Journal of Algorithms
Estimating Rarity and Similarity over Data Stream Windows
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A Unified Approach to Detecting Spatial Outliers
Geoinformatica
A Unified Framework for Monitoring Data Streams in Real Time
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Flow Anomalies: A SWEET Approach
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Mining correlations between multi-streams based on Haar wavelet
ASIAN'05 Proceedings of the 10th Asian Computing Science conference on Advances in computer science: data management on the web
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Tipping points, butterflies, and black swans: a vision for spatio-temporal data mining analysis
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
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Given a collection of sensors monitoring a flow network, the problem of discovering teleconnected flow anomalies aims to identify strongly connected pairs of events (e.g., introduction of a contaminant and its removal from a river). The ability to mine teleconnected flow anomalies is important for applications related to environmental science, video surveillance, and transportation systems. However, this problem is computationally hard because of the large number of time instants of measurement, sensors, and locations. This paper characterizes the computational structure in terms of three critical tasks, (1) detection of flow anomaly events, (2) identification of candidate pairs of events, and (3) evaluation of candidate pairs for possible teleconnection. The first task was addressed in our recent work. In this paper, we propose a RAD (Relationship Analysis of spatio-temporal Dynamic neighborhoods) approach for steps 2 and 3 to discover teleconnected flow anomalies. Computational overhead is brought down significantly by utilizing our proposed spatio-temporal dynamic neighborhood model as an index and a pruning strategy. We prove correctness and completeness for the proposed approaches. We also experimentally show the efficacy of our proposed methods using both synthetic and real datasets.