1
Semantic Sensor Web
ARC Research Network on Intelligent Sensors, Sensor Networks and
Information Processing – ISSNIP talk
Melbourne, August 1, 2008
Amit Sheth
LexisNexis Ohio Eminent Scholar
Kno.e.sis Center, Wright State University
Thanks: Cory Henson and Kno.e.sis Semantic Sensor Web team
2
Presentation Outline
1.
Motivating scenario
2. Sensor Web Enablement
3. Metadata in the domain of Sensors
4. Semantic Sensor Web
5. Prototyping the Semantic Sensor Web
Motivating Scenario
High-level Sensor
Low-level Sensor
How do we determine if the three images depict …
• the same time and same place?
• same entity?
• a serious threat?
4
The Challenge
Collection and analysis of information from
heterogeneous multi-layer sensor nodes
5
Why is this a Challenge?
•
There is a lack of uniform operations and standard representation for
sensor data.
•
There exists no means for resource reallocation and resource sharing.
•
Deployment and usage of resources is usually tightly coupled with the
specific location, application, and devices employed.
•
Resulting in a lack of interoperability.
6
Interoperability
• The ability of two or more autonomous,
heterogeneous, distributed digital entities to
communicate and cooperate among themselves
despite differences in language, context, format
or content.
• These entities should be able to interact with one
another in meaningful ways without special
effort by the user – the data producer or
consumer – be it human or machine.
Survey
Many diverse sensor data management application frameworks were compared,
such as:
•
GSN (Global Sensor Network, Digital Enterprise Research Institute (DERI),
http://gsn.sourceforge.net/
•
Hourglass (Harvard, http://www.eecs.harvard.edu/~syrah/hourglass/ )
•
•
An Infrastructure for Connecting Sensor Networks and Applications
IrisNet (Intel & Carnegie Mellon University, http://www.intel-iris.net/ )
•
Internet-Scale Resource-Intensive Sensor Network Service
These application frameworks provided only localized interoperability and that a
standards-based framework was necessary.
Recent work that does follow key standard (SWE/OGC framework/standards)
•
SensorWeb project at University of Melbourne (http://www.gridbus.org/sensorweb/)
•
52°North's Sensor Web Community
•
NASA JPL/GSFC SersorWeb, Northrop Grumman's PULSENet
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The Open Geospatial Consortium
Sensor Web Enablement Framework
Open Geospatial Consortium
• Consortium of 330+ companies,
government agencies, and academic
institutes
• Open Standards development by consensus
process
• Interoperability Programs provide end-toend implementation and testing before spec
approval
• Develop standard encodings and Web
service interfaces
OGC Mission
To lead in the
development,
promotion and
harmonization of
open spatial
standards
• Sensor Web Enablement
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What is Sensor Web Enablement?
http://www.opengeospatial.org/projects/groups/sensorweb
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What is Sensor Web Enablement?
• An interoperability framework for accessing and utilizing sensors and
sensor systems in a space-time context via Internet and Web
protocols
• A set of web-based services may be used to maintain a registry of
available sensors and observation queries
• The same web technology standard for describing the sensors’
outputs, platforms, locations, and control parameters should be used
across applications
• This standard encompasses specifications for interfaces, protocols,
and encodings that enable the use of sensor data and services
http://www.opengeospatial.org/projects/groups/sensorweb
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Sensor Web Enablement Desires
• Quickly discover sensors (secure or public) that can meet my needs –
location, observables, quality, ability to task
• Obtain sensor information in a standard encoding that is
understandable by me and my software
• Readily access sensor observations in a common manner, and in a
form specific to my needs
• Subscribe to and receive alerts when a sensor measures a particular
phenomenon
OGC Sensor Web Enablement
Constellations of heterogeneous sensors
Vast set of users and applications
Satellite
Airborne
Sensor Web Enablement
Weather
Surveillance
•
•
Chemical
Detectors
Biological
Detectors
•
•
Distributed self-describing sensors and
related servicesNetwork Services
Link sensors to network and networkcentric services
Common XML encodings, information
models, and metadata for sensors and
observations
Access observation data for value added
processing and decision support
applications
Sea State
http://www.opengeospatial.org/projects/groups/sensorweb
SWE Components - Languages
Information Model
for Observations and
Sensing
Sensor and Processing
Description Language
Observations &
Measurements
(O&M)
GeographyML
(GML)
SensorML
(SML)
TransducerML
(TML)
Common Model
for Geographical
Information
Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.
Multiplexed, Real
Time Streaming
Protocol
SWE Components - Languages
•
Sensor Model Language (SensorML) – Standard models and XML
Schema for describing sensors systems and processes; provides information
needed for discovery of sensors, location of sensor observations, processing of
low-level sensor observations, and listing of taskable properties
•
Transducer Model Language (TransducerML) – The conceptual model
and XML Schema for describing transducers and supporting real-time
streaming of data to and from sensor systems
•
Observations and Measurements (O&M) – Standard models and XML
Schema for encoding observations and measurements from a sensor, both
archived and real-time
SWE Components – Web Services
Command and Task
Sensor Systems
Access Sensor
Description and
Data
SOS
Discover Services,
Sensors, Providers,
Data
SPS
SAS
Catalog
Service
Clients
Accessible from various
types of clients from
PDAs and Cell Phones
to high end
Workstations
Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.
Dispatch Sensor
Alerts to registered
Users
SWE Components – Web Services
•
Sensor Observation Service (SOS) – Standard Web service interface for
requesting, filtering, and retrieving observations and sensor system
information. This is the intermediary between a client and an observation
repository or near real-time sensor channel
•
Sensor Alert Service (SAS) – Standard Web service interface for
publishing and subscribing to alerts from sensors
•
Sensor Planning Service (SPS) – Standard Web service interface for
requesting user-driven acquisitions and observations. This is the
intermediary between a client and a sensor collection management
environment
•
Web Notification Service (WNS) – Standard Web service interface for
asynchronous delivery of messages or alerts from SAS and SPS web services
and other elements of service workflows
SWE Components - Dictionaries
Phenomena
Units of
Measure
Sensor Types
Registry
Service
OGC Catalog Service
for the Web (CSW)
Applications
Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.
Sensor Model Language
(SensorML)
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SensorML Overview
•
SensorML is an XML schema for defining the geometric, dynamic, and observational
characteristics of a sensor
•
The purpose of the sensor description:
1. provide general sensor information in support of data discovery
2. support the processing and analysis of the sensor measurements
3. support the geolocation of the measured data.
4. provide performance characteristics (e.g. accuracy, threshold, etc.)
5. archive fundamental properties and assumptions regarding sensor
•
SensorML provides functional model for sensor, not detail description of hardware
•
SensorML separates the sensor from its associated platform(s) and target(s)
Scope of SensorML Support
•
Designed to support a wide range of sensors
– Including both dynamic and stationary platforms
– Including both in-situ and remote sensors
•
Examples:
– Stationary, in-situ – chemical “sniffer”, thermometer, gravity meter
– Stationary, remote – stream velocity profiler, atmospheric profiler, Doppler
radar
– Dynamic, in-situ – aircraft mounted ozone “sniffer”, GPS unit, dropsonde
– Dynamic, remote – satellite radiometer, airborne camera, soldier-mounted video
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Information provided by SensorML
•
Observation characteristics
– Physical properties measured (e.g. radiometry, temperature, concentration, etc.)
– Quality characteristics (e.g. accuracy, precision)
– Response characteristics (e.g. spectral curve, temporal response, etc.)
•
Geometry Characteristics
– Size, shape, spatial weight function (e.g. point spread function) of individual samples
– Geometric and temporal characteristics of sample collections (e.g. scans or arrays)
•
Description and Documentation
– Overall information about the sensor
– History and reference information supporting the SensorML document
23
SML Concepts – Sensor
Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville
SML Concepts – Sensor Description
Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville
SML Concepts –Accuracy and Range
Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville
SML Concepts –Platform
Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville
SML Concepts – Process Model
•
In SensorML, everything is modeled as a
Process
•
ProcessModel
– defines atomic process modules
(detector being one)
– has five sections
• metadata
• inputs, outputs,
parameters
• method
– Inputs, outputs, and parameters
defined using SWE Common data
definitions
Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville
SML Concepts – Process
•
Process
– defines a process chain
– includes:
• metadata
• inputs, outputs, and
parameters
• processes (ProcessModel,
Process)
• data sources
• connections between
processes and between
processes and data
•
System
– defines a collection of related processes
along with positional information
Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville
SML Concepts –Metadata Group
•
Metadata is primarily for discovery and
assistance, and not typically used within
process execution
•
Includes
– Identification, classification,
description
– Security, legal, and time constraints
– Capabilities and characteristics
– Contacts and documentation
– History
Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville
SML Concepts – Event
Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville
Example: Observation
+precedingEvent 0..*
«FeatureType»
Event
«Union»
Procedure
+
+
+followingEvent 0..*
+
+
procedureType: ProcedureSystem
procedureUse: ProcedureEvent
+procedure
eventParameter: TypedValue [0..*]
time: TM_Object
«DataType»
TypedValue
+
+
property: ScopedName
value: Any
1
AnyDefinition
+generatedObservation
0..*
+
+
+
0..*
1
AnyIdentifiableObject
«FeatureType»
Observ ation
quality: DQ_Element [0..1]
responsible: CI_ResponsibleParty [0..1]
result: Any
«ObjectType»
Phenomenon
+observedProperty
1
{Definition must be of a
phenomenon that is a property
of the featureOfInterest}
+propertyValueProvider
+featureOfInterest
«FeatureType»
AnyIdentifiableFeature
An Observation is an Event whose result is an estimate of the value
of some Property of the Feature-of-interest, obtained using a specified Procedure
The Feature-of-interest concept reconciles remote and in-situ observations
Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville
Presentation Outline
1.
Motivating scenario
2. Sensor Web Enablement
3. Metadata in the domain of Sensors
4. Semantic Sensor Web
5. Prototyping the Semantic Sensor Web
Data Pyramid
Data Pyramid
Sensor Data Pyramid
Ontology
Metadata
Knowledge
Entity Metadata
Information
Feature Metadata
Raw Sensor (Phenomenological) Data
Data
Sensor Data Pyramid
• Avalanche of data
• Streaming data
• Multi-modal/level data fusion
• Lack of interoperability
Ontology
Metadata
Entity Metadata
Feature Metadata
Raw Sensor Data
(e.g., binary images, streaming video, etc.)
Sensor Data Pyramid
• Extract features from data
• Annotate data with feature metadata
• Store and query feature metadata
Ontology
Metadata
Entity Metadata
Feature Metadata
Raw Sensor Data
(e.g., lines, color, texture, etc.)
Sensor Data Pyramid
• Detect objects-events from features
• Annotate data with objects-event metadata
• Store and query objects-events
Ontology
Metadata
Entity Metadata
Feature Metadata
Raw Sensor Data
(e.g., objects and events such as cars driving)
Sensor Data Pyramid
Discover and reason over associations:
• objects and events
• space and time
• provenance/context
Ontology
Metadata
Entity Metadata
Feature Metadata
Raw Sensor Data
(e.g., situations such as cars speeding
dangerously)
Presentation Outline
1.
Motivating scenario
2. Sensor Web Enablement
3. Metadata in the domain of Sensors
4. Semantic Sensor Web
5. Prototyping the Semantic Sensor Web
Semantic Sensor Web
What is the Semantic Sensor Web?
• Adding semantic annotations to existing standard Sensor
Web languages in order to provide semantic descriptions
and enhanced access to sensor data
• This is accomplished with model-references to ontology
concepts that provide more expressive concept
descriptions
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Semantic Sensor Web
What is the Semantic Sensor Web?
• For example,
– using model-references to link O&M annotated sensor data with
concepts within an OWL-Time ontology allows one to provide
temporal semantics of sensor data
– using a model reference to annotate sensor device ontology
enables uniform/interoperable characterization/descriptions of
sensor parameters regardless of different manufactures of the
same type of sensor and their respective proprietary data
representations/formats
42
Standards Organizations
W3C Semantic Web
• SML-S
• O&M-S
• TML-S
• Resource Description Framework
• RDF Schema
• Web Ontology Language
• Semantic Web Rule Language
OGC Sensor Web Enablement
• SensorML
• TransducerML
• SA-REST
Web Services
Sensor
Ontology
• O&M
• SAWSDL*
• Web Services Description Language
• REST
• GeographyML
Sensor
Ontology
National Institute for Standards
and Technology
• Semantic Interoperability Community
of Practice
• Sensor Standards Harmonization
* SAWSDL - now a W3C Recommendation is based on our work.
Semantic Sensor Web
44
Semantic Annotation
RDFa
• Used for semantically annotating XML documents.
• Several important attributes within RDFa include:
–
–
–
–
about: describes subject of the RDF triple
rel: describes the predicate of the RDF triple
resource: describes the object of the RDF triple
instanceof: describes the object of the RDF triple with the predicate as
“rdf:type”
Other used Model Reference in Semantic Annotations
• SAWSDL: Defines mechanisms to add semantic annotations to
WSDL and XML-Schema components (W3C Recommendation)
• SA-REST: Defines mechanisms to add semantic annotations to
REST-based Web services.
W3C, RDFa, http://www.w3.org/TR/rdfa-syntax/
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Semantically Annotated O&M
<swe:component name="time">
<swe:Time definition="urn:ogc:def:phenomenon:time" uom="urn:ogc:def:unit:date-time">
<sa:swe rdfa:about="?time" rdfa:instanceof="time:Instant">
<sa:sml rdfa:property="xs:date-time"/>
</sa:swe>
</swe:Time>
</swe:component>
<swe:component name="measured_air_temperature">
<swe:Quantity definition="urn:ogc:def:phenomenon:temperature“
uom="urn:ogc:def:unit:fahrenheit">
<sa:swe rdfa:about="?measured_air_temperature“
rdfa:instanceof=“senso:TemperatureObservation">
<sa:swe rdfa:property="weather:fahrenheit"/>
<sa:swe rdfa:rel="senso:occurred_when" resource="?time"/>
<sa:swe rdfa:rel="senso:observed_by" resource="senso:buckeye_sensor"/>
</sa:sml>
</swe:Quantity>
</swe:component>
<swe:value name=“weather-data">
2008-03-08T05:00:00,29.1
</swe:value>
46
Semantically Annotated O&M
<swe:component name="time">
<swe:Time definition="urn:ogc:def:phenomenon:time" uom="urn:ogc:def:unit:date-time">
<sa:swe rdfa:about="?time" rdfa:instanceof="time:Instant">
<sa:sml rdfa:property="xs:date-time"/>
</sa:swe>
</swe:Time>
</swe:component>
<swe:component name="measured_air_temperature">
<swe:Quantity definition="urn:ogc:def:phenomenon:temperature“
uom="urn:ogc:def:unit:fahrenheit">
<sa:swe rdfa:about="?measured_air_temperature“
rdfa:instanceof=“senso:TemperatureObservation">
<sa:swe rdfa:property="weather:fahrenheit"/>
<sa:swe rdfa:rel="senso:occurred_when" resource="?time"/>
<sa:swe rdfa:rel="senso:observed_by" resource="senso:buckeye_sensor"/>
</sa:sml>
</swe:Quantity>
</swe:component>
<swe:value name=“weather-data">
2008-03-08T05:00:00,29.1
</swe:value>
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Semantically Annotated O&M
<swe:component name="time">
<swe:Time definition="urn:ogc:def:phenomenon:time" uom="urn:ogc:def:unit:date-time">
<sa:swe rdfa:about="?time" rdfa:instanceof="time:Instant">
?time rdf:type
time:Instant
<sa:sml
rdfa:property="xs:date-time"/>
</sa:swe> ?time xs:date-time "2008-03-08T05:00:00"
</swe:Time>
</swe:component>
<swe:component name="measured_air_temperature">
<swe:Quantity definition="urn:ogc:def:phenomenon:temperature“
uom="urn:ogc:def:unit:fahrenheit">
<sa:swe rdfa:about="?measured_air_temperature“
?measured_air_temperature rdf:type senso:TemperatureObservation
rdfa:instanceof=“senso:TemperatureObservation">
?measured_air_temperature
weather:fahrenheit "29.1"
<sa:swe
rdfa:property="weather:fahrenheit"/>
?measured_air_temperature
senso:occurred_when
?time
<sa:swe
rdfa:rel="senso:occurred_when"
resource="?time"/>
?measured_air_temperature
senso:observed_by
senso:buckeye_sensor
<sa:swe
rdfa:rel="senso:observed_by"
resource="senso:buckeye_sensor"/>
</sa:sml>
</swe:Quantity>
</swe:component>
<swe:value name=“weather-data">
2008-03-08T05:00:00,29.1
</swe:value>
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Semantic Query
Semantic Temporal Query
•
•
•
Model-references from SML to OWL-Time ontology concepts provides the
ability to perform semantic temporal queries
Supported semantic query operators include:
– contains: user-specified interval falls wholly within a sensor reading
interval (also called inside)
– within: sensor reading interval falls wholly within the user-specified
interval (inverse of contains or inside)
– overlaps: user-specified interval overlaps the sensor reading interval
Example SPARQL query defining the temporal operator ‘within’
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Semantic Sensor Data-to-Knowledge Architecture
Knowledge
• Object-Event Relations
• Spatiotemporal Associations
Semantic Analysis and Query
• Provenance/Context
Data Storage
(Raw Data, XML, RDF)
Information
• Entity Metadata
Feature Extraction and Entity Detection
• Feature Metadata
Semantic
Annotation
Data
• Raw Phenomenological Data
Sensor Data Collection
Ontologies
• Space Ontology
• Time Ontology
• Situation Theory Ontology
• Domain Ontology
50
Presentation Outline
1.
Motivating scenario
2. Sensor Web Enablement
3. Metadata in the domain of Sensors
4. Semantic Sensor Web
5. Prototyping the Semantic Sensor Web
Prototyping the Semantic Sensor Web
Application 1: Temporal Semantics for Video Sensor Data
• Semantically annotated police cruiser videos collected from
YouTube with model references to an OWL-Time ontology
• Enables time-interval based queries, such as contains, within,
overlaps
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Temporal Semantics for Video Sensor Data
Data Collection
Data Source
(e.g., YouTube)
Extraction & Metadata Creation
Video
Conversion
AVI
Converted
Videos
Filtering
& OCR
Time & Date
information
SML
Annotation
Generation
Storage
Query
UI
SML
(XML-DB)
SML Interface
Google Maps
Ontology
(OWL/RDF-DB)
Ontology
Interface
GWT
(Java to Ajax)
OWL-Time
Annotation
Generation
53
Temporal Semantics for Video Sensor Data
Optical Character Recognition (OCR)
– Feature Extraction
– Temporal Entity Recognition
– Metadata Generation & Semantic annotation
54
Demo: Temporal Semantics for Video Sensor Data
Demo: http://knoesis.wright.edu/library/demos/ssw/prototype.htm
55
Prototyping the Semantic Sensor Web
Application 2: Semantic Sensor Observation Service
• Semantically annotated weather data collected from
BuckeyeTraffic.org with model references to an OWL-Time
ontology, geospatial ontology, and weather ontology
• Capable of multi-level weather queries and inferences on a network
of multi-modal sensors
56
SOS-S Architecture
S-SOS Client
BuckeyeTraffic.org
Collect Sensor Data
HTTP-GET
Request
O&M-S or SML-S
Response
Semantic Sensor Observation Service
Get Observation
Oracle
SensorDB
Describe Sensor
Get Capabilities
Ontology & Rules
SWE
Annotated SWE
• Weather
• Time
SA-SML Annotation Service
• Space
57
SOS-S Data Collection
BuckeyeTraffic, http://www.buckeyetraffic.org/
58
S-SOS Ontology Concepts
Location
Sensor
observed_by
occurred_where
occurred_when
Observation
Time
described
measured
Weather_Condition
Phenomena
subClassOf
Temperature
Key
subClassOf
Precipitation
• Sensor Ontology
…
• Weather Ontology
• Temporal Ontology
• Geospatial Ontology
59
S-SOS Ontology Concepts
Weather_Condition
subClassOf
Wet
Instances of simple weather
conditions created directly
from BuckeyeTraffic data
Icy
Blizzard
Freezing
Instances of complex weather
conditions inferred through
rules
Potentially Icy
60
S-SOS Rules for Weather Conditions
• Rules allow inferred knowledge from the sensor data
• For example: Based on temperature, wind speed,
precipitation, etc., we can infer the “potential” road
condition the type of storm being observed
Example
Potential_Ice_with_Rain_and_Celcius_Temp
• Blizzard
• Potential Ice
• Freezing
• etc.
Observation(?obs) ^
measured(?obs, ?precip) ^
Rain(?precip) ^
measured(?obs, ?temp) ^
Temperature(?temp) ^
temperature_value(?temp, ?tval) ^
lessThanOrEqual(?tval, 0) ^
unit_of_measurement(?temp, “celcius")
→ described(?obs, Potential_Ice)
61
SOS-S Client
HTTP-GET Request
http://knoesis1.wright.edu/weather/weather
?service=SOS
&version=1.0
&request=GetObservation
&offering=WEATHER_DATA
&format=application/com-xml
&time=2008-03-08T05:00:00Z/2008-03-08T06:00:00Z
&interval_type=within
&weather_condition=potentially_icy
O&M-S Response
<swe:Time definition="urn:ogc:def:phenomenon:time"
uom="urn:ogc:def:unit:date-time">
<sa:swe rdfa:about="?time“rdfa:instanceof="time:Instant">
<sa:sml rdfa:property="xs:date-time"/>
</sa:swe>
</swe:Time>
<swe:value name=“weather-data">
2008-03-08T05:00:00,29.1
</swe:value>
Semantic Sensor Observation Service
Get Observation
Describe Sensor
Get Capabilities
62
SOS-S Client
HTTP-GET Request
http://knoesis1.wright.edu/weather/weather
?service=SOS
&version=1.0
&request=GetObservation
&offering=WEATHER_DATA
&format=application/com-xml
&time=2008-03-08T05:00:00Z/2008-03-08T06:00:00Z
&interval_type=within
&weather_condition=potentially_icy
O&M-S Response
<swe:Time definition="urn:ogc:def:phenomenon:time"
uom="urn:ogc:def:unit:date-time">
<sa:swe rdfa:about="?time“rdfa:instanceof="time:Instant">
<sa:sml rdfa:property="xs:date-time"/>
</sa:swe>
</swe:Time>
<swe:value name=“weather-data">
2008-03-08T05:00:00,29.1
</swe:value>
Semantic Sensor Observation Service
Get Observation
Describe Sensor
Get Capabilities
63
Demo: Semantic Sensor Observation Service
Demo:
http://knoesis.wright.edu/research/semsci/application_domain/sem_sensor/afrl/demo/ssw.html
64
Spatial, Temporal, Thematic Analytics
within the Semantic Sensor Web
Value to Sensor Networks
•
Simple (Analyze Infrastructure):
– What types of sensors are available?
– What sensors can observe a particular phenomenon at a given
geolocation?
– Get all observations for a particular geolocation during a given time
interval.
•
Complex (More background thematic information):
– How do I detect weather events from observation data?
– What do I know about the buildings (georeferenced) in this image?
– Which sensors cover an area which intersects with a planned event?
66
Challenges
• Data Modeling and Querying:
– Thematic relationships can be directly stated but
many spatial and temporal relationships (e.g.
distance) are implicit and require additional
computation
– Temporal properties of paths aren’t known until
query execution time … hard to index
• RDFS Inferencing:
– If statements have an associated valid time this must
be taken into account when performing inferencing
– (x, rdfs:subClassOf, y) : [1, 4] AND (y,
rdfs:subClassOf, z) : [3, 5]  (x, rdfs:subClassOf, z) :
[3, 4]
67
Work to Date
• Ontology-based model for spatiotemporal data using
temporal RDF 1
– Illustrated benefits in flexibility, extensibility and expressiveness
as compared with existing spatiotemporal models used in GIS
• Definition, implementation and evaluation of
corresponding query operators using an extensible DBMS
(Oracle) 2
– Created SQL Table Functions which allow SPARQL graph
patterns in combination with Spatial and Temporal predicates
over Temporal RDF graphs
1.
Matthew Perry, Farshad Hakimpour, Amit Sheth. "Analyzing Theme, Space and Time: An Ontology-based
Approach", Fourteenth International Symposium on Advances in Geographic Information Systems (ACM-GIS
'06), Arlington, VA, November 10 - 11, 2006
2.
Matthew Perry, Amit Sheth, Farshad Hakimpour, Prateek Jain. "What, Where and When: Supporting Semantic,
Spatial and Temporal Queries in a DBMS", Kno.e.sis Center Technical Report. KNOESIS-TR-2007-01, April 22,
2007
68
Sample STT Query
select * from table (spatial_find(
‘(?sensor :location ?loc)
(?sensor :generatedObservation ?obs)
(?obs :featureOfInterest :Blizzard)', ‘loc',
'POINT(-149.40572 61.29302)',
'GEO_DISTANCE(distance=100 unit=mile)‘);
Scenario (Blizzard Detection): Find all sensors that have observed a Blizzard
within a 100 mile radius of a given location.
Query specifies
(1) a relationship between a sensor, observation, blizzard, and location
(2) a spatial filtering condition based on the proximity of the sensor and the
defined point
69
Current Work & Future Demo
•
MesoWest Dataset
– 20,000 Sensor Systems predominately within United States
– Archive observation data since April 2002
– Building dataset of ~1 billion triples
•
Trusted Sensors
– Reputation based framework to detect trustworthiness of sensors
– Model-based diagnosis to detect abnormal and/or malicious sensor
behavior
•
Abductive Perception
– Generating explanations for sensor observations through abductive
inference and ranking
– Validating explanations through deductive inference prediction and
comparison with subsequent observation data
70
Future Work
• Incorporation of spatial ontology in order to include spatial analytics
and query (perhaps with OGC GML Ontology or ontology developed
by W3C Geospatial Incubator Group - GeoXG)
• Extension with enhanced datasets including MesoWest (Univ. of
Utah) and OOSTethys (OGC Oceans IE)
• Trust calculation and analysis over multi-layer sensor networks
• Integration of framework with emergent applications, including
video on mobile devices running Android OS
71
References
•
Cory Henson, Amit Sheth, Prateek Jain, Josh Pschorr, Terry Rapoch, “Video on the Semantic Sensor
Web,” W3C Video on the Web Workshop, December 12-13, 2007, San Jose, CA, and Brussels,
Belgium
•
Matthew Perry, Amit Sheth, Farshad Hakimpour, Prateek Jain. “Supporting Complex Thematic,
Spatial and Temporal Queries over Semantic Web Data,” Second International Conference on
Geospatial Semantics (GEOS ’07), Mexico City, MX, November 29-30, 2007
•
Matthew Perry, Farshad Hakimpour, Amit Sheth. “Analyzing Theme, Space and Time: An Ontologybased Approach,” Fourteenth International Symposium on Advances in Geographic Information
Systems (ACM-GIS ’06), Arlington, VA, November 10-11, 2006
•
Farshad Hakimpour, Boanerges Aleman-Meza, Matthew Perry, Amit Sheth. “Data Processing in
Space, Time, and Semantic Dimensions,” Terra Cognita 2006 – Directions to Geospatial Semantic
Web, in conjunction with the Fifth International Semantic Web Conference (ISWC ’06), Athens, GA,
November 6, 2006
•
Mike Botts, George Percivall, Carl Reed, John Davidson, “OGC Sensor Web Enablement: Overview
and High Level Architecture (OGC 07-165),” Open Geospatial Consortium White Paper, December
28, 2007.
•
Open Geospatial Consortium, Sensor Web Enablement WG,
http://www.opengeospatial.org/projects/groups/sensorweb
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Kno.e.sis Labs (3rd floor, Joshi)
Semantic Sciences Lab (Dr Sheth)
Bioinformatics Lab (Dr Raymer)
Semantic Web Lab (Dr Sheth + Dr. S.Wang)
Service Research Lab (Dr Sheth)
Metadata and Languages Lab (Dr Prasad)
Data Mining Lab (Dr Dong)
Joint Proposals With Each
Sensor Networking Bin Wang
Kno.e.sis Members – a subset
References
Semantic Sensor Web projects:
http://knoesis.org/research/semsci/application_domain/sem_sensor
/
Spatio-temporal-thematic Query Processing & Reasoning:
http://knoesis.org/research/semweb/projects/stt/
Demos at: http://knoesis.wright.edu/library/demos/
Publications: http://knoesis.wright.edu/library
Rest: http://knoesis.org
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Semantic Sensor Web - Wright State University