Sampling from a moving window over streaming data
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Maintaining variance and k-medians over data stream windows
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Detection of complex temporal patterns over data streams
Information Systems - Special issue: ADBIS 2002: Advances in databases and information systems
Recovering latent time-series from their observed sums: network tomography with particle filters.
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Atomic Wedgie: Efficient Query Filtering for Streaming Times Series
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining sequential patterns from data streams: a centroid approach
Journal of Intelligent Information Systems
Warping the time on data streams
Data & Knowledge Engineering
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Toward accurate dynamic time warping in linear time and space
Intelligent Data Analysis
Proceedings of the VLDB Endowment
iSAX 2.0: Indexing and Mining One Billion Time Series
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Similarity matching for uncertain time series: analytical and experimental comparison
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data
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Nowadays online monitoring of data streams is essential in many real life applications, like sensor network monitoring, manufacturing process control, and video surveillance. One major problem in this area is the online identification of streaming sequences similar to a predefined set of pattern-sequences. In this paper, we present a novel solution that extends the state of the art both in terms of effectiveness and efficiency. We propose the first online similarity matching algorithm based on Longest Common SubSequence that is specifically designed to operate in a streaming context, and that can effectively handle time scaling, as well as noisy data. In order to deal with high stream rates and multiple streams, we extend the algorithm to operate on multilevel approximations of the streaming data, therefore quickly pruning the search space. Finally, we incorporate in our approach error estimation mechanisms in order to reduce the number of false negatives. We perform an extensive experimental evaluation using forty real datasets, diverse in nature and characteristics, and we also compare our approach to previous techniques. The experiments demonstrate the validity of our approach.