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Ma, Jiangang; Sun, Le; Wang, Hua; Zhang, Yanchun; Aickelin, Uwe (2016)
Publisher: Association for Computing Machinery
Languages: English
Types: Article
Subjects: Computer Science - Artificial Intelligence
Identifiers:doi:10.1145/2806890
Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this paper, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts an efficient uncertainty pre-processing procedure to identify and eliminate uncertainties in data streams. Based on the corrected data streams, we develop effective period pattern recognition and feature extraction techniques to improve the computational efficiency. We use classification methods for anomaly detection in the corrected data stream. We also empirically show that the proposed approach shows a high accuracy of anomaly detection on a number of real datasets.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Charu C. Aggarwal. 2009. On high dimensional projected clustering of uncertain data streams. In IEEE 25th International Conference on Data Engineering (ICDE'09). IEEE, Shanghai, China, 1152-1154. DOI:http://dx.doi.org/10.1109/ICDE.2009.188
    • Charu C. Aggarwal and Philip S. Yu. 2008. A framework for clustering uncertain data streams. In IEEE 24th International Conference on Data Engineering (ICDE'08). IEEE, Cancun, Mexico, 150-159. DOI:http://dx.doi.org/10.1109/ICDE.2008.4497423
    • Ian F. Akyildiz, Dario Pompili, and Tommaso Melodia. 2005. Underwater acoustic sensor networks: research challenges. Ad Hoc Netw. 3, 3 (2005), 257-279. DOI:http://dx.doi.org/10.1016/j.adhoc.2005.01.004
    • Lv an Tang, Bin Cui, Hongyan Li, Gaoshan Miao, Dongqing Yang, and Xinbiao Zhou. 2007. Effective variation management for pseudo periodical streams. In Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data (SIGMOD'07). ACM, New York, NY, USA, 257-268. DOI:http://dx.doi.org/10.1145/1247480.1247511
    • Arvind Arasu, Shivnath Babu, and Jennifer Widom. 2003. The CQL continuous query language: semantic foundations and query execution. Technical Report 2003-67. Stanford InfoLab. http://ilpubs.stanford. edu:8090/758/
    • David Arthur and Sergei Vassilvitskii. 2007. K-means++: the advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA'07). Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 1027-1035. http://dl.acm.org/citation.cfm? id=1283383.1283494
    • Johannes A falg, Hans-Peter Kriegel, Peer Kro¨ger, and Matthias Renz. 2009. Probabilistic similarity search for uncertain time series. In Scientific and Statistical Database Management, Marianne Winslett (Ed.). Lecture Notes in Computer Science, Vol. 5566. Springer Berlin Heidelberg, New Orleans, LA, USA, 435-443. DOI:http://dx.doi.org/10.1007/978-3-642-02279-1 31
    • Jianjun Chen, David J. DeWitt, Feng Tian, and Yuan Wang. 2000. NiagaraCQ: a scalable continuous query system for internet databases. In Proceedings of ACM SIGMOD International Conference on Management of Data (SIGMOD'00). 379-390. http://doi.acm.org/10.1145/342009.335432
    • CSIRO. 2011. Sensors and Sensor Networks 2010-2011 Year in Review. (2011). http://research.ict.csiro.au/ news/sensors-and-sensor-networks-2010-2011-year-in-review
    • Michele Dallachiesa, Besmira Nushi, Katsiaryna Mirylenka, and Themis Palpanas. 2012. Uncertain time-series similarity: return to the basics. Proc. VLDB Endow. 5, 11 (July 2012), 1662-1673. DOI:http://dx.doi.org/10.14778/2350229.2350278
    • David H. Douglas and Thomas K. Peucker. 1973. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica 10, 2 (1973), 112-122.
    • Philippe Esling and Carlos Agon. 2012. Time-series data mining. ACM Comput. Surv. 45, 1, Article 12 (December 2012), 12:1-12:34 pages. DOI:http://dx.doi.org/10.1145/2379776.2379788
    • Victor A. Folarin, Patrick J. Fitzsimmons, and William B. Kruyer. 2001. Holter monitor findings in asymptomatic male military aviators without structural heart disease. Aviat. Space. Envir. MD 72, 9 (2001), 836-838. http://www.ncbi.nlm.nih.gov/pubmed/11565820
    • Ary L. Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung-Kang Peng, and H. Eugene Stanley. 2000a. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101, 23 (2000), e215-e220.
    • Ary L. Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung-Kang Peng, and H. Eugene Stanley. 2000b. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101, 23 (2000). DOI:http://dx.doi.org/10.1161/01.CIR.101.23.e215
    • Aslak Grinsted, John C. Moore, and Svetlana Jevrejeva. 2004. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Proc. Geoph. 11, 5/6 (2004), 561-566. DOI:http://dx.doi.org/10.5194/npg-11-561-2004
    • Yu Gu, Andrew McCallum, and Don Towsley. 2005. Detecting anomalies in network traffic using maximum entropy estimation. In Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement (IMC'05). USENIX Association, Berkeley, CA, USA, 32-32. http://dl.acm.org/citation.cfm?id=1251086. 1251118
    • Joachim Gudmundsson, Marc van Kreveld, and Bettina Speckmann. 2007. Efficient detection of patterns in 2D trajectories of moving points. GeoInformatica 11, 2 (2007), 195-215. DOI:http://dx.doi.org/10.1007/s10707-006-0002-z
    • S¸ule Gu¨ ndu¨ z and M Tamer O¨zsu. 2003. A web page prediction model based on click-stream tree representation of user behavior. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 535-540.
    • John A. Hartigan and Manchek A. Wong. 1979. Algorithm AS 136: a K-means clustering algorithm. J. Roy. Stat. Soc. C.-APP 28, 1 (1979), 100-108. http://www.jstor.org/stable/2346830
    • Jing He, Yanchun Zhang, and Guangyan Huang. 2012. Exceptional object analysis for finding rare environmental events from water quality datasets. Neurocomputing 92, 0 (2012), 69-77. DOI:http://dx.doi.org/10.1016/j.neucom.2011.08.036 Data Mining Applications and Case Study.
    • Jing He, Yanchun Zhang, Guangyan Huang, and Paulo de Souza. 2013. CIRCE: correcting imprecise readings and compressing excrescent points for querying common patterns in uncertain sensor streams. Inform. Syst. 38, 8 (2013), 1234-1251. DOI:http://dx.doi.org/10.1016/j.is.2012.01.003
    • John Hershberger and Jack Snoeyink. 1994. An O(Nlogn) implementation of the Douglas-Peucker algorithm for line simplification. In Proceedings of the 10th Annual Symposium on Computational Geometry (SCG'94). ACM, New York, NY, USA, 383-384. DOI:http://dx.doi.org/10.1145/177424.178097
    • Guangyan Huang, Yanchun Zhang, Jie Cao, Michael Steyn, and Kersi Taraporewalla. 2014. Online mining abnormal period patterns from multiple medical sensor data streams. World Wide Web 17, 4 (2014), 569-587. DOI:http://dx.doi.org/10.1007/s11280-013-0203-y
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