Stream Annotation Ontology

Working Draft

Latest version:: http://purl.oclc.org/NET/UNIS/sao/sao# (RDF/XML, Turtle)
Last Update:: Date: 23:06:39 12/05/2016 BST
Editors:: Sefki Kolozali, Institute for Communication Systems, University of Surrey
Authors:: Sefki Kolozali, Institute for Communication Systems, University of Surrey; Maria Bermudez, Institute for Communication Systems, University of Surrey; Payam Barnaghi, Institute for Communication Systems, University of Surrey
Contributors:: See Acknowledgements

You are granted a license to use, reproduce and create derivative works of this document under Creative Commons Attribution 3.0 Unported License. This copyright applies to Stream Annotation Ontology specification and RDF.

Abstract

Representing IoT data streams is an important requirement in semantic stream data applications, as well as in knowledge-based environments for Smart Cities. This study aims to semantically represent the features of a data stream defining the specifications of an information model on top of Semantic Sensor Networks (SSN), PROV-O and TimeLine Ontologies, and involves connections with the Complex Event Processing Ontology and Quality Ontology.

Status of this Document

This is a work in progress and as such is subject to change. Comments are very welcome, please send them to First Author.

Introduction
Exemplifications
Stream Annotation Ontology at Glance
Cross-reference for NAME classes
Cross-reference for NAME properties

Appendices

References

1. Introduction

This study aims to semantically represent the features of a stream data. The SAO ontology is a lightweight semantic model, which is built on top of well-known models to represent IoT data streams. It has been developed within the realm of CityPulse project that contains four main modules, namely Stream Annotation Ontology (SAO), Quality, Compex Event Ontology, and User Profiles information models.

The SAO Ontology provides temporal concepts such as StreamData, Segment, StreamAnalysis on top of the TimeLine concepts, PROV-O, SSN and Event Ontology. Timeline Ontology extends OWL-Time with various timelines (e.g. universal or discrete), temporal concepts, such as Instant, and Interval, and interval relationships. The SAO ontology concepts are in line with PROV-O ontology principels where it adopts, Agent, Entity, Activity. In terms of event description, it adopts Event concept of the Event Ontology and provides a broad definition of the StreamEvent concept in order to express an artificial classification of a time region, corresponding to a particular data stream. It also extends the sensor observations described in SSN Ontology ssn:Observation through a concept, StreamData, that allows to describe Segment or Point linked to time intervals or time instants. Below is the depiction of the workflow of the SAO Ontology.

2. Exemplification

The following is an exemplification of an annotated stream based on SAO Ontology, where it describes a traffic observation of a sensor feature called, "Average Speed", for a pair of sensors that are provided by the City of Aarhus via Open Data Aarhus Platform.

Example 1


    
    prefix ssn: <http://purl.oclc.org/NET/ssnx/ssn#> .
    prefix tl: <http://purl.org/NET/c4dm/timeline.owl#> .
    prefix sao: <http://purl.oclc.org/NET/UNIS/sao/sao#> .
    prefix ct: <http://www.insight-centre.org/ct#> .
    
    :cityofaarhus a foaf:Organisation, prov: Agent .
    
    <trafficData158324>  a  ces:PrimitiveEventService , ssn:Sensor ;
        ssn:observes  "Measured_Time" , "Estimated_Time" , "Congestion_Level" , "Vehicle_Count" , "Average_Speed" ;
        prov: actedOnBehalfOf :cityofaarhus .
    
    
    
    <http://unis/trafficData158324FoI-001>
        ct:hasFirstNode   [ a                  ct:Node ;
                            ct:hasCity        "Hinnerup"^^xsd:string ;
                            ct:hasLatitude    "56.23172069428216"^^xsd:double ;
                            ct:hasLongtitude  "10.104986076057457"^^xsd:double ;
                            ct:hasStreet      "Ãrhusvej"^^xsd:string
                           ] ;
        ics:hasSecondNode  [ a                  ct:Node ;
                            ct:hasCity        "Hinnerup"^^xsd:string ;
                            ct:hasLatitude    "56.22579478256016"^^xsd:double ;
                            ct:hasLongtitude  "10.116589665412903"^^xsd:double ;
                            ct:hasStreet      "Ãrhusvej"^^xsd:string
                            ] .
    
    <http://unis/trafficData158324Property-001> a <http://www.surrey.ac.uk/ics#Average_Speed> ;
        ssn:isPropertyOf  <http://unis/trafficData158324FoI-001> .
    
    
    <http://unis/trafficData158324Observation-001> a sao:Point , ssn:Observation ;
        sao:hasUnitOfMeasurement  <http://unit1:seconds>; “no brackets”
        sao:value   "141.0"^^xsd:double ;
        sao:time    [ a            tl:Instant ;
                        tl:at        "2014-09-30T06:00:00"^^xsd:dateTime ;
                        tl:duration  "PT5M"^^xsd:duration
                    ] ;
        ssn:observedBy  <trafficData158324> ;
        ssn:observedProperty    <http://unis/trafficData158324Property-001> .
        prov:wasAttributedTo :cityofaarhus .

Below is an illustration of a description of an aggregation analysis output using on Symbolic Aggregate Approximation algorithm to compress the data for 24 hours long of traffic sensor observation for a sensor feature, called "Average Speed", with a segmentation size 12, and alphabet size 5. TimeLine Ontology is used to link the segment feature description with the time extent of a temporal entity representing the stream data. Thus, we can express a stream data as a time interval on the universal timeline, and also relate such an interval with the corresponding interval on the discrete timeline.

Example 2


    
    prefix ssn: <http://purl.oclc.org/NET/ssnx/ssn#> .
    prefix tl: <http://purl.org/NET/c4dm/timeline.owl#> .
    prefix sao: <http://purl.oclc.org/NET/UNIS/sao/sao#> .
    prefix ct: <http://www.insight-centre.org/ct#> .

    cityOfAarhus a foaf:Organisation, prov:Agent .
    
    <trafficData158324>  a  ces:PrimitiveEventService , ssn:Sensor ;
        ssn:observes  "Estimated_Time" , "Congestion_Level" , "Vehicle_Count" , "Average_Speed" .
    
    
    <http://unis/trafficData158324Property-001>   a   "http://www.surrey.ac.uk/ ics#Average_Speed" ;
    ssn:isPropertyOf  <http://unis/trafficData158324FoI-001> .
    
    <http://unis/trafficData158324FoI-001>
        ct:hasFirstNode   [ a                  ct:Node ;
            ct:hasCity        "Hinnerup"^^xsd:string ;
            ct:hasLatitude    "56.23172069428216"^^xsd:double ;
            ct:hasLongtitude  "10.104986076057457"^^xsd:double ;
            ct:hasStreet      "Ãrhusvej"^^xsd:string
            ] ;
        ct:hasSecondNode  [ a                  ct:Node ;
            ct:hasCity        "Hinnerup"^^xsd:string ;
            ct:hasLatitude    "56.22579478256016"^^xsd:double ;
            ct:hasLongtitude  "10.116589665412903"^^xsd:double ;
            ct:hasStreet      "Ãrhusvej"^^xsd:string
            ] .
    
    <VeryHighAverage_Speed001>    a   sao:StreamEvent ;
            sao:time   [ a  tl:Interval ;
                    tl:beginsAtDateTime  "27-Sep-2014 12:59:00"^^xsd:duration ;
                    tl:endsAtDateTime    "27-Sep-2014 13:59:00"^^xsd:duration
                    ] ;
                    prov:used  <PAA-Average_Speedcae001> .
                    dc:description “VeryHigh Average Speed”  ;
                    prov:wasAsscoatedWith :cityofaarhus  .
       
       
       <PAA-Average_Speed001> a   sao:PiecewiseAggregateApproximation ;
            sao:time    [ a tl:Instant ;
                        tl:at        "27-Sep-2014 12:59:00"^^xsd:date ;
                        tl:duration  "PT1H"^^xsd:duration
                        ] ;
            sao:value            “52”^^xsd:string ;
            tl:onTimeLine        <feature_timeline> ;
            prov:wasGeneratedBy  <VeryHighAverage_Speed001>  ;
            prov:wasAttributedTo :cityofaarhus  .
       
       ics:Average_SpeedSaxWord-001    a   sao:SymbolicAggregateApproximation ;
            sao:alphabetsize     "5"^^xsd:int ;
            sao:segmentsize      "12"^^xsd:int ;
            sao:time             [ a tl:Instant ;
                                tl:at   "01-Aug-2014 06:59:00"^^xsd:date ;
                                tl:duration  "P1D"^^xsd:duration
                            ] ;
            sao:value            "eeeeeeebbbcc"^^xsd:string ;
            prov:wasGeneratedBy  <http://unis/trafficData158324Property-001> ;
            prov:wasAttributedTo :cityofaarhus  .
            
        <feature_timeline>  a  tl:UniversalTimeline .

The following sparql query exemplify how to query such turtle document to obtain results for all observations for property of average speed, which has been collected after "2014-09-29T10:20:00".

Example 3


    prefix ssn: <http://purl.oclc.org/NET/ssnx/ssn#> 
    prefix tl: <http://purl.org/NET/c4dm/timeline.owl#> 
    prefix xsd: <http://www.w3.org/2001/XMLSchema#> 
    prefix sao: <http://purl.oclc.org/NET/UNIS/sao/sao#> 
                    
    select ?observation ?value ?observation_start ?property
            WHERE { ?observation a ssn:Observation .
                    ?observation sao:hasValue ?value .
                    ?observation sao:time ?time .
                    ?time tl:at ?observation_start .
                    ?observation ssn:observedProperty ?property .
                    ?property a "http://www.surrey.ac.uk/ics#Average_Speed" .
                        FILTER(?observation_start > "2014-09-29T10:20:00"^^xsd:dateTime)}

3. Stream Annotation Ontology at Glance

An alphabetical index of NAME terms, by class (concepts) and properties are given below. All the terms are hyperlinked to their detailed description for quick reference.

Classes: DiscreteCosineTransform, DiscreteFourierTransform, Mean, Median, KMeans, MovingAverage, PiecewiseAggregateApproximation, Point, Segment, StreamAnalysis, StreamData, StreamEvent, SymbolicAggregateApproximation, SensorSAX, UnitOfMeasurement, FeatureOfInterest, Observation, Sensor, Instant , Interval , SpatialThing, Activity, Entity

Properties: Timestamp, alphabetsize, hasDatatype, hasID, hasLocation, hasURI, quality, samplesize, samplingrate, segmentsize, stepsize, time, value, wasAttributedTo, wasAssociatedWith, used, wasDerivedFrom, wasGeneratedBy, , observationSamplingTime, observationResultTime, hasUnitOfMeasurement, hasSegment, hasPoint, beginsAtLocation, endsAtLocation sao:severity sao:status sao:severity sao:sensitivity sao:causeArea sao:name sao:levelOfInterest sao:comments sao:minwindowsize sao:maxwindowsize sao:sensitivity

4. Cross-reference for NAME classes

Class: sao:StreamAnalysis

URI: http://purl.oclc.org/NET/UNIS/sao/sao#StreamAnalysis

- It describes the methods that have been used in the data analysis process. The output of the method is descirbed by sao:value. It offers an opportunity to describe the output as points and segments along with its temporal relationships. With these features, it is not only possible to describe simple sensor observations (e.g. temperature) but also complex data, such as audio and video. The outputs can be described as matrix by using sao:value (string) along with the number of columns and rows involved in the matrix: this enables the annotated data to be easily reconstructed. This concept is also related to the prov:Entity.

sub-class-of:: ssn:Observation prov:Entity
in-domain-of:: sao:hasSegment sao:hasPoint; prov:wasAttributedTo prov:wasGeneratedBy prov:wasAssociatedWith; sao:quality sao:time sao:hasUnitOfMeasurement sao:segmentsize; sao:stepsize sao:nColumns sao:nRows sao:value
in-range-of:: prov:used prov:wasDerivedFrom

Stream Annotation Ontology

Working Draft

Abstract

Status of this Document

Appendices

1. Introduction

2. Exemplification

3. Stream Annotation Ontology at Glance

4. Cross-reference for NAME classes

Class: sao:StreamAnalysis

Class: sao:StreamData

Class: sao:StreamEvent

Class: sao:DiscreteCosineTransform

Class: sao:DiscreteFourierTransform

Class: sao:Mean

Class: sao:Median

Class: sao:KMeans

Class: sao:PiecewiseAggregateApproximation

Class: sao:Point

Class: sao:Segment

Class: sao:SymbolicAggregateApproximation

Class: sao:SensorSAX

Class: ssn:Sensor

Class: muo:UnitOfMeasurement

Class: ssn:FeatureOfInterest

Class: ssn:Observation

Class: tl:Instant

Class: tl:Interval

Class: geo:SpatialThing

Class: prov:Activity

Class: prov:Agent

Class: prov:Entity

Property: sao:Timestamp

5. Cross-reference for NAME properties

Property: sao:alphabetsize

Property: sao:hasDatatype

Property: sao:hasID

Property: sao:hasLocation

Property: sao:hasURI

Property: sao:quality

Property: sao:samplesize

Property: sao:samplingrate

Property: sao:segmentsize

Property: sao:stepsize

Property: sao:time

Property: prov:wasAttributedTo

Property: prov:wasAssociatedWith

Property: prov:used

Property: prov:wasDerivedFrom

Property: prov:wasGeneratedBy

Property: ssn:observationSamplingTime

Property: sao:hasUnitOfMeasurement

Property: sao:hasSegment

Property: sao:hasPoint

Property: sao:beginsAtLocation

Property: sao:endsAtLocation

Property: sao:value

Property: sao:clustersize

Property: sao:status

Property: sao:severity

Property: sao:sensitivity

Property: sao:minwindowsize

Property: sao:maxwindowsize

Property: sao:name

Property: sao:levelOfInterest

Property: sao:comments

Property: sao:causeArea

Property: sao:type

References

Acknowledgements