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Database Sensor network technicianscientist data user (including scientists) maintains (deploys, calibrates) Individual Instrument(s) measurement data measurement Data (e.g., CSV file) queries uses reports needs data flows interactions senses Knowledge Capture
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Human-Aware Sensor Network Ontology (HASNetO): Semantic Support for
Empirical Data Collection
Paulo Pinheiro1, Deborah McGuinness1, Henrique Santos1,2
1Rensselaer Polytechnic Institute, USA2Universidade de Fortaleza, Brazil
ISWC/LISC, October 2015
Outline
• Capturing Contextual Knowledge• Integration of Empirical Concepts and
Sensor Network Concepts• Provenance Knowledge support for
Contextual Knowledge• HASNetO: The Human-Aware Sensor
Network Ontology • Conclusions
2
Database
Sensornetwork
technician scientist
data user(including scientists)
maintains(deploys,calibrates)
Individual Instrument(s)
measurementdata
measurement Data (e.g., CSV file)
queries
uses
reportsneeds
data flows
interactions
senses
senses
senses
Knowledge Capture
Measurement Time Interval
TimeStamp,AirTemp_C_Avg,RH_Pct_Avg 2015-02-12T09:30:00Z,-4.5,66.582015-02-12T09:45:00Z,-4.372,66.452015-02-12T10:00:00Z,-4.146,65.982015-02-12T10:15:00Z,-4.084,66.222015-02-12T10:30:00Z,-4.251,67.482015-02-12T10:45:00Z,-4.185,69.852015-02-12T11:00:00Z,-4.133,722015-02-12T11:15:00Z,-3.959,70.84…2015-02-12T23:00:00Z,-9.63,77.882015-02-12T23:15:00Z,-10.48,80.82015-02-12T23:30:00Z,-10.96,822015-02-12T23:45:00Z,-10.1,80.7
t
A Comma-Separated Value (CSV) dataset:
February 12, 2015, 9:30AM
February 12, 2015, 11:45PM
Temporal Contextual Diff
t
Configuration
Deployment
SensorCalibration
InfrastructureAcquisition
t
February 12, 2015, 9:30AM
February 12, 2015, 11:45PM
Data usage
Full Extent of Contextual Knowledge Scope
6
timespaceagentstrust
“typical” measurement scope
Selected Observation and Sensor Network Ontologies
• Sensor Network Knowledge– Needed to describe the infrastructure of a
sensor network, and the use of sensor network components in the generation of datasets
• Observation Knowledge– Needed to describe observations and their
measurements. Measurements need to be characterized in terms of physical entities, entity characteristics, units, and values
Observation ConceptsIn our measurements, observation concepts are either OBOE concepts or OBOE-derived concepts.
The thing that one is observing is an entity, e.g.,’air’.
Things that are observed, however, cannot be measured. For example, how can one measure ‘air’? A characteristic is a measurable property of an entity, e.g., air temperature.
An observation is a collection of measurements of entity’s characteristics.
Each measurement has a value, e.g, ’45’, and a standard unit, e.g., ‘Celsius’.
oboe:Entity
oboe:Observation
of-entity11
hasneto:DataCollection
oboe:Measurement
oboe:Standard
oboe:Characteristic
oboe:Value
of-characteristic
hasneto:hasMeasurement
uses-standard
has-characteristic
has-characteristic-value
has-standard-value
has-value
hasneto:hasContext
11
*
1
1
1
1
1
1
*
*
*
*
*
*
Sensor Network ConceptsIn the Jefferson Project, sensor network concepts are either Virtual Solar-Terrestrial Observatory (VSTO) concepts or VSTO-derived concepts.
Instruments and their detectors are used to perform measurements.
Instruments, however, can only perform measurements during a deployment at a given platform, e.g., tower, plane, person, buoy
vstoi:Detector
vstoi:Instrument
vstoi:Platform
hasneto:Sensing
Perspective
oboe:Characteristic
oboe:Entity
vstoi:Detachable
Detector
vstoi:AttachedDetector
* *
*
1
0..1*
hasPerspectiveCharacteristic
perspectiveOf
Selected Provenance Ontology
Provenance Knowledge is needed to contextualize VTSO deployments and OBOE observations
– “Who deployed an instrument?” – “When was the instrument deployed?” – “How many times instrument parameters
changed during deployment?” – “What was the value of each parameter
during a given observation?”
W3C PROV Concepts
Provenance concepts are W3C PROV concepts.
Provenance-Level Integration
• Provenance provides contextual high-level integration of observation and sensor network concepts
• Integration also occurs in terms of information flow allowing full accountability of measurements in the context of sensor network components and configurations
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prov:Activity
hasneto:DataCollection
vstoi:Deployment
xsd:dateTime
xsd:dateTime
hasDataCollection
1*
prov:Agent
prov:Entity
usedwasGeneratedBy
wasAttributeTo
wasAssociatedWith
actedOnBehalfOf
wasDerivedFrom
startedAtTime
endedAtTime
The Human-Aware Sensor Network Ontology
vstoi:Detector
vstoi:Instrument
vstoi:Platform
hasneto:Sensing
Perspective
oboe:Characteristic
oboe:Entity
vstoi:Detachable
Detector
vstoi:AttachedDetector
*
*
*1
0..1
*hasPerspectiveCharacteristic
perspectiveOf
prov:Activity
hasneto:DataCollection
vstoi:Deployment
xsd:dateTime
xsd:dateTime
hasDataCollection
1*
prov:Agent
wasAssociatedWithstartedAtTime
endedAtTime
1
1
*
**
*
oboe:Measurement
of-characteristic
hasneto:hasMeasurement 1
1
*
*
Metadata in Action
14
Mouse over
Combining Data and Metadata
15
Mouse over
Mouse over
Metadata
based
facete
d searc
h
Measurement metadata
Metadata about the metadata
Conclusions
• HASNetO was briefly presented along with its support for describing sensor networks
• OBOE and VSTO provide concepts required for encoding observation and sensor network metadata
• Neither OBOE and VSTO provide concepts for describing contextual knowledge about deployments and observations
16
HASNetO provides a comprehensive integrated set of concepts for capturing sensor network measurements along with contextual knowledge about these measurements
• Extra
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SPARQL Queries Against HASNetO
• Question in English:“List detectors currently deployed with instrument vaisalaAW310-SN000000 and the physical characteristics measured by these detectors”
• W3C SPARQL query (a translation of the question above):select ?detector ?characteristic ?platform where {?deployment a Deployment>. ?deployment vsto:hasInstrument kb:vaisalaAW310-SN000000. ?platform vsto:hasDeployment ?deployment. ?deployment hasneto:hasDetector ?detector. ?detector oboe:detectsCharacteristic ?characteristic. }
• Query Result:+----------------+-------------------+--------------------+
| detector | characteristic | platform |+----------------+-------------------+--------------------+ | Vaisala WMT52 | windSpeed | towerDomeIsland |+----------------+-------------------+--------------------+
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Example of a HASNetO Knowledge Base*
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:obs1 a oboe:Observation; oboe:ofEntity oboe:air; prov:startedAtTime "2014-02-11T01:01:01Z"^^xsd:dateTime;
prov:endedAtTime "2014-02-12T01:01:01Z"^^xsd:dateTime; . :dp1 a vsto:Deployment;
vsto:hasInstrument :vaisalaAW310-SN000000; hasneto:hasDetector :vaisalaWMT52-SN000000;
hasneto:hasObservation :obs1;prov:startedAtTime "2014-02-10T01:01:01Z"^^xsd:dateTime; prov:endedAtTime "2014-02-17T01:20:02Z"^^xsd:dateTime; .
:genericTower vsto:hasDeployment :dp1; . :dset1 a vsto:Dataset;
prov:wasAttributedTo :vaisalaAW310; prov:wasGeneratedBy :obs1; .
*The knowledge base fragment above is represented in W3C Turtle.
Knowledge About Sensor Network Operation
• Knowledge about sensor networks, however, can rarely be inferred from sensor data themselves.
• The lack of contextual knowledge about sensor data can render them useless.
Knowledge about sensor networks is as important as data captured by sensor networks, and sensor network metadata is as important as sensor data
21
Human-Aware Data Acquisition Framework
• Two locations: • Darrin Fresh Water
Institute (DFWI) at Lake George, NY and
• data processing site in Troy, NY
• Wireless network used to communicate with sensors
• Relational database for data management and RDF triple store for metadata management
Future Steps
• We will keep refining the HASNetO vocabulary and testing it over a constantly growing HASNetO-based knowledge base
• We are in the process of integrating HASNetO into the HAScO (Human-Aware Science Ontology) to accommodate contextual knowledge beyond observation data to include simulation data and experimental data
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