Evolution of a Data Series Index

The iSAX Family of Data Series Indexes: iSAX, iSAX2.0, iSAX2+, ADS, ADS+, ADS-Full, ParIS, ParIS+, MESSI, DPiSAX, ULISSE, Coconut-Trie/Tree, Coconut-LSM
  • Themis PalpanasEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1197)


There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of sequences, or data series. It is not unusual for these applications to involve numbers of data series in the order of billions, which are often times not analyzed in their full detail due to their sheer size. In this work, we describe techniques for indexing and efficient similarity search in truly massive collections of data series, focusing on the iSAX family of data series indexes. We present their design characteristics, and describe their evolution to address different needs: bulk loading, adaptive indexing, parallelism and distribution, variable-length query answering, and bottom-up indexing. Based on this discussion, we conclude by presenting promising research directions.


Data series Time series Sequences Indexing Analytics 



I would like to thank my collaborators (in alphabetical order): R. Akbarinia, H. Benbrahim, A. Bezerianos, A. Camerra, M. Dallachiesa, N. Dayan, K. Echihabi, A. Gogolou, P. Fatourou, J. Gehrke, S. Idreos, I. Ilyas, E. Keogh, B. Kolev, H. Kondylakis, O. Levchenko, M. Linardi, Y. Lou, F. Masseglia, K. Mirylenka, B. Nushi, B. Peng, T. Rakthanmanon, D. Shasha, J. Shieh, T. Tsandilas, P. Valduriez, and D.-E. Yagoubi. Special thanks go to K. Zoumpatianos.


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Authors and Affiliations

  1. 1.University of ParisParisFrance

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