Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

An Unified Representation of Context Knowledge Base for Mobile Context-Aware System

  • Received : 2013.10.14
  • Accepted : 2014.05.21
  • Published : 2014.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

To facilitate the implementation of a wide variety of context-aware applications based on mobile devices, general-purpose context-aware framework that applications can use by calling is needed. The context-aware framework is a middleware that performs the sensing, reasoning, and retrieving based on the knowledge base. The knowledge base must systematically represent the information required on the behavior of the context-aware framework, such as context information and reasoning information. It must also provide functions for storage and retrieval. To date, previous research on the representation of the context information have been carried out, but studies on the unified representation of the knowledge base has seen little progress. This study defines the knowledge base as the unified context information, and proposes the UniOWL, which can do a good job of representing it. UniOWL is based on OWL and represents the information that is necessary for the operation of the context-aware framework. Therefore, UniOWL greatly facilitates the implementation of the knowledge base on a context-aware framework.

Keywords

References

  1. J. Gu and G. Chen, "Design of physical and logical context aware middleware," International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 5, no. 1, pp. 113-130, 2012.
  2. C. Zhu, C. Guo, J. Wang, and T. T. Tay, "Towards scalability issue in ontology-based context-aware systems," in Proceedings of the International Conference on Software and Computer Applications (ICSCA 2012), Singapore, 2012, pp. 127-131.
  3. P. D. Costa, L. F. Pires, M. van Sinderen, and T. Broens, "Controlling services in a mobile contextaware infrastructure," in Proceedings of the 2nd Workshop on Context Awareness for Proactive Systems (CAPS2006), Kassel, Germany, 2006, pp. 153-166.
  4. S. Ickin, K. Wac, M. Fiedler, L. Janowski, J. H. Hong, and A. K. Dey, "Factors influencing quality of experience of commonly used mobile applications," IEEE Communications Magazine, vol. 50, no. 4, pp. 48-56, 2012.
  5. H. Liberman, T. Selker, "Out of context: computer systems that adapts to, and learn from, context," IBM Systems Journal, vol. 39, no. 3-4, pp. 617-632, 2000. https://doi.org/10.1147/sj.393.0617
  6. J. Jeong and D. Bang, "A context-aware system supporting distributed processing and multireasoning," in Proceedings of Korea Computer Congress, vol. 39, no. 1D, pp. 91-93, 2012.
  7. A. K. Dey, "Understanding and using context," Personal Ubiquitous Computer, vol. 5, no. 1, pp. 4-7, 2001. https://doi.org/10.1007/s007790170019
  8. L. S. Rosenthal and A. K. Dey, "Towards maximizing the accuracy of human-labeled sensor data," in Proceedings of the 15th International Conference on Intelligent User Interfaces, Hong Kong, China, 2010, pp. 259-268.
  9. M. Miraoui, C. Tadj, C. ben Amar, "Context modeling and context-aware service adaptation for pervasive computing systems," International Journal of Computer & Information Science & Engineering, vol. 2, no. 3, pp. 148-157, 2008.

Cited by

  1. A light-weight secure information transmission and device control scheme in integration of CPS and cloud computing vol.52, 2017, https://doi.org/10.1016/j.micpro.2016.08.001
  2. An Efficient Cooperative Neighbor Discovery Framework of Cognitive Radio Ad-Hoc Networks for Future Internet of Things vol.91, pp.4, 2016, https://doi.org/10.1007/s11277-015-3143-2
  3. Description and classification for facilitating interoperability of heterogeneous data/events/services in the Internet of Things vol.256, 2017, https://doi.org/10.1016/j.neucom.2016.03.115
  4. Sustainable Wearables: Wearable Technology for Enhancing the Quality of Human Life vol.8, pp.5, 2016, https://doi.org/10.3390/su8050466
  5. Ransomware detection method based on context-aware entropy analysis vol.22, pp.20, 2018, https://doi.org/10.1007/s00500-018-3257-z