The Community for Technology Leaders
RSS Icon
Issue No.02 - Feb. (2013 vol.25)
pp: 360-373
Massimiliano Albanese , George Mason University, Fairfax
Andrea Pugliese , University of Calabria, Rende
V.S. Subrahmanian , University of Maryland, College Park
Today, numerous applications require the ability to monitor a continuous stream of fine-grained data for the occurrence of certain high-level activities. A number of computerized systems—including ATM networks, web servers, and intrusion detection systems—systematically track every atomic action we perform, thus generating massive streams of timestamped observation data, possibly from multiple concurrent activities. In this paper, we address the problem of efficiently detecting occurrences of high-level activities from such interleaved data streams. A solution to this important problem would greatly benefit a broad range of applications, including fraud detection, video surveillance, and cyber security. There has been extensive work in the last few years on modeling activities using probabilistic models. In this paper, we propose a temporal probabilistic graph so that the elapsed time between observations also plays a role in defining whether a sequence of observations constitutes an activity. We first propose a data structure called “temporal multiactivity graph” to store multiple activities that need to be concurrently monitored. We then define an index called Temporal Multiactivity Graph Index Creation (tMAGIC) that, based on this data structure, examines and links observations as they occur. We define algorithms for insertion and bulk insertion into the tMAGIC index and show that this can be efficiently accomplished. We also define algorithms to solve two problems: the “evidence” problem that tries to find all occurrences of an activity (with probability over a threshold) within a given sequence of observations, and the “identification” problem that tries to find the activity that best matches a sequence of observations. We introduce complexity reducing restrictions and pruning strategies to make the problem—which is intrinsically exponential—linear to the number of observations. Our experiments confirm that tMAGIC has time and space complexity linear to the size of the input, and can efficiently retrieve instances of the monitored activities.
Stochastic processes, Hidden Markov models, Context, Indexing, Monitoring, Automata, timestamped data, Activity detection, indexing, stochastic automata
Massimiliano Albanese, Andrea Pugliese, V.S. Subrahmanian, "Fast Activity Detection: Indexing for Temporal Stochastic Automaton-Based Activity Models", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 2, pp. 360-373, Feb. 2013, doi:10.1109/TKDE.2011.246
[1] M. Albanese, R. Chellappa, V. Moscato, A. Picariello, V.S. Subrahmanian, P. Turaga, and O. Udrea, "A Constrained Probabilistic Petri Net Framework for Human Activity Detection in Video," IEEE Trans. Multimedia, vol. 10, no. 8, pp. 1429-1443, Dec. 2008.
[2] M. Albanese, V. Moscato, A. Picariello, V.S. Subrahmanian, and O. Udrea, "Detecting Stochastically Scheduled Activities in Video," Proc. 20th Int'l Joint Conf. Artifical Intelligence (IJCAI '07), pp. 1802-1807, Jan. 2007.
[3] M. Albanese, A. Pugliese, V.S. Subrahmanian, and O. Udrea, "MAGIC: A multiactivity Graph Index for Activity Detection," Proc. IEEE Int'l Conf. Information Reuse and Integration (IRI '07), pp. 267-278, Aug. 2007.
[4] A. Arasu, S. Babu, and J. Widom, "The CQL Continuous Query Language: Semantic Foundations and Query Execution," Int'l J. Very Large Data Bases, vol. 15, pp. 121-142, June 2006.
[5] J. Ben-Arie, Z. Wang, P. Pandit, and S. Rajaram, "Human Activity Recognition Using Multidimensional Indexing," IEEE Trans. Pattern Analysis Machine Intelligence, vol. 24, no. 8, pp. 1091-1104, Aug. 2002.
[6] S. Chandrasekaran, O. Cooper, A. Deshpande, M.J. Franklin, J.M. Hellerstein, W. Hong, S. Krishnamurthy, S. Madden, V. Raman, F. Reiss, and M.A. Shah, "TelegraphCQ: Continuous Dataflow Processing for an Uncertain World," Proc. Conf. Innovative Data Systems Research (CIDR '03), 2003.
[7] T.V. Duong, H.H. Bui, D.Q. Phung, and S. Venkatesh, "Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR '05), 2005.
[8] L. Golab and M.T. Özsu, "Issues in Data Stream Management," ACM SIGMOD Record, vol. 32, pp. 5-14, June 2003.
[9] R. Hamid, Y. Huang, and I. Essa, "ARGMode Activity Recognition Using Graphical Models." Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR '03), 2003.
[10] B. Kerkez, "Learning Plan Libraries for Case-Based Plan Recognition," Proc. Midwest Artificial Intelligence and Cognitive Science Conf. (MAICS '02), Apr. 2002.
[11] S. Lühr, H.H. Bui, S. Venkatesh, and G.A.W. West, "Recognition of Human Activity through Hierarchical Stochastic Learning," Proc. IEEE First Int'l Conf. Pervasive Computing and Comm. (PerCom '03), pp. 416-422, Mar. 2003.
[12] F. Mörchen, "Unsupervised Pattern Mining from Symbolic Temporal Data," SIGKDD Explorations Newsletter, vol. 9, no. 1, pp. 41-55, June 2007.
[13] F. Reiss, K. Stockinger, K. Wu, A. Shoshani, and J.M. Hellerstein, "Enabling Real-Time Querying of Live and Historical Stream Data," Proc. 19th Int'l Conf. Scientific and Statistical Database Management (SSDBM '07), 2007.
[14] K. Seymore, A. McCallum, and R. Rosenfeld, "Learning Hidden Markov Model Structure for Information Extraction," Proc. Workshop Machine Learning for Information Extraction (AAAI '99), 1999.
[15] V.T. Vu, F. Brémond, and M. Thonnat, "Automatic Video Interpretation: A Novel Algorithm for Temporal Scenario Recognition," Proc. 18th Int'l Joint Conf. Artificial Intelligence (IJCAI '03), pp. 1295-1302, Aug. 2003.
[16] S. Wasserkrug, A. Gal, O. Etzion, and Y. Turchin, "Complex Event Processing over Uncertain Data," Proc. Second Int'l Conf. Distributed Event-Based Systems (DEBS '08), pp. 253-264, 2008.
30 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool