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.
|Title of host publication||Proceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|Publication status||Published - 2015 Oct 19|
|Event||16th IEEE International Conference on Information Reuse and Integration, IRI 2015 - San Francisco, United States|
Duration: 2015 Aug 13 → 2015 Aug 15
|Name||Proceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015|
|Other||16th IEEE International Conference on Information Reuse and Integration, IRI 2015|
|Period||15/8/13 → 15/8/15|
Bibliographical notePublisher Copyright:
© 2015 IEEE.
All Science Journal Classification (ASJC) codes
- Information Systems
- Information Systems and Management
- Electrical and Electronic Engineering