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A Sensor Data Management System for USN based Fire Detection Application

USN 기반의 화재감시 응용을 위한 센서 데이터 처리 시스템

  • Park, Won-Ik (Dept. of Computer Engineering, Chungnam National University) ;
  • Kim, Young-Kuk (Dept. of Computer Engineering, Chungnam National University)
  • Received : 2010.12.28
  • Accepted : 2011.02.07
  • Published : 2011.05.31

Abstract

These days, the research of a sensor data management system for USN based real-time monitoring application is active thanks to the development and diffusion of sensor technology. The sensor data is rapidly changeable, continuous and massive row level data. However, end user is only interested in high level data. So, it is essential to effectively process the row level data which is changeable, continuous and massive. In this paper, we propose a sensor data management system with multi-analytical query function using OLAP and anomaly detection function using learning based classifier. In the experimental section, we show that our system is valid through the some experimental scenarios. For the this, we use a sensor data generator implemented by ourselves.

오늘날 센서 기술의 발전 및 보급으로 인해 USN 기반의 실시간 모니터링 응용에서의 센서 데이터 처리 시스템에 대한 연구가 활발히 진행 되고 있다. 센서 데이터는 시간에 따라 빠르게 변화하고 연속적인 저수준 상태의 방대한 양의 데이터를 생성하는 특성을 갖는다. 하지만 엔드유저는 상대적으로 고수준 상태의 데이터에 관심이 있기 때문에 빠르게 변화하고 연속적인 대량의 저수준 센서 데이터를 효과적으로 처리하는 시스템이 필수적이다. 본 논문에서는 USN 기반의 화재감시 응용에서 OLAP(On-Line Analytical Processing) 기술을 이용한 다차원 분석 질의 처리 기능과 학습기반 분류기를 통한 이상치 탐지 기능을 제공하는 센서 데이터 처리 시스템을 제안한다. 실험 시나리오를 통해 우리의 센서 데이터 처리 시스템에 대한 타당성을 검증하며 실험에 필요한 다양한 센서 데이터는 자체 개발한 센서 데이터 생성기를 이용한다.

Keywords

References

  1. Jiawei, H., et al., "Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams," Distrib. Parallel Databases, 18(2), pp. 173-197, 2005. https://doi.org/10.1007/s10619-005-3296-1
  2. P. Domingos and M. Pazzani, "Beyond Independence: Conditions for the Optimality of The Simple Bayesian Classifier," In Proc. Int. Conf. Machine Learning, pp. 105-112, 1996.
  3. A. Arasu, et al., "Stream: The Stanford Data Stream Management System," in IEEE Data Engineering Bulletin, 4(1), 2003.
  4. C. Don, U, et al.,"Monitoring Streams: A New Class of Data Management Applications," In Proc. Int. Conf. Very Large DataBase, pp.215-226, 2002.
  5. C. Sirish, et al., "TelegraphCQ: Continuous Dataflow Processing," In Proc. Int. Conf. Innovative Data Systems Research, pp.11-18, 2003.
  6. Cougar Project, http://www.cs.cornell.edu/boom/2003sp/ProjectArch/CougarSM/index.php
  7. C. Jianjun, et al.,"NiagaraCQ: A Scalable Continuous Query System for Internet Databases," SIGMOD Rec., pp. 379-390, 2000.
  8. Calton Pu, Ling Liu, "Update Monitoring: The CQ Project," In Proc. Int. Conf. Worldwide Computing and Its Applications, Tuskuba, Japan, Lecture Notes in Computer Science, pp.396-411, 1998.
  9. R. Philipp, sch, and L. Wolfgang, "Sample synopses for approximate answering of group-by queries," In Proc. Int. Conf. Extending Database Technology, ACM, pp.403-414, 2009.
  10. K. Henning, hler, Z. Xiaofang, S. Shazia, S. Yanfeng, and T. Kerry, "Sampling dirty data for matching attributes," In Proc. Int. Conf. Management of data, Indianapolis, Indiana, USA, ACM, pp.63-74, 2010.
  11. Alfredo Cuzzocrea , Sharma Chakravarthy, "Event-based Lossy Compression for Effective and Efficient OLAP over Data Streams," Data & Knowledge Engineering, v.69 n.7, p.678-708, July, 2010. https://doi.org/10.1016/j.datak.2010.02.006
  12. Yufei Tao , Dimitris Papadias, "Maintaining Sliding Window Skylines on Data Streams," IEEE Trans. on Knowledge and Data Engineering, 18(3), pp.377-391, March, 2006. https://doi.org/10.1109/TKDE.2006.48
  13. Abhirup Chakraborty , Ajit Singh, "A Disk-based, Adaptive Approach to Memory-limited Computation of Windowed Stream Joins," In Proc. Int. Database and expert systems applications: Part I, Aug. 30-Sep. 03, Bilbao, Spain, 2010.
  14. Hua-Fu Li , Suh-Yin Lee, "Mining Frequent Itemsets over Data Streams using Efficient Window Sliding Techniques," Expert Systems with Applications: An Int. Journal, 36(2), pp.1466-1477, March, 2009. https://doi.org/10.1016/j.eswa.2007.11.061
  15. W. Hai, and C.S. Kenneth, "Histograms based on the Minimum Description Length Principle," The VLDB Journal, pp. 419-442, 2008.
  16. L. Xin, and G. Jihong, "A New Approach to Building Histogram for Selectivity Estimation in Query Processing Optimization," Comput. Math. Appl., pp. 1037-1047, 2009.
  17. H. Ming-Jyh, C. Ming-Syan, and S.Y. Philip, "Integrating DCT and DWT for Approximating Cube Streams," In Proc. Int. Conf. Information and knowledge management, pp.179-189, 2005.
  18. Xiao-Bo Fan , Ting-Ting Xie , Cui-Ping Li , Hong Chen, "MRST: An Efficient Monitoring Technology of Summarization on Stream Data," Journal of Computer Science and Technology, 22(2), pp.190-196, March, 2007. https://doi.org/10.1007/s11390-007-9025-7
  19. Hanady Abdulsalam , David B. Skillicorn , Patrick Martin, "Classifying Evolving Data Streams Using Dynamic Streaming Random Forests," In Proc. Int. Conf. Database and Expert Systems Applications, September 01-05, Turin, Italy, 2008.
  20. Wei Qu , Yang Zhang , Junping Zhu , Qiang Qiu, "Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble," In Proc. Int. Conf. Machine Learning: Advances in Machine Learning, November 02-04, Nanjing, China,2009.
  21. Qu Wei , Zhang Yang , Zhu Junping , Wang Yong, "Mining Multi-label Concept-drifting Data Streams Using Ensemble Classifiers," In Proc. Int. Conf. Fuzzy systems and knowledge discovery, Tianjin, China, August 14-16, 2009.
  22. Mohammad M. Masud , et al., "Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space," In Proc. Int. Conf. Machine learning and knowledge discovery in databases: Part II, Barcelona, Spain, September 20-24, 2010.
  23. Panagiotis Antonellis , Christos Makris , Nikos Tsirakis, "Algorithms for Clustering Clickstream Data," Information Processing Letters, 109(8), pp.381-385, March, 2009. https://doi.org/10.1016/j.ipl.2008.12.011
  24. Li Wan , et al., "Density-based Clustering of Data Streams at Multiple Resolutions," ACM Trans. on Knowledge Discovery from Data), 3(3), pp.1-28, July 2009.
  25. Li Tu , Yixin Chen, "Stream Data Clustering based on Grid Density and Attraction," ACM Trans. on Knowledge Discovery from Data, 3(3), pp.1-27, July 2009.
  26. Maria Kontaki , Apostolos N. Papadopoulos , Yannis Manolopoulos, "Continuous Trend-Based Clustering in Data Streams," In Proc. Int. Conf. Data Warehousing and Knowledge Discovery, Turin, Italy, September 02-05, 2008.
  27. Bai-En Shie , Vincent S. Tseng , Philip S. Yu, "Online Mining of Temporal Maximal Utility Itemsets from Data Streams," In Proc. Int. Conf. Applied Computing, Sierre, Switzerland, March 22-26, 2010.
  28. T. Syed Khairuzzaman, A. Chowdhury Farhan, J. Byeong-Soo, and L. Young-Koo, "Efficient Frequent Pattern Mining over Data Streams," In Proc. Int. Conf. Information and knowledge management, pp.1447-1448, California, USA, October 26-30, 2008.
  29. Yoshiaki Yasumura, Naho Kitani, Kuniaki Uehara, "Quick Adaptation to Changing Concepts by Sensitive Detection," In Proc. Int. Conf. Industrial, engineering, and other applications of applied intelligent systems, Kyoto, Japan, June 26-29, 2007.
  30. Alfredo Cuzzocrea , Sharma Chakravarthy, "Event-based Lossy Compression for Effective and Efficient OLAP over Data Streams," Data & Knowledge Engineering, 69(7), pp.678-708, July, 2010. https://doi.org/10.1016/j.datak.2010.02.006
  31. Alfredo Cuzzocrea, "CAMS: OLAPing Multidimensional Data Streams Efficiently," In Proc. Int. Conf. Data Warehousing and Knowledge Discovery, Linz, Austria, August 31-September 02, 2009.
  32. Sebastien Nedjar , Alain Casali , Rosine Cicchetti , Lotfi Lakhal, "Emerging Cubes: Borders, Size Estimations and Lossless Reductions," Information Systems, 34(6), pp.536-550, September, 2009. https://doi.org/10.1016/j.is.2009.03.001

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