Aitor Rodriguez, Gabriel Anzaldi, Roberto Garcia
Friday 3 july 2015
13:45 - 14:00h at Central America (level 0)
Themes: (T) Water resources and hydro informatics (WRHI), (ST) Management support systems and serious gaming
Parallel session: 16H. Water resources - Serious gaming
To date, Semantic Sensor Web research and development has focused on establishing common techniques and practices that homogenize how to discover, collect and integrate information from sensors. However, as these processes are starting to be overcome and huge databases of sensor data begin to accumulate, the focus can now change to improve data management and reduce information overload, helping users discard irrelevant information and make its exploration easy and intuitive. This paper depicts the development of an architecture that focuses on water sensors data fusion, plus additional data from heterogeneous sources to contextualize sensor data. By mixing and semantically annotating data, the architecture better assist users to autonomously understand data and then, generate new management strategies. The proposal is based on applying Semantic Streams Technology (SST) to collect and integrate continuous sensor data from multiple databases in form of RDF streams. The RDF streams are generated by transforming SQL-structured data into semantic data by using a knowledge base, thus enriching the information contained in the database. Hence, abstracting sensor data stored in a database through the R2RML technique generates the RDF streams. Moreover, SST processes sensor data using real-time semantic stream reasoning over the generated RDF streams from noisy sensor data to support the decision process even in large and complex scenarios. The proposed architecture has been analysed by combining it with the infrastructure deployed over the water supply and distribution chain implemented in the context of the FP7-WatERP project, where sensor data is continuously stored in OGC-SOS server database. This data is transformed into RDF streams using also the Water Management Ontology developed in WatERP. The semantic stream reasoning is performed by C-SPARQL permitting the architecture to register and execute queries, which provide water managers knowledge to improve water resource management. Consequently, the proposed architecture enhances current state of the art in: (i) avoiding information replication by wrapping data streams with semantic stream technologies; and (ii) proving real-time reasoning, decision support and rapid actuation against resource mismanagements or other critical situations.