sensor Data Streams
Jean-Paul Calbimonte
Supervisor: Oscar Corcho
Ontology Engineering Group
Facultad de Informática, Universidad Politécnica de Madrid
jp.calbimonte@upm.es
PhD Thesis Defense
18.4.2013
2
Outline
Motivation
Background
Conclusions
Semantic stream query processing
Sensor metadata characterization
Ontology-based Access to Sensor Data Streams
Hypotheses & contributions
Challenges
Motivation
3
from Sensor Networks
to the Sensor Web
and the Semantic Sensor Web
Sensors
4
http://www.flickr.com/photos/wouterh/2409251427/
data capture
different Sensor providers
transmission
. . .. . .
data streams
Sensor Networks and the Web
5
Sensor Networks
users
applications
data streams
Volume
Velocity
Variety WEB
Universal Web-based access to Sensor data
Querying the semantic sensor Web
6
e.g. publish sensor data as RDF/Linked Data?
URIs as names of things
HTTP URIs
useful information when URI
is dereferenced
Link to other URIs
users
applications
WEB
Use ontology models to continuously query real-
time data streams originated from sensors?
1
static vs. streams
one-off vs. continuous

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Phd

  • 1. Ontology-based Access to Sensor Data Streams Jean-Paul Calbimonte Supervisor: Oscar Corcho Ontology Engineering Group Facultad de Informática, Universidad Politécnica de Madrid jp.calbimonte@upm.es PhD Thesis Defense 18.4.2013
  • 2. 2 Outline Motivation Background Conclusions Semantic stream query processing Sensor metadata characterization Ontology-based Access to Sensor Data Streams Hypotheses & contributions Challenges
  • 3. Motivation 3 from Sensor Networks to the Sensor Web and the Semantic Sensor Web
  • 5. Sensor Networks and the Web 5 Sensor Networks users applications data streams Volume Velocity Variety WEB Universal Web-based access to Sensor data
  • 6. Querying the semantic sensor Web 6 e.g. publish sensor data as RDF/Linked Data? URIs as names of things HTTP URIs useful information when URI is dereferenced Link to other URIs users applications WEB Use ontology models to continuously query real- time data streams originated from sensors? 1 static vs. streams one-off vs. continuous
  • 7. Research questions & hypotheses 7 Ontology models to query real-time sensor data streams? Access heterogeneous SPEs using ontologies as an overarching data model? SPARQL streaming extensions for querying data from SPEs (stream processing engines)? 1 H1: Sensor streaming data  instances of an ontology model H2: SPARQL extensions  streaming operators & continuous processing H3: Ontology-based streaming queries  rewritten to relational-based queries using mappings H4: Ontology-based streaming queries  abstract expressions  concrete executable SPE queries H5: Query rewriting  Pull & Push delivery  acceptable overhead
  • 8. Sensor Data: Observations Citizen Science Multiple publishers Heterogeneity Metadata quality 8
  • 10. Characterizing semantic sensor metadata 10 users applications WEB Characterizing sensor data, deriving semantic metadata from the sensor observations 2 different publishers different metadata publish streams Search/query relevant data sources? GSN
  • 11. Research questions & hypotheses 11 Data representation suitable for extracting data features that characterize a set of sensor streams? Classification and mining techniques to characterize sensor data streams? 2 H6: Sensor data series  find characteristic patterns make it recognizable among other types H7: Slope representations  semantic properties such as the type of data  learned with classification techniques  acceptable precision
  • 12. Contributions 12  SPARQL extensions & formalization  rewriting to algebra expressions  using declarative mappings  results data translation  query evaluation pluggable to ≠ SPEs  query rewriting using R2RML mappings  data representation as slope distributions  characterize types of sensor data  classifying sensor time series  extract metadata features  derive semantic properties & R2RML SPARQLStream Sensor metadata characterization QueryingMetadata 2 1
  • 13. Limitations 13 L1: Rewriting  medium sampling throughput, e.g. Env. monitoring L2: Query expressivity  is limited to underlying SPEs’. L3: Adapters  implemented for custom sources. L4: Querying  only simple entailment L5: Arbitrarily noisy sensor series  no accurate characterization. L6: Classification  number of sensor time series in training set L7: Data characterization is not computed in real-time, but offline
  • 14. 14 Outline Motivation Background Conclusions Semantic stream query processing Sensor metadata characterization Ontology-based Access to Sensor Data Streams Hypotheses & contributions Challenges Data Streams Continuous queries Window SPEs Ontology-based data access
  • 15. Sensor data streams & events 15 (temp,hum,pres) τi (36.2,89,4) τimilford1 (35.6,87,4) τi-1 (37.2,88,4) τi+1 watford7 . . . (37.6,88,7) τi (36.3,89,2) τi+1 . . . . . . stream tuples event processing
  • 16. Querying streams & events 16 w1 w2 windows SELECT attribute FROM stream [NOW -10 MIN] streaming tuples Query processor query results database Continuous query processor query push results pull request SPE continuous processingone-off queries
  • 17. Stream Processing Engines (SPE) 17 Data Stream Management Systems (DSMS) Complex Event Processors (CEP) Sensor Data Middleware CQL/Strea m Borealis TelegraphCQ StreamMill Cayuga GEM CEDR NiagaraCQ Rapide CosmHourglass SStreamWare GSN IBM InfoSphere Sybase CEP Microsoft StreamInsight Oracle CEP Esper StreamBase Diverse query languages Different query capabilities Different query models
  • 18. Extracting data from relational databases 18 WEB Ontology-based data access one-off SPARQL queries data as RDF relational database RDB to RDF mappings static data D2R Morph ODEMapster Triplify UltraWrap Mastro R2RML W3C SSN Ontology
  • 19. Summary 19 Existing SPEs available and producing data streams Ontology-based access only for stored data SPARQL query language not suitable for streams SPEs are highly heterogeneous in models and queries
  • 20. 20 Outline Motivation Background Conclusions Semantic stream query processing Sensor metadata characterization Ontology-based Access to Sensor Data Streams Hypotheses & contributions SPARQLStream Challenges Query rewritingRDF Stream Mappings using R2RML Execution over SPEs
  • 21. RDF Streams 21 s,p,o <aemet:observation1, qudt:hasNumericValue, “15.5”> <aemet:observation1, ssn:observedBy, aemet:Sensor3> For streams? ( s,p,o ,τ) (<aemet:observation1, qudt:hasNumericValue, “15.5”>,34532) timestamped triples • Gutierrez et al. (2007) Introducing time into RDF. IEEE TKDE • Rodríguez et al. (2009) Semantic management of streaming data. SSN
  • 22. SPARQLStream extensions 22 SELECT (MAX(?temperature) AS ?maxtemp) ?sensor WHERE { ?obs ssn:observedBy ?sensor. ?obs ssn:observationResult ?res. ?res aemet:hasAirTemperatureValue ?val. ?val qu:numericValue ?temperature. } GROUP BY ?sensor SELECT (MAX(?temp) AS ?maxtemp) ?sensor FROM NAMED STREAM <http://aemet.linkeddata.es/observations.srdf> [NOW-1 HOURS] WHERE { ?obs ssn:observedBy ?sensor. ?obs ssn:observationResult ?res. ?res aemet:hasAirTemperatureValue ?val. ?val qu:numericValue ?temp. } GROUP BY ?sensor SPARQLStream Named streams Time windows Other approaches: Streaming SPARQL (2008), C-SPARQL (2009), CQELS (2011), EP-SPARQL (2011), INSTANS (2012)
  • 23. Streaming SPARQL execution approaches 23 Extend RDF for streaming data Extend SPARQL for streaming RDF Use a SPE internally for evaluation Query rewriting to SPEs RDF Streaming engine from scratch Logic-programming based query evaluation ~Similarities Divergence streams DSMSs CEPs Middleware SPARQLStream
  • 24. Mapping SPE schemas and ontologies 24 wan7 timed: datetime PK sp_wind: float timed sp_wind 1 3.4 2 5.6 3 11.2 4 1.2 5 3.1 .. … Queries SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10 SPE SPE data schemas ssn:Observation Ontology models SPARQLStream Queries Stream-to-ontology mappings SELECT ?wspeed FROM STREAM <SensorReadings.srdf> [NOW–5 HOUR] WHERE { ?obs a ssn:ObservationValue; qudt:numericalValue ?wspeed; FILTER (?wspeed>10) }
  • 25. http://swissex.ch/data# Wan7/WindSpeed/ObsValue{timed} sp_wind http://swissex.ch/data# Wan7/WindSpeed/Observation{timed} http://swissex.ch/data# Wan7/ WindSpeed/ ObsOutput{timed} sweetSpeed:WindSpeed Creating Mappings 25 wan7 timed: datetime PK sp_wind: float ssn:ObservationValue qudt:numericValue xsd:decimal ssn:SensorOutput ssn:Observation ssn:hasValue ssn:observationResult ssn:Property ssn:observedProperty :Wan4WindSpeed a rr:TriplesMapClass; rr:tableName "wan7"; rr:subjectMap [ rr:template "http://swissex.ch/data#Wan7/WindSpeed/ObsValue/{timed}"; rr:class ssn:ObservationValue; rr:graph ssg:swissexsnow.srdf ]; rr:predicateObjectMap [ rr:predicateMap [ rr:predicate qudt:numericValue ]; rr:objectMap [ rr:column "sp_wind” rr:datatype xsd:decimal]];. W3C R2RML Mapping Language
  • 26. Query rewriting SELECT ?windspeed FROM STREAM <http://ssg4env.eu/SensorReadings.srdf> [NOW–5 HOUR TO NOW] WHERE { ?obs a ssn:ObservationValue; qudt:numericalValue ?windspeed; FILTER (?windspeed>10) } SELECT sp_wind FROM wan7 [FROM NOW-5 HOURS TO NOW] WHERE sp_wind >10 timed, sp_wind π ω σsp_wind>10 5 Hour wan7 SELECT sp_wind FROM wan7.win:time(5 hour) WHERE sp_wind >10 http://montblanc.slf.ch:22001/multidata?vs[0]=wan7& field[0]=wind_speed_scalar_av&c_min[0]=10& from=15/05/2012+05:00:00&to=15/05/2012+10:00:00 http://api.cosm.com/v2/feeds/14321/datastreams/4?start=2012-05- 15T05:00:00Z&end=2012-05-15T10:00:00Z Query rewriting R2RM L SNEE (DSMS) Esper (DSMS) GSN (middlwr) Cosm(middlwr) 26 H4: Ontology-based streaming queries  abstract expressions  concrete executable SPE queries H3: Ontology-based streaming queries  rewritten to relational-based queries using mappings SPARQLStream
  • 27. Ontology-based query rewriting 27 Query rewriting Query Processing Client SPARQLStream [tuples] [triples/bin dings] Algebra expression R2RML Mappings SPARQLStream query processing SELECT ?windspeed FROM STREAM <http://ssg4env.eu/SensorReadings.srdf> [NOW–5 HOUR] WHERE { ?obs a ssn:ObservationValue; qudt:numericalValue ?windspeed; FILTER (?windspeed>10) } SELECT sp_wind FROM wan7.win:time(5 hour) WHERE sp_wind >10 π timed,sp_wind ω σsp_wind>10 5 Hour wan7 Data translation SNEE Esper GSN Cosm pull/push https://github.com/jpcik/morph-streams Other H1: Sensor streaming data  instances of an ontology model H2: SPARQL extensions  streaming operators & continuous processing
  • 28. Evaluation of query rewriting overhead 28 H5: Query rewriting  Pull & Push delivery  acceptable overhead Native execution w/o rewriting Execution with rewriting Pull & Push delivery End-to latency Adapted Esper benchmark
  • 29. 29 Outline Motivation Background Conclusions Semantic stream query processing Sensor metadata characterization Ontology-based Access to Sensor Data Streams Hypotheses & contributions Representation Challenges Classification Metadata
  • 30. Characterizing semantic sensor metadata 30 WEB GSN Air Pressure? Air Temperature? Already classified time series Unclassified input series compare
  • 32. 0 1 2 3 4 5 6 7 8 9 10 3.65 3.7 3.75 3.8 3.85 3.9 3.95 4 4.05 4.1 0 1 2 3 4 5 6 7 8 9 10 3.7 3.75 3.8 3.85 3.9 3.95 4 4.05 4.1 Piecewise Linear Approximation 32 Reflect data trends Apply with different resolutions Applicable for different rates Online computation cheap Linear segments Time series time Reduce numerosity
  • 33. Linear Approximations 33 a d a c 0 π/2 -π/4 π/4 a b c d Key: segment slopes (angles) Divide the angle space in sectors distribution of angles in training set compute linear approximation compute slope distribution K-nearest neighbor classification 2 1 3
  • 34. Experiments SwissEx Confusion matrix SwissEx Training-Test datasets SwissExperiment AEMET 34
  • 35. Experiments AEMET Confusion matrix AEMET H6: Sensor data series  find characteristic patterns  make it recognizable among other types 35 Classification according to type FPs on subclasses of the same property
  • 36. Evaluation vs SAX 36 H7: Slope representations  type of data: semantic property  learned through classification
  • 37. Semantic Sensor Metadata swissex:Sensor1 rdf:type ssn:Sensor; ssn:onPlatform swissex:Station1; ssn:observes cf-property:wind_speed. swissex:Sensor2 rdf:type ssn:Sensor; ssn:onPlatform swissex:Station1; ssn:observes cf-property:air_temperature. 37 station1 W3C SSN Ontology Derive semantic metadata properties cf-property:wind_speed rdf:type dim:VelocityOrSpeed; rdfs:label "wind speed"; ssn:isPropertyOf cf-feature:wind; qu:propertyType qu:scalar; qu:generalQuantityKind qu:speed. Raw sensor data Semantic metadata
  • 38. 38 Outline Motivation Background Conclusions Semantic stream query processing Sensor metadata characterization Ontology-based Access to Sensor Data Streams Hypotheses & contributions Challenges
  • 39. Conclusions H1: Sensor streaming data  instances of an ontology model H2: SPARQL extensions  streaming operators & continuous processing H3: Ontology-based streaming queries  rewritten to relational-based queries using mappings Mapping sensor data to ontology instances, e.g. SSN Ontology SPARQLStream  data model, extensions syntax, semantics SPARQLStream  semantics of query rewriting to relational steaming algebra  usage of declarative mappings (W3C R2RML) Calbimonte, Corcho & Gray. Enabling ontology-based access to streaming data sources. ISWC 2010 Gray, García-Castro, Kyzirakos, Karpathiotakis, Calbimonte, Page et al. A semantically enabled service architecture for mashups over streaming and stored data. ESWC 2011 Gray, Sadler, Kit, Kyzirakos, Karpathiotakis, Calbimonte, Page, García-Castro, et al. A semantic sensor web for environmental decision support applications. Sensors, MDPI, 2011 Calbimonte, Corcho & Gray. Ontology-based Access to Streaming Data. In Posters ESWC 2010 39
  • 40. Conclusions 40 H4: Ontology-based streaming queries  abstract expressions  concrete executable SPE queries Instantiate, execute  ≠ SPEs: SNEE (DSMS), Esper (CEP), GSN & Cosm (Middlwr)  Available implementation  application in different domains H5: Query rewriting  Pull & Push delivery  evaluation overhead SPARQLStream  evaluation overhead wrt. native execution Push & pull delivery evaluation Calbimonte, Jeung, Corcho & Aberer. Enabling Query Technologies for the Semantic Sensor Web. IJSWIS 2012. Calbimonte & Corcho. Evaluating SPARQL Queries over RDF Streams. Linked Data Management: Principles and Techniques, CRC Press, 2013 (under review) Zhang, Duc, Corcho & Calbimonte. SRBench: A Streaming RDF/SPARQL Benchmark. ISWC 2012. Ruckhaus, Calbimonte, García-Castro & Corcho. Short Paper: From Streaming Data to Linked Data–A Case Study with Bike Sharing Systems. ISWC SSN 2012
  • 41. Conclusions 41 H6: Sensor data series  analyze in order to find characteristic patterns make it recognizable among other types H7: Slope representations  semantic properties such as the type of data  learned with classification techniques  acceptable precision 41 Raw observations analysis  slope distribution representation  compared with SoA representations i.e. SAX Evaluation of classification task  real world datasets AEMET, SwissEx  in presence of noisy data  deriving semantic metadata Calbimonte, Yan, Jeung, Corcho & Aberer. Deriving Semantic Sensor Metadata from Raw Measurements. ISWC SSN 2012 Calbimonte, Jeung, Corcho, & Aberer. Semantic Sensor Data Search in a Large-Scale Federated Sensor Network. ISWC SSN 2011
  • 42. Future directions 42 WEB SPARQLStream queries Publishing Linked Stream Data Currently static SPARQL streaming standards Dereferencing streaming data Query Federation Distributed sensor data Static and streaming sources Stream Reasoning query rewriting, expanding queries Expresiveness Integrate with the Web of Data Inferencing
  • 43. Future directions WEB Sensor pattern classification Combine with query processing Live data classification Statistical & quality analysis Integrate statistic analyisis Mappings to statistical models Data quality filtering Parallel Massive Stream Processing Online stream analysis Scalable stream processing S4, Storm, Streamcloud Heterogeneity 43
  • 44. Ontology-based Access to Sensor Data Streams Jean-Paul Calbimonte Supervisor: Oscar Corcho Ontology Engineering Group Facultad de Informática, Universidad Politécnica de Madrid 18.4.2013 jp.calbimonte@upm.es PhD Thesis Defense
  • 45. 45
  • 46. SSN Ontology with other ontologies 46 W3C SSN Ontology tool for modeling our sensor data combine with domain ontologies
  • 48. Static optimization 48 timed, sp_wind π ω σ sp_wind>10 5 Hour wan7 timed, windvalue π ω σ windvalue>10 5 Hour windsensor1 timed, windvalue π ω σ windvalue>10 5 Hour windsensor2
  • 52. RDF Streams and SPARQLStream 52 RDF Stream Time window Window-Stream
  • 53. Mappings 53 Subject, predicate, object Given a triple pattern t p = (sp, pp,op), the semantics of its evaluation over a lational streams referenced by a set of mappings M , is given by eval (t p,M), wh n algebra expression defined as: eval (t p,M) = ρf s→sp,f p→pp,f o→opπf s,f p,f o(s) where ρ is the relational rename operation and π is the relational projection on. s is the stream referenced by the mapping µ = f i ndM appi ng(t p,M) and f s , e the functions of µ that generate the projection expressions for producing respec e subject, predicate and object, for every tuple of s. For the previous example, the evaluation of t p1 is given by: eval (t p1,M) = ρf s→sp,f p→pp,f o→opπf s µ1 (s1.ts),f p µ1 (),f o µ1 ()(s1) The resulting algebra expression projects the s1.ts attribute, applying the f s on to create the subject. The functions f p µ1 and f o µ1 in this case are constants, edicate and object are the same for all tuples of s1. For the evaluation of more co Evaluate query
  • 54. Rewrite to algebra 54 Then, the evaluation of gp can be represented as the following algebra expression: eval (t p,M) = ωts,te,δ πf s µ1 (s1) ✶ πf s µ2 ,f o µ2 (s1) ✶ πf s µ4 ,f o µ4 (s1) ✶πf s µ5 ,f o µ5 (s1) This expression can be represented as a tree (Figure 4.1), where the leaf nodes are the streams and the other nodes are the relational streaming operators. Figure 4.1: Tree representation of the evaluation of a SPARQL Stream query rewritten as an alge- bra expression. eval (t p, M ) = ωts,te,δ πf s µ1 (s1) ✶ πf s µ2 ,f o µ2 (s1) ✶ πf s µ4 ,f o µ4 (s1) ✶πf s µ5 ,f o µ5 (s1) This expression can be represented as a tree (Figure 4.1), where the leaf nodes are th streams and the other nodes are the relational streaming operators. Figure 4.1: Tree representation of the evaluation of a SPARQL Stream query rewritten as an alg bra expression.
  • 57. SRBench Datasets real-world U.S. weather data1 first & largest sensor dataset in LOD 57 LinkedSensorData LinkedSensorMetadata LinkedObservationData ~20k US weather stations, ~100k sensors links to locations in GeoNames nearby hurricane & blizzard observations in US ~1.73 billion RDF triples ~159 million observations 1 http://mesowest.utah.edu Name Storm Type Date #Triples #Observations Data size Bill Hurricane Aug. 17 – 22, 2009 231,021,108 21,272,790 ~15 GB Ike Hurricane Sep. 01 – 13, 2008 374,094,660 34,430,964 ~34 GB Gustav Hurricane Aug. 25 – 31, 2008 258,378,511 23,792,818 ~17 GB Bertha Hurricane Jul. 06 – 17, 2008 278,235,734 25,762,568 ~13 GB Wilma Hurricane Oct. 17 – 23, 2005 171,854,686 15,797,852 ~10 GB Katrina Hurricane Aug. 23 – 30, 2005 203,386,049 18,832,041 ~12 GB Charley Hurricane Aug. 09 – 15, 2004 101,956,760 9,333,676 ~7 GB Blizzard Apr. 01 – 06, 2003 111,357,227 10,237,791 ~2 GB
  • 58. SRBench Queries 58 graph pattern matching solution modifier query form SPARQL 1.1 reasoning streaming data access and, filter, union, optional projection, distinct select, construct, ask aggregate, subquery subclass, subproperty, sameAs time window, istream observations, sensor metadata geonames, dbpedia select expr, property path dstream, rstream 17queries
  • 59. Query Features 59 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 1.Graph pattern matching A A,F,O A A,F A A,F,U A A A A A,F A,F,U A,F A,F,U A,F A,F A,F 2. Solution modifier P,D P,D P P P P P,D P P P,D P,D P P P,D P P P 3. Query form S S A S C S S S S S S S S S S S S 4. SPARQL 1.1 F,P A A,E,M ,F A,S N A,E,M A,E,M A,S,M ,F A,S,E, M,F,P A,E,M ,F,P F,P A,E,M ,P P P 5. Reasoning C R C A C 6. Streaming T T T T T T T,D T T T T T T T T 7. Dataset O O O O O O O O,S O,S O,S O,S O,S,G O,S,G O,S,G O,S,D O,S,G ,D S 1. And, Filter, Union, Optional 2. Projection, Distinct 3. Select, Construct, Ask 4. Aggregate, Subquery, Negation, Expr in SELECT, assignMent, Functions&operators, PropertyPath 5. subClassOf, subpRopertyOf, owl:sameAs 6. Time-based window, Istream, Dstream,Rstream 7. LinkedObservationData, LinkedSensorMetadata, GeoNames, Dbpedia