| Title | Estimating \iSAX Parameters for Efficiency |
| Publication Type | Conference Paper |
| Year of Publication | 2023 |
| Authors | Tsoukalos M, Platis N, Vassilakis C |
| Conference Name | New Trends in Database and Information Systems (Proceedings of the 2023 European Conference on Advances in Databases and Information Systems / ADBIS 2023) |
| Publisher | Springer Nature Switzerland |
| Conference Location | Cham |
| ISBN Number | 978-3-031-42941-5 |
| Keywords | Indexing, Parameter tuning, SAX, Time series |
| Abstract | \iSAX is considered one of the most efficient indexes for time series. Several parameters affect the construction of an \iSAX index: the sliding window size, the threshold value, the number of segments and the maximum cardinality, the last two being related to the SAX representation. In this paper (i) we consider the effect of each parameter on the efficiency of the \iSAX index, (ii) we evaluate the importance of each parameter, and, (iii) suggest how to optimize these parameters. |
| DOI | 10.1007/978-3-031-42941-5_1 |
