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Full-Text Articles in Life Sciences
The Ssn Ontology Of The W3c Semantic Sensor Network Incubator Group, Michael Compton, Payam Barnaghi, Luis Bermudez, Raul Garcia-Castro, Oscar Corcho, Simon Cox, John Graybeal, Manfred Hauswirth, Cory Andrew Henson, Arthur Herzog, Vincent Huang, Krzysztof Janowicz, W. David Kelsey, Danh Le Phuoc, Laurent Lefort, Myriam Leggieri, Holger Neuhaus, Andriy Nikolov, Kevin Page, Alexandre Passant, Amit P. Sheth, Kerry Taylor
The Ssn Ontology Of The W3c Semantic Sensor Network Incubator Group, Michael Compton, Payam Barnaghi, Luis Bermudez, Raul Garcia-Castro, Oscar Corcho, Simon Cox, John Graybeal, Manfred Hauswirth, Cory Andrew Henson, Arthur Herzog, Vincent Huang, Krzysztof Janowicz, W. David Kelsey, Danh Le Phuoc, Laurent Lefort, Myriam Leggieri, Holger Neuhaus, Andriy Nikolov, Kevin Page, Alexandre Passant, Amit P. Sheth, Kerry Taylor
Kno.e.sis Publications
The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations — the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.
An Efficient Bit Vector Approach To Semantics-Based Machine Perception In Resource-Constrained Devices, Cory Andrew Henson, Krishnaprasad Thirunarayan, Amit P. Sheth
An Efficient Bit Vector Approach To Semantics-Based Machine Perception In Resource-Constrained Devices, Cory Andrew Henson, Krishnaprasad Thirunarayan, Amit P. Sheth
Kno.e.sis Publications
The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data. Emerging solutions are using ontologies for expressive representation of concepts in the domain of sensing and perception, which enable advanced integration and interpretation of heterogeneous sensor data. The computational complexity of OWL, however, seriously limits its applicability and use within resource-constrained environments, such as mobile devices. To overcome this issue, we employ OWL to formally define the inference tasks needed for machine perception – explanation and discrimination – and then provide efficient algorithms for these tasks, using bit-vector encodings …
Computing Perception From Sensor Data, Payam Barnaghi, Frieder Ganz, Cory Andrew Henson, Amit P. Sheth
Computing Perception From Sensor Data, Payam Barnaghi, Frieder Ganz, Cory Andrew Henson, Amit P. Sheth
Kno.e.sis Publications
This paper describes a framework for perception creation from sensor data. We propose using data abstraction techniques, in particular Symbolic Aggregate Approximation (SAX), to analyse and create patterns from sensor data. The created patterns are then linked to semantic descriptions that define thematic, spatial and temporal features, providing highly granular abstract representation of the raw sensor data. This helps to reduce the size of the data that needs to be communicated from the sensor nodes to the gateways or highlevel processing components. We then discuss a method that uses abstract patterns created by SAX method and occurrences of different observations …
W3c Semantic Sensor Networks: Ontologies, Applications, And Future Directions, Cory Andrew Henson
W3c Semantic Sensor Networks: Ontologies, Applications, And Future Directions, Cory Andrew Henson
Kno.e.sis Publications
Plenary Talk discussing the W3C Semantic Sensor Network, including the ontology, applications, and future directions.
Semantics Of Perception: Towards A Semantic Web Approach To Machine Perception, Cory Andrew Henson, Amit P. Sheth
Semantics Of Perception: Towards A Semantic Web Approach To Machine Perception, Cory Andrew Henson, Amit P. Sheth
Kno.e.sis Publications
The acts of observation and perception provide the building blocks for all human knowledge (Locke, 1690); they are the processes from which all ideas are born; and the sole bond connecting ourselves to the world around us. Now, with the advent of sensor networks capable of observation, this world may be directly accessible to machines. Missing from this vision, however, is the ability of machines to glean semantics from observation; to apprehend entities from detected qualities; to perceive. The systematic automation of this ability is the focus of machine perception -- the ability of computing machines to sense and interpret …
Demonstration: Real-Time Semantic Analysis Of Sensor Streams, Harshal Patni, Cory Andrew Henson, Michael Cooney, Amit P. Sheth, Krishnaprasad Thirunarayan
Demonstration: Real-Time Semantic Analysis Of Sensor Streams, Harshal Patni, Cory Andrew Henson, Michael Cooney, Amit P. Sheth, Krishnaprasad Thirunarayan
Kno.e.sis Publications
The emergence of dynamic information sources – including sensor networks – has led to large streams of real-time data on the Web. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years [1]. With this coming data explosion, real-time analytics software must either adapt or die [2]. This paper focuses on the task of integrating and analyzing multiple heterogeneous streams of sensor data with the goal of creating meaningful abstractions, or features. These features are then temporally aggregated …
Demonstration: Secure - Semantics Empowered Rescue Environment, Pratikkumar Desai, Cory Andrew Henson, Pramod Anantharam, Amit P. Sheth
Demonstration: Secure - Semantics Empowered Rescue Environment, Pratikkumar Desai, Cory Andrew Henson, Pramod Anantharam, Amit P. Sheth
Kno.e.sis Publications
This paper demonstrates a Semantic Web enabled system for collecting and processing sensor data within a rescue environment. The real-time system collects heterogeneous raw sensor data from rescue robots through a wireless sensor network. The raw sensor data is converted to RDF using the Semantic Sensor Network (SSN) ontology and further processed to generate abstractions used for event detection in emergency scenarios.
Citizen Sensing: Opportunities And Challenges In Mining Social Signals And Perceptions, Amit P. Sheth
Citizen Sensing: Opportunities And Challenges In Mining Social Signals And Perceptions, Amit P. Sheth
Kno.e.sis Publications
Millions of persons have become 'citizens' of an Internet- or Web-enabled social community. Web 2.0 fostered the open environment and applications for tagging, blogging, wikis, and social networking sites that have made information consumption, production, and sharing so incredibly easy. An interconnected network of people who actively observe, report, collect, analyze, and disseminate information via text, audio, or video messages, increasingly through pervasively connected mobile devices, has led to what we term citizen sensing. In this talk, we review recent progress in supporting collective intelligence through intelligent processing of citizen sensing. Key issues we cover in this talk are: - …
Demonstration: Real-Time Semantic Analysis Of Sensor Streams, Harshal Kamlesh Patni, Cory Andrew Henson, Michael Cooney, Amit P. Sheth, Krishnaprasad Thirunarayan
Demonstration: Real-Time Semantic Analysis Of Sensor Streams, Harshal Kamlesh Patni, Cory Andrew Henson, Michael Cooney, Amit P. Sheth, Krishnaprasad Thirunarayan
Kno.e.sis Publications
The emergence of dynamic information sources - including sensor networks - has led to large streams of real-time data on the Web. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years. With this coming data explosion, real-time analytics software must either adapt or die. This paper focuses on the task of integrating and analyzing multiple heterogeneous streams of sensor data with the goal of creating meaningful abstractions, or features. These features are then temporally aggregated into feature …
Spatial Semantics For Better Interoperability And Analysis: Challenges And Experiences In Building Semantically Rich Applications In Web 3.0, Amit P. Sheth
Kno.e.sis Publications
No abstract provided.
Linked Sensor Data, Harshal Kamlesh Patni, Cory Andrew Henson, Amit P. Sheth
Linked Sensor Data, Harshal Kamlesh Patni, Cory Andrew Henson, Amit P. Sheth
Kno.e.sis Publications
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 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.
Sensor Data And Perception: Can Sensors Play 20 Questions, Cory Andrew Henson
Sensor Data And Perception: Can Sensors Play 20 Questions, Cory Andrew Henson
Kno.e.sis Publications
Currently, there are many sensors collecting information about our environment, leading to an overwhelming number of observations that must be analyzed and explained in order to achieve situation awareness. As perceptual beings, we are also constantly inundated with sensory data, yet we are able to make sense of our environment with relative ease. Why is the task of perception so easy for us, and so hard for machines; and could this have anything to do with how we play the game 20 Questions?
Provenance Aware Linked Sensor Data, Harshal Kamlesh Patni, Satya S. Sahoo, Cory Andrew Henson, Amit P. Sheth
Provenance Aware Linked Sensor Data, Harshal Kamlesh Patni, Satya S. Sahoo, Cory Andrew Henson, Amit P. Sheth
Kno.e.sis Publications
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 …
An Ontological Representation Of Time Series Observations On The Semantic Sensor Web, Cory Andrew Henson, Holger Neuhaus, Amit P. Sheth, Krishnaprasad Thirunarayan, Rajkumar Buyya
An Ontological Representation Of Time Series Observations On The Semantic Sensor Web, Cory Andrew Henson, Holger Neuhaus, Amit P. Sheth, Krishnaprasad Thirunarayan, Rajkumar Buyya
Kno.e.sis Publications
Time series observations are a common method of collecting sensor data. The Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) provides a standard representation for time series observations within the Observations and Measurements language, and therefore is in heavy use on the Sensor Web. By providing a common model, Observations and Measurements (O&M) facilitates syntax-level integration, but lacks the ability to facilitate semantic-level integration. This inability can cause problems with interoperability between disparate sensor networks that may have subtle variations in their sensing methods. An ontological representation of time series observations could provide a more expressive model and resolve problems …
Situation Awareness Via Abductive Reasoning For Semantic Sensor Data: A Preliminary Report, Krishnaprasad Thirunarayan, Cory Andrew Henson, Amit P. Sheth
Situation Awareness Via Abductive Reasoning For Semantic Sensor Data: A Preliminary Report, Krishnaprasad Thirunarayan, Cory Andrew Henson, Amit P. Sheth
Kno.e.sis Publications
Semantic sensor Web enhances raw sensor data with spatial, temporal, and thematic annotations to enable high-level reasoning. In this paper, we explore how abductive reasoning framework can benefit formalization and interpretation of sensor data to garner situation awareness. Specifically, we show how abductive logic programming techniques, in conjunction with symbolic knowledge rules, can be used to detect inconsistent sensor data and to generate human accessible description of the state of the world from consistent subset of the sensor data. We also show how trust/belief information can be incorporated into the interpreter to enhance reliability. For concreteness, we formalize weather domain …
Semsos: Semantic Sensor Observation Service, Cory Andrew Henson, Josh Pschorr, Amit P. Sheth, Krishnaprasad Thirunarayan
Semsos: Semantic Sensor Observation Service, Cory Andrew Henson, Josh Pschorr, Amit P. Sheth, Krishnaprasad Thirunarayan
Kno.e.sis Publications
Sensor observation service (SOS) is a Web service specification defined by the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) group in order to standardize the way sensors and sensor data are discovered and accessed on the Web. This standard goes a long way in providing interoperability between repositories of heterogeneous sensor data and applications that use this data. Many of these applications, however, are ill equipped at handling raw sensor data as provided by SOS and require actionable knowledge of the environment in order to be practically useful. There are two approaches to deal with this obstacle, make the …
Semantic Sensor Web, Amit P. Sheth, Cory Andrew Henson
Semantic Sensor Web, Amit P. Sheth, Cory Andrew Henson
Kno.e.sis Publications
No abstract provided.
Semantic Sensor Web, Cory Andrew Henson
Semantic Sensor Web, Amit P. Sheth, Satya S. Sahoo
Semantic Sensor Web, Amit P. Sheth, Satya S. Sahoo
Kno.e.sis Publications
Sensors are distributed across the globe leading to an avalanche of data about our environment. It is possible today to utilize networks of sensors to detect and identify a multitude of observations, from simple phenomena to complex events and situations. The lack of integration and communication between these networks, however, often isolates important data streams and intensifies the existing problem of too much data and not enough knowledge. With a view to addressing this problem, the semantic sensor Web (SSW) proposes that sensor data be annotated with semantic metadata that will both increase interoperability and provide contextual information essential for …
Semantic Sensor Web, Amit P. Sheth
Sensor Data Management, Cory Andrew Henson
Sensor Data Management, Cory Andrew Henson
Kno.e.sis Publications
No abstract provided.
Sensor Networks Survey, Cory Andrew Henson, Satya S. Sahoo
Sensor Networks Survey, Cory Andrew Henson, Satya S. Sahoo
Kno.e.sis Publications
No abstract provided.