Paper Info
Title
SDPA: Sensor Data Processing Architecture for Modeling Semantic Data from Sensor Steams
Abstract
With the rapid deployment of a number of sensors, it is crucial to efficiently manage their data streams with heterogeneous properties. To achieve various sensor applications such as discovery and mashup, a method of retrieving meaningful information from raw sensor data is required. However, it is hard to analyze and represent the sensor data since sensors generate streaming data of different patterns and continuously transmit the observations to servers in real-time. In this paper, we propose a sensor data processing architecture to retrieve meaningful information from raw sensor data. In particular, we adopt a machine leaning strategy for sensor data analysis. Semantic sensor data are modeled based on ontologies. The processed semantic data construct a semantic knowledgebase, which allows a user to make the best use of sensor information. We present an evaluation of our approach by using real-world datasets and experimental results.
Year
DOI
Venue
2015
10.1109/IRI.2015.13
Information Reuse and Integration
Keywords
Field
DocType
sensor network, data processing architecture, ontology, semantic data representation
Data architecture,Data mining,Data modeling,Data stream mining,Computer science,Data mapping,Visual sensor network,Sensor web,Wireless sensor network,Semantic computing
Conference
Citations 
PageRank 
References 
0
0.34
15
Authors
4
Name
Order
Citations
PageRank
Seungmin Seo1245.68
Sejin Chun2213.56
Byungkook Oh3263.69
Kyong-Ho Lee4133.23