Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances
The VLDB Journal — The International Journal on Very Large Data Bases
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Indexing multi-dimensional time-series with support for multiple distance measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying similarities, periodicities and bursts for online search queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A review on time series data mining
Engineering Applications of Artificial Intelligence
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In many classification and data-mining applications the user does not know a priori which distance measure is the most appropriate for the task at hand without examining the produced results. Also, in several cases, different distance functions can provide diverse but equally intuitive results (according to the specific focus of each measure). In order to address the above issues, we elaborate on the construction of a hybrid index structure that supports query-by-example on shape and structural distance measures, therefore lending enhanced exploratory power to the system user. The shape distance measure that the index supports is the ubiquitous Euclidean distance, while the structural distance measure that we utilize is based on important periodic features extracted from a sequence. This new measure is phase-invariant and can provide flexible sequence characterizations, loosely resembling the Dynamic Time Warping, requiring only a fraction of the computational cost of the latter. Exploiting the relationship between the Euclidean and periodic measure, the new hybrid index allows for powerful query processing, enabling the efficient answering of kNN queries on both measures in a single index scan. We envision that our system can provide a basis for fast tracking of correlated time-delayed events, with applications in data visualization, financial market analysis, machine monitoring/diagnostics and gene expression data analysis.