provenance aware linked sensor data
DESCRIPTION
Provenance, from the French word “provenir”, describes the lineage or histo-ry of a data entity. Provenance is critical information in the sensors domain to identify a sensor and analyze the observation data over time and geographical space. In this paper, we present a framework to model and query the provenance information associated with the sensor data exposed as part of the Web of Data using the Linked Open Data conventions. This is accomplished by developing an ontology-driven provenance man-agement infrastructure that includes a representation model and query infrastructure. This provenance infrastructure, called Sensor Provenance Management System (PMS), is underpinned by a domain specific provenance ontology called Sensor Provenance (SP) ontology. The SP ontology extends the Provenir upper level provenance ontology to model domain-specific provenance in the sensor domain. In this paper, we describe the implementation of the Sensor PMS for provenance tracking in the Linked Sensor Data.Authors - Harshal Patni, Satya S. Sahoo, Cory Henson, Amit ShethTRANSCRIPT
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Provenance Aware Linked Sensor Data
Harshal Patni, Satya S. Sahoo, Cory Henson, Amit P. ShethOhio Center of Excellence in Knowledge enabled Computing (Kno.e.sis)
Wright State University, Dayton, OH
48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010.
SPOT2010 – 2nd Workshop on Trust and Privacy on the Social and Semantic Web
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OUTLINE
Motivating Scenario Provenance Sensor Provenance Management System
(Sensor PMS) Workflow Implementation Future Work Conclusion
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Motivating Scenario
• Find all the sensors which have observations related to a blizzard occurring in Nevada on 24th August 2005 at 11 AM
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Thematic information Temporal information
Spatial informationSensors in USA
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PROVENANCE
• Find all the sensors which have observations related to a blizzard occurring in Nevada on 24th August 2005 at 11 AM
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Thematic information Temporal information
Spatial information
PROVENANCE information of the observation is required for SENSOR DISCOVERY
PROVENANCE : History or Lineage of data entity
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Sensor PMS
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Data capture phase
Annotating data with Sensor Provenance
Ontology
Store the Provenance Aware Sensor Data
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Provenance Capture
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Weather Sensors
Camera SensorsSatellite Sensors
GPS SensorsSensor Dataset
Provenance Aware Linked Sensor Data
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Provenance Representation
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Sensor Dataset
Sensor Provenance Ontology
Annotate data using concepts in Provenance Sensor Ontology
Provenance Aware Linked Sensor Data
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Provenance Representation
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Sensor Ontology
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Provenance Representation
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Provenance Storage
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Sensor Dataset
Publicly Accessible
loca
tedNea
r
GeoNames Dataset: Geographic dataset contaning information about countries and 8 million
place names
Provenance Aware Sensor is adding provenance to Linked Sensor Data (on LoD).
Provenance Aware Linked Sensor Data
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MesoWest Service Data
OGC Observation and Measurement
(O&M)RDF Instance
Workflow Implementation
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Sensor Provenance Ontology
MesoWest is a Project at University of Utah, Department of Meteorology that collects observations for ~20,000 sensors in United States
Open Geo-Spatial Consortium standard (O&M) for encoding sensor descriptions and observations
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Workflow Implementation
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MesoWest Service Data
OGC Observation and Measurement
(O&M)RDF Instance
Sensor Provenance Ontology
Virtuoso RDF Store
• Virtuoso RDF store is an open source RDF triple store from Open Link software.
• Currently contains 1.7 billion triples of sensor observational data
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Future Work
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• Implementing the motivating scenario• Implement provenance query operators• Create a plug-in implementation that can add
provenance information to any processing of sensor dataset automatically
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Conclusion
• Developed an ontology-driven provenance management infrastructure for Sensor data called Sensor PMS
• Developed a domain specific provenance ontology by extending the provenir ontology
• Extension of standard ontology helps sharing and integration of provenance information across different domains
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• NIH RO1 Grant# 1R01HL087795-01A1• Dayton Area Graduate Studies Institute (DAGSI)• AFRL/DAGSI Research Topic SN08-8: "Architectures
for Secure Semantic Sensor Networks for Multi-Layered Sensing."
ACKNOWLEDGEMENTS
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QUESTIONS
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