My name is Michael Cooney and I am 22 years old. I am an undergraduate student at Wright State University majoring in Computer Science and I plan to graduate in 2012. I currently work in research for Kno.e.sis which deals primarily with the Semantic web. Semantic Web is a term coined by World Wide Web Consortium (W3C) director, Tim Berners-Lee. It describes methods and technologies to allow machines to understand the meaning - or "semantics" - of information on the World Wide Web. The project I am currently working on is called Twitris and is a Semantic Web application that facilitates understanding of social perceptions by Semantics-based processing of massive amounts of event-centric data. The project is up and running and can be found here.
Twitris-IAC, a Semantic Web application that facilitates understanding of social perceptions by Semantics-based processing of massive amounts of event-centric data. Twitris IAC addresses challenges in large scale processing of social data, preserving spatio-temporal-thematic properties. It also covers context based semantic integration of multiple Web resources and expose semantically enriched social data to the public domain. Semantic Web technologies enable the system's integration and analysis abilities.
One of fastest growing fields right now is Healthcare. This Android application is an attempt to give you more control over your own healthcare by alerting you when a problem could be happening well before you actually show symptoms. This will allow people to take action before they are so sick that they have to go to the doctor.
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.
Sensors are increasingly being deployed for continuous monitoring of physical phenomena, resulting in avalanche of sensor data. Current sensor data streams provide summaries (e.g., min., max., avg.) of how phenomena change over time; however, such summaries are of little value to decision makers attempting to attain an insight or an intuitive awareness of the situation. Feature-streams, on the other hand, provide a higher-level of abstraction over the sensor data and provide actionable knowledge useful to the decision maker. This work presents an approach to generate feature-streams in real-time. This is accomplished through the application of ontological domain knowledge in order to integrate multiple, multimodal, heterogeneous low-level sensor data streams and infer the existence of real-world events like Blizzard, RainStorm etc. The generated feature-streams are publicly accessible on the Linked Open Data (LOD) Cloud.
To make the site easier to update, Drupal CMS was used to create the new website for Knoesis.
This project was to tie together trust and perception of sensor data into a visualization to present to the Air Force Research Lab(AFRL).
Harshal Patni, Cory Henson, Michael Cooney, Amit Sheth, Krishnaprasad Thirunarayan, 'Real-Time Semantic Analysis of Sensor Streams,' Department of Computer Science, Wright State University, Dayton, Ohio, 2011.
Ashutosh Jadhav, Hemant Purohit, Pavan Kapanipathi, Pramod Ananthram, Ajith Ranabahu, Vinh Nguyen, Pablo N. Mendes, Alan Gary Smith, Michael Cooney, and Amit Sheth, 'Twitris 2.0 : Semantically Empowered System for Understanding Perceptions From Social Data,' Department of Computer Science, Wright State University, Dayton, Ohio, 2011.