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Overload management has become very important in public safety systems that analyse high performance multimedia data streams, especially in the case of detection of terrorist and criminal dangers. Efficient overload management improves the accuracy of automatic identification of persons suspected of terrorist or criminal activity without requiring interaction with them. We argue that in order to improve the quality of multimedia data stream processing in the public safety arena, the innovative concept of a Multimedia Data Stream Management System (MMDSMS) using load-shedding techniques should be introduced into the infrastructure to monitor and optimize the execution of multimedia data stream queries. In this paper, we present a novel content-centered load shedding framework, based on searching and matching algorithms, for analysing video tuples arriving within multimedia data streams. The framework tracks and registers all symptoms of overload, and either prevents overload before it occurs, or minimizes its effects. We have extended our Continuous Query Language (CQL) syntax to enable this load shedding technique. The effectiveness of the framework has been verified using both artificial and real data video streams collected from monitoring devices.
Rocznik
Tom
Strony
79--95
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
autor
- Poznan University of Technology, Institute of Computing Science, ul. Piotrowo 2, 60- 965 Poznan, Poland
autor
- Poznan University of Technology, Institute of Computing Science, ul. Piotrowo 2, 60- 965 Poznan, Poland
Bibliografia
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- [11] S. Chandrasekaran, O. Cooper, A. Deshpande, M. J. Franklin, J. M. Hellerstein, W. Hong, S. Krishnamurthy, S. Madden, V. Raman, F. Reiss, and M. Shah, “TelegraphCQ: Continuous Dataflow Processing for an Uncertain World,” Proc. 1st Biennial Conf. on Innovative Data Syst. Res. (CIDR ’03) (Asilomar, CA, 2003).
- [12] S. Chang, L. Zhao, S. Guirguis and R. Kulkarni, “A Computation-Oriented Multimedia Data Streams Model for Content-Based Information Retrieval.” In Multimedia Tools and Applications, Volume 46, Nos. 2-3, pages 399-423, Jan 2010.
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- [20] R. Maison, D.J. Steen, M. Zakrzewicz and Z. Biniek, “Monitoring High Performance Data Streams in Vertical Markets: Theory and Applications in Public Safety and Healthcare,” Bell Labs Technical Journal, 16(3) (2011), 163-180.
- [21] R. Maison and M. Zakrzewicz, “Prediction-Based Load Shedding for Burst Data Streams,” Bell Labs Technical Journal, 16(1) (2011), 121-132.
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- [26] F. Reiss and J. M. Hellerstein, “Declarative Network Monitoring with an Underprovisioned Query Processor,” Proc. 22nd Internat. Conf. on Data Eng. (ICDE ’06) (Atlanta, GA, 2006), p. 56.
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- [28] N. Tatbul, U. Çetintemel, S. Zdonik, M. Cherniack, and M. Stonebraker, “Load Shedding in a Data Stream Manager,” Proc. 29th Internat. Conf. on Very Large Data Bases (VLDB ’03) (Berlin, Ger., 2003), pp. 309-320.
- [29] N. Tatbul and S. Zdonik, “Window-Aware Load Shedding for Aggregation Queries over Data Streams,” Proc. 32nd Internat. Conf. on Very Large Data Bases (VLDB ’06) (Seoul, Kor., 2006), pp. 799-810.
- [30] Y.-C. Tu, S. Liu, S. Prabhakar, and B. Yao, “Load Shedding in Stream Databases: A Control-Based Approach,” Proc. 32nd Internat. Conf. on Very Large Data Bases (VLDB ’06) (Seoul, Kor., 2006), pp. 787-798.
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- [33] J. S. Vitter and M. Wang, “Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets,” Proc. ACM SIGMOD Internat. Conf. on Management of Data (SIGMOD ’99) (Philadelphia, PA, 1999), pp. 193-204.
- [34] L. Wiskott, J.-M. Fellous, N. Krüger, and C. von der Malsburg, “Face Recognition by Elastic Bunch Graph Matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, 19:7 (1997), 775-779.
- [35] Qing Xu; Pengcheng Wang; Bin Long; Sbert, M.; Feixas, M.; Scopigno, R.; , "Selection and 3D visualization of video key frames," Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on , vol., no., pp.52-59, 10-13 Oct. 2010 doi: 10.1109/ICSMC.2010.5642204.
- [36] W. Zhao, A. Krishnaswamy, R. Chellappa, D. L. Swets, and J. Weng, “Discriminant Analysis of Principal Components for Face Recognition,” Face Recognition: From Theory to Applications (H. Wechsler, P. J. Phillips, V. Bruce, F. Fogelman Soulié, and T. S. Huang, eds.), Springer-Verlag, Berlin, Heidelberg, New York, 1998, pp. 73-85.
- [37] Y. Zhuang, Y. Rui, T.S. Huang and S. Mehrotra, "Adaptive key frame extraction using unsupervised clustering," Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on , vol.1, no., pp.866-870 vol.1, 4-7 Oct 1998.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0b4f3cb3-66a8-4df1-982d-bddffdd2f800