Vector quantization and signal compression
Vector quantization and signal compression
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A dimensionality reduction technique for efficient similarity analysis of time series databases
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Exact indexing of dynamic time warping
Knowledge and Information Systems
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Approximate embedding-based subsequence matching of time series
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Proceedings of the VLDB Endowment
Time series analysis with multiple resolutions
Information Systems
Multi-resolution approach to time series retrieval
Proceedings of the Fourteenth International Database Engineering & Applications Symposium
A survey of query-by-humming similarity methods
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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Time series constitute a prevalent data type that arise in several diverse disciplines (e.g., biomedical data, sensor data, images, video data), and therefore analyzing time series is a significant task with a plethora of important applications. In this paper, we study the general problem of similarity search in time series databases and we propose a novel multiresolution indexing (i.e., representation) and retrieval method for time series similarity search. Our approach is motivated by the idea that if we examine a time series at different resolution levels, we could possibly acquire further insights about the data. The proposed algorithm adopts a combined, two-step pruning (filtering) strategy to further reduce data dimensionality by discarding irrelevant time series (i.e., false alarms). At a first level, the time series are represented by line segments and filtered by the triangular inequality property. Then, a Vector Quantization like scheme is applied to encode data and thus to reduce dimensionality. We test and demonstrate the performance of the proposed method, analyzing EEG time series data for retrieval of one of the constituent brain waveforms in EEG recordings, the K-complex, but the method can as well be applied for retrieval of other patterns of interest in time series analysis. The automatic detection and categorization of the EEG patterns will allow the advanced correlation analysis of large amounts of data and will lead to advanced decision making capabilities assisting diagnosis by medical professionals.