SSW - Revision history

Admin: /* People */

2021-06-16T00:33:16Z

‎People

Cory: /* Presentations */

2012-06-24T10:37:59Z

‎Presentations

Cory: /* Presentations */

2012-06-24T10:15:44Z

‎Presentations

Pramod: /* Presentations */

2012-05-24T16:52:13Z

‎Presentations

Cory: /* Prototypes, Demos and Tools */

2012-04-16T15:15:03Z

‎Prototypes, Demos and Tools

Pramod: /* Publications */

2012-04-14T01:17:19Z

‎Publications

Pramod: /* Publications */

2012-04-14T01:12:18Z

‎Publications

Cory: /* Publications */

2012-03-01T16:38:49Z

‎Publications

Cory: /* Publications */

2012-03-01T16:27:43Z

‎Publications

Pramod: /* Prototypes, Demos and Tools */

2012-01-26T22:17:56Z

‎Prototypes, Demos and Tools

@@ Line 26: / Line 26: @@
 <strong>Faculty</strong>
 * [http://knoesis.wright.edu/amit Amit Sheth]
-* [http://www.engineering.wright.edu/~tkprasad T.K. Prasad]
+* [http://cecs.wright.edu/people/faculty/tkprasad/ Krishnaprasad Thirunarayan (T. K. Prasad)]
 <strong>Researchers</strong>

@@ Line 145: / Line 145: @@
 ==Presentations==
 *[http://www.knoesis.org/library/resource.php?id=1721 W3C Semantic Sensor Networks: Ontologies, Applications, and Future Directions] Cory Henson delivers plenary talk at the IERC AC4 Semantic Interoperability Workshop, Venice, Italy, June 19, 2012 (co-located with IoT Week 2012).
 *[http://knoesis.org/library/resource.php?id=1708 Trust Networks] T. K. Prasad delivers a tutorial at the The 2012 International Conference on Collaboration Technologies and Systems (CTS 2012), Denver, Colorado, USA, May 21-25, 2012.
 *[http://knoesis.org/library/resource.php?id=1639 Trust Networks] T. K. Prasad delivers an invited talk at the 5th Indian International Conference on Artificial Intelligence, Tumkur, India, December 14-16, 2011.

@@ Line 144: / Line 144: @@
 ==Presentations==
 *[http://knoesis.org/library/resource.php?id=1708 Trust Networks] T. K. Prasad delivers a tutorial at the The 2012 International Conference on Collaboration Technologies and Systems (CTS 2012), Denver, Colorado, USA, May 21-25, 2012.
 *[http://knoesis.org/library/resource.php?id=1639 Trust Networks] T. K. Prasad delivers an invited talk at the 5th Indian International Conference on Artificial Intelligence, Tumkur, India, December 14-16, 2011.

@@ Line 144: / Line 144: @@
 ==Presentations==
 *[http://knoesis.org/library/resource.php?id=1639 Trust Networks] T. K. Prasad delivers an invited talk at the 5th Indian International Conference on Artificial Intelligence, Tumkur, India, December 14-16, 2011.
 *[http://www.slideshare.net/knoesis/secure-semantics-empowered-rescue-environment SECURE: Semantics Empowered resCUe EnviRonmEnt] Amit Sheth delivers a demo talk at the 4th International Workshop on Semantic Sensor Networks 2011 (SSN11), co-located with The 10th ISWC, Bonn, Germany.

@@ Line 175: / Line 175: @@
 <UL>
 <LI>
-<strong>SECURE - Semantics Empowered resCUe enviRonmEnt: </strong>[http://www.youtube.com/watch?v=in2KMkD_uqg demo]<br/>This demo is a Semantic Web research effort towards a Physical-Cyber-Social system that uses background knowledge on the web, and an ontology of perception, to reason over the sensor observations generated by a mobile robot.
+<strong>SECURE - Semantics Empowered Rescue Environment: </strong>[http://www.youtube.com/watch?v=in2KMkD_uqg demo]<br/>This demo is a Semantic Web research effort towards a Physical-Cyber-Social system that uses background knowledge on the web, and an ontology of perception, to reason over the sensor observations generated by a mobile robot.
 </LI>
 <LI>
-<strong>Real Time Feature Streams: </strong>[http://knoesis1.wright.edu/EventStreams prototype], [http://www.youtube.com/watch?v=_ews4w_eCpg demo]<br/>Real Time Feature Streams focuses on reasoning over lower-level raw sensor data streams to detect higher-level abstractions called features (a concept that represents a real world entity like Blizzard, RainStorm etc) in real-time. The feature-streams are added to the Linked Open Data Cloud (LOD).Main contributions involve -
+<strong>Real Time Feature Streams: </strong>[http://www.youtube.com/watch?v=_ews4w_eCpg demo]<br/>Real Time Feature Streams focuses on reasoning over lower-level raw sensor data streams to detect higher-level abstractions called features (a concept that represents a real world entity like Blizzard, RainStorm etc) in real-time. The feature-streams are added to the Linked Open Data Cloud (LOD).Main contributions involve -
 (1). Integration of multiple, multimodal, heterogeneous “low-level” sensor data streams to generate “high-level” feature streams.
 (2). The summarization is across the thematic dimension involving multiple data streams and the use of background knowledge as opposed to summarizations of single streams across temporal dimension (like min,max,average etc).
@@ Line 186: / Line 186: @@
 </LI>
 <LI>
-<strong>Sensor Discovery On Linked Data:</strong> [http://knoesis1.wright.edu/SSW_NEW_BETA/ prototype], [http://knoesis.org/projects/sensorweb/demos/sensor_discovery_on_lod/sample.htm demo]<br/>This application provides ability to search for sensors based on location names rather than location coordinates. Main contributions include: (1) large sensor description dataset (> 20,000 weather stations) with links to "nearby" locations described in Geonames.org dataset, (2) large sensor observation dataset (> 1.7 billion triples) with links to sensor dataset, (3) both datasets openly available on LOD, (4) extend SemSOS (described above) to semantically annotate SOS GetCapabilities document with model references to Geonames locations.
+<strong>Sensor Discovery On Linked Data: </strong>[http://archive.knoesis.org/projects/sensorweb/demos/sensor_discovery_on_lod/sample.htm demo]<br/>This application provides ability to search for sensors based on location names rather than location coordinates. Main contributions include: (1) large sensor description dataset (> 20,000 weather stations) with links to "nearby" locations described in Geonames.org dataset, (2) large sensor observation dataset (> 1.7 billion triples) with links to sensor dataset, (3) both datasets openly available on LOD, (4) extend SemSOS (described above) to semantically annotate SOS GetCapabilities document with model references to Geonames locations.
 </LI>
 <LI>
-<strong>Semantic Sensor Observation Service (MesoWest):</strong> [http://knoesis.org/projects/sensorweb/demos/semsos_mesowest/ssos_demo.htm demo]<br/>SemSOS is an extension of SOS to allow SOS queries and access to an ontological knowledgebase. Main contributions include: (1) sensor/observation ontology based on Observations and Measurements (O&M), (2) semantic annotation of O&M and SML documents, (2) mappings and translation scripts to convert O&M and SML into RDF (and vice-versa), (3) rule-based reasoning to infer events from low-level sensor data, (4) query translation from SOS format into SPARQL.</LI>
+<strong>Semantic Sensor Observation Service (MesoWest): </strong>[http://archive.knoesis.org/projects/sensorweb/demos/semsos_mesowest/ssos_demo.htm demo]<br/>SemSOS is an extension of SOS to allow SOS queries and access to an ontological knowledgebase. Main contributions include: (1) sensor/observation ontology based on Observations and Measurements (O&M), (2) semantic annotation of O&M and SML documents, (2) mappings and translation scripts to convert O&M and SML into RDF (and vice-versa), (3) rule-based reasoning to infer events from low-level sensor data, (4) query translation from SOS format into SPARQL.</LI>
 <LI>
-<strong>Semantic Sensor Observation Service (Buckeye Traffic): </strong>[http://knoesis.org/projects/sensorweb/demos/semsos_buckeye/ssw.html demo]
+<strong>Semantic Sensor Observation Service (Buckeye Traffic): </strong>[http://archive.knoesis.org/projects/sensorweb/demos/semsos_buckeye/ssw.html demo]
 </LI>
-<LI><strong>Video on the Semantic Sensor Web:</strong> [http://knoesis.org/projects/sensorweb/demos/video_on_ssw/prototype.htm demo]
+<LI><strong>Video on the Semantic Sensor Web: </strong>[http://archive.knoesis.org/projects/sensorweb/demos/video_on_ssw/prototype.htm demo]
 </LI>
 </UL>

@@ Line 87: / Line 87: @@
 * [http://knoesis.wright.edu/library/resource.php?id=836 Trust Model for Semantic Sensor and Social Networks: A Preliminary Report]
-** Pramod Anantharam,Cory Henson,Krishnaprasad Thirunarayan and Amit Sheth
+** Pramod Anantharam, Cory Henson, Krishnaprasad Thirunarayan and Amit Sheth
 ** in Proceedings of 2010 National Aerospace & Electronics Conference (NAECON), Dayton Ohio, July 14-16th, 2010

@@ Line 41: / Line 41: @@
 ==Publications==
 <strong>2012</strong>
 * [http://www.knoesis.org/library/resource.php?id=1682 Semantic Perception: Converting Sensory Observations to Abstractions]

@@ Line 42: / Line 42: @@
 <strong>2012</strong>
-* [http://www.computer.org/csdl/mags/ic/2012/02/mic2012020026-abs.html Semantic Perception: Converting Sensory Observations to Abstractions]
+* [http://www.knoesis.org/library/resource.php?id=1682 Semantic Perception: Converting Sensory Observations to Abstractions]
 ** Cory Henson, Amit Sheth, and Krishnaprasad Thirunarayan
 ** IEEE Internet Computing, Special Issue on Context-Aware Computing, March/April 2012

@@ Line 171: / Line 171: @@
 <UL>
 <LI>
-<strong>SECURE - Semantics Empowered resCUe enviRonmEnt: </strong>[http://www.youtube.com/watch?v=gHn9aCt9zQU demo]<br/>This demo is a Semantic Web research effort towards a Physical-Cyber-Social system that uses background knowledge on the web, and an ontology of perception, to reason over the sensor observations generated by a mobile robot.
+<strong>SECURE - Semantics Empowered resCUe enviRonmEnt: </strong>[http://www.youtube.com/watch?v=in2KMkD_uqg demo]<br/>This demo is a Semantic Web research effort towards a Physical-Cyber-Social system that uses background knowledge on the web, and an ontology of perception, to reason over the sensor observations generated by a mobile robot.
 </LI>
 <LI>