COMPONENT-BASED FRAMEWORK FOR MOBILE DATA MINING WITH SUPPORT FOR REAL-TIME SENSORS

Taneli Rautio, Perttu Laurinen, Juha Röning

Abstract

The increasing use of various mobile devices has shown that there is a need for mobile data mining applications. While many existing data mining frameworks can be modified to handle data streams generated in real time, they are usually too complex and inflexible to be used in mobile devices. This paper presents Mobile Smart Archive, a component-based framework for data stream mining in mobile devices. The framework takes care of generic data mining operations, allowing the application developer to concentrate on implementing only application-specific functionalities. This reduces implementation time and generates fewer errors, since the underlying framework of the application is tested and robust. The presented framework is written in C++ and it extends the existing Smart Archive framework with support for mobile systems and real-time sensors. The benefits of framework-based applications in the mobile world are presented by building and testing a demonstration program in different computer architectures. In this paper we show that the MSA framework is suitable for building data stream mining applications for the hardware-oriented mobile environment.

References

  1. Berthold, M. R., Cebron, N., Dill, F., Fatta, G. D., Gabriel, T. R., Georg, F., Meinl, T., Ohl, P., Sieb, C., and Wiswedel, B. (2006). Knime: The Konstanz Information Miner. In Proceedings of the 4th Annual Industrial Simulation Conference, pages 58-61.
  2. Hsu, J. (2002). Data Mining Trends and Developments: The Key Data Mining Technologies and Applications for the 21st Century. In The Proceedings of 19th Annual Information Systems Education Conference (ISECON 2002).
  3. Kargupta, H., Bhargava, R., Kun, L., Powers, M., Blair, P., Bushra, S., and Dull, J. (2004). VEDAS: A Mobile and Distributed Data Stream Mining System for RealTime Vehicle Monitoring. In Proceedings of the fourth SIAM international conference on data mining, pages 300-311.
  4. Kargupta, H., Park, B.-H., Pittie, S., Liu, L., Kushraj, D., and Sarkar, K. (2002). MobiMine: monitoring the stock market from a PDA. ACM SIGKDD Explorations Newsletter, 3(2):37-46.
  5. Laurinen, P., Tuovinen, L., and Röning, J. (2005). Smart Archive: A Component-based Data Mining Application Framework. In Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, pages 20-26, Wroclaw, Poland. IEEE Computer Society Press.
  6. Mierswa, I., Wurst, M., Klingenberg, R., Scholz, M., and Euler, T. (2006). YALE: Rapid Prototyping for Complex Data Mining Tasks. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 935-940.
  7. NCSA Automated Learning Group (2003). D2K Toolkit User Manual.
  8. SAMH Engineering Services (2007). SHAKE Sensing Hardware Accessory for Kinaestethic Expression Model SK6. Dublin, Ireland, revision f edition.
  9. Smirnov, I. B. (2007). Raw Pointers in Application Classes of C++ Considered Harmful. ACM SIGPLAN Notices, 42(4):23-31.
  10. Thuraisingham, B., Khan, L., Clifton, C., Maurer, J., and Ceruti, M. (2005). Dependable Real-time Data Mining. In Proceedings of the Eight International Symposium on Object-Oriented Real-Time Distributed Computing, pages 158-165.
  11. Tuovinen, L., Laurinen, P., Juutilainen, I., and Röning, J. (2008). Data Mining Applications for Diverse Industrial Application Domains with Smart Archive. In Proceedings of the IASTED International Conference on Software Engineering, pages 56-61.
  12. Wang, F., Helian, N., Guo, Y., and Jin, H. (2003). A Distributed and Mobile Data Mining System. In Proceedings of the Fourth International Conference on Parallel and Distributed Computing, Applications and Technologies, pages 916- 918.
Download


Paper Citation


in Harvard Style

Rautio T., Laurinen P. and Röning J. (2009). COMPONENT-BASED FRAMEWORK FOR MOBILE DATA MINING WITH SUPPORT FOR REAL-TIME SENSORS . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8111-66-1, pages 208-213. DOI: 10.5220/0001657702080213


in Bibtex Style

@conference{icaart09,
author={Taneli Rautio and Perttu Laurinen and Juha Röning},
title={COMPONENT-BASED FRAMEWORK FOR MOBILE DATA MINING WITH SUPPORT FOR REAL-TIME SENSORS},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2009},
pages={208-213},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001657702080213},
isbn={978-989-8111-66-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - COMPONENT-BASED FRAMEWORK FOR MOBILE DATA MINING WITH SUPPORT FOR REAL-TIME SENSORS
SN - 978-989-8111-66-1
AU - Rautio T.
AU - Laurinen P.
AU - Röning J.
PY - 2009
SP - 208
EP - 213
DO - 10.5220/0001657702080213