Research Topic

From Real Time Sensor Streams to Real Time Feature Streams
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. In this work, we present an approach to generate feature-streams in real-time that helps users pose a query about our environment on the conceptual-level. This is accomplished through the application of ontological domain knowledge in order to integrate sensor data streams and infer the existence of real-world events. The generated feature-streams are publicly accessible on the Linked Open Data (LOD) Cloud

Prototype - (Real Time Feature Streams)
Affiliated Technical Report - (From Real Time Sensor Streams to Real Time Feature Streams)

Linked Sensor Data

A number of government, corporate, and academic organizations are collecting enormous amounts of data provided by environmental sensors. However, this data is too often locked within organizations and underutilized by the greater community. In this paper, we present a framework to make this sensor data openly accessible by publishing it on the Linked Open Data (LOD) Cloud. This is accomplished by converting raw sensor observations obtained from MesoWest to RDF and linking with other datasets on LOD. With such a framework, organizations can make large amounts of sensor data openly accessible, thus allowing greater opportunity for utilization and analysis.

Webpage - (Linked Sensor Data Wiki Page)
Affiliated Workshop Paper - (Linked Sensor Data)
Associated Tools - (Page Under Construction)
LinkedSensorData on LOD - (Linked Sensor Data LOD Page)

Semantic Library Toolkit

Semantic Library Toolkit is an online academic library publications management software based on Semantic Web technologies. The system includes a web based interface to add, edit and manage publications and author information. The data is stored in RDF making it easier to link to other datasets on Linked Open Data Cloud (LOD) like DBPedia (for author and conference information), GeoNames (for location information about the conference venues) etc. The tool is deployed and in-use at Kno.e.sis Center for managing academic publications. The system also contains a web service API to easily add new management tasks for the academic library. The tool is currently being integrated with Drupal 7.0 and would be released under an open source licence in the near future.

Webpage - (Kno.e.sis Library)

Provenance Aware Linked Sensor Data

Provenance, from the French word “provenir”, describes the lineage or history 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 management 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 work, we describe the implementation of the Sensor PMS for provenance tracking in the Linked Sensor Data.

Affiliated Workshop Paper - (Provenance Aware Linked Sensor Data)

Sensor Discovery on Linked Data

There has been a drive recently to make sensor data accessible on the Web. However, because of the vast number of sensors collecting data about our environment, finding relevant sensors on the Web is a non-trivial challenge. In this work, we present an approach to discovering sensors through a standard service interface over Linked Data. This is accomplished with a semantic sensor network middleware that includes a sensor registry on Linked Data and a sensor discovery service that extends the OGC Sensor Web Enablement. With this approach, we are able to access and discover sensors that are positioned near named-locations of interest..

Affiliated Technical Report - (Sensor Discovery on Linked Data)
Demo - (Video on Linked Sensor Data)
Prototype - (Prototype on Linked Sensor Data - Works best with Chrome)

RDF based Web Forms Infrastructure for Tarleton Research Laboratory

Biologists at Tarleton Research Laboratory perform scientific experiments and the data generated during these experiments is stored in MYSQL database. Retrieving specific results from database requires a programmer writing on-demand queries. This project involved converting the past experimental data into RDF and building new web forms to add and edit data into the RDF store. Finally using Cuebee (Knowledge Driven Query Formulation) to query scientific data in real-time without the intervention of a programmer.