Semantically Enabling the SEMAT Project: Extending Marine Sensor Networks for Decision Support and Hypothesis Testing

Myers, Trina; Atkinson, Ian; Johnstone, Ron

doi:10.1109/cisis.2010.46

Cited by 8 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sensor networks have already proven to be useful in many areas such as environmental monitoring [ 44 , 45 ], agriculture and water monitoring [ 46 , 47 ], ecosystem monitoring [ 31 , 48 ] and coastal monitoring [ 21 , 49 ].…”

Section: Discussionmentioning

confidence: 99%

Smart Query Answering for Marine Sensor Data

Shahriar

Souza

Timms

2011

Sensors

View full text Add to dashboard Cite

We review existing query answering systems for sensor data. We then propose an extended query answering approach termed smart query, specifically for marine sensor data. The smart query answering system integrates pattern queries and continuous queries. The proposed smart query system considers both streaming data and historical data from marine sensor networks. The smart query also uses query relaxation technique and semantics from domain knowledge as a recommender system. The proposed smart query benefits in building data and information systems for marine sensor networks.

show abstract

Section: Discussionmentioning

confidence: 99%

Smart Query Answering for Marine Sensor Data

Shahriar

Souza

Timms

2011

Sensors

View full text Add to dashboard Cite

show abstract

“…Rather, the questions can be as flexible, and may evolve as new data becomes available and/or as ideas emerge [35]. For example, a researcher may initially propose a bleaching event with two factors: Sea Surface Temperature (SST) and salinity.…”

Section: Versatile Hypothesis Designmentioning

confidence: 99%

Eco-informatics modelling via semantic inference

Myers

Atkinson

2013

Information Systems

Self Cite

View full text Add to dashboard Cite

a b s t r a c tThere is a demand for new and evolved research practices resulting from the so called ''data deluge'' emerging from high volume digital collection methods. As the volume of raw data increases traditional data processing methodologies, especially those involving manual manipulation are becoming increasingly difficult to manage. This paper presents the ''Semantic Reef'' architecture that offers an alternative approach to the development, application and execution of observational hypotheses involving studies of coral reef ecosystems. The Semantic Reef Knowledge Representation system is an eco-informatics application designed to assist in the integration of remotely sensed data streams and historic data sets supporting flexible hypothesis design and knowledge extraction. The system is an ontology-based architecture built to allow researchers to combine disjoint data sets into a single Knowledge Base for modelling the impact of climate change on coral reef ecosystems. The Knowledge Base consists of a hierarchy of ontologies developed to maximise usability and reusability by separating data instances from the concept descriptions. The model can be effectively reused to extract or disclose phenomena of any coral reef. This paper both demonstrates and describes a performance analysis of the Semantic Reef knowledge system.

show abstract

“…Trina Myers et al [4] proposed Smart Environmental Monitoring and Analysis Technologies (SEMAT) project to develop advanced wireless sensor networks to collect, store, process and interpret coastal data. The aim of the project is to build a smart wireless sensor architecture that allow the user to use their existing hardware and new technologies within a multi-scale monitoring system that allow them to interrogate and task aspects of the sensor network according to need and monitoring outcomes.…”

Section: Heterogeneous Data Management On Environmental Sensors Usingmentioning

confidence: 99%

Heterogeneous Data Management on Environmental Sensors Using Ontology Mapping

Ramar¹,

Mirnalinee²

2013

LNIT

View full text Add to dashboard Cite

Wireless sensors are used for monitoring environments such as climate change, water quality and disaster management. The critical task is interpretation of the complex multidimensional data generated by sensors and interoperability among various system and handling of heterogeneous data. There is also need to get a common understanding and decision making by various agencies and research institutions. To handle these requirements importance is given to both syntactic and semantic nature of the data. The syntactic part is responsible for the interoperability of the sensor data. Interoperability is addressed by using Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) framework has developed a suite of specifications enable sensors to be accessible and controllable via the web. Ontologies are widely used as a means for solving the information heterogeneity problems because of their capability to provide explicit meaning to the information. In this paper we propose a Service Oriented Architecture (SOA) model for the interoperability of sensor networks data and semantics of the data. The heterogeneities between various sensor networks are reduced through ontology mapping. The service is available to the users through standard data formats like XML and protocol like SOAP.

show abstract

Semantically Enabling the SEMAT Project: Extending Marine Sensor Networks for Decision Support and Hypothesis Testing

Cited by 8 publications

References 5 publications

Smart Query Answering for Marine Sensor Data

Smart Query Answering for Marine Sensor Data

Eco-informatics modelling via semantic inference

Heterogeneous Data Management on Environmental Sensors Using Ontology Mapping

Contact Info

Product

Resources

About