Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The String-to-String Correction Problem
Journal of the ACM (JACM)
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 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
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
ABC-SG: a new artificial bee colony algorithm-based distance of sequential data using sigma grams
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Similarity search is a fundamental problem in information technology. The main difficulty of this problem is the high dimensionality of the data objects. In large time series databases, it's important to reduce the dimensionality of these data objects, so that we can manage them. Symbolic representation is a promising technique of dimensionality reduction. In this paper we propose a new distance metric, which is applied to symbolic sequential data objects, and we test it on time series databases in classification task experiments. We also compare it to other distances that are well known in the literature for symbolic data objects, and we prove that it's metric.