ADS: the adaptive data series index


Numerous applications continuously produce big amounts of data series, and in several time critical scenarios analysts need to be able to query these data as soon as they become available. This, however, is not currently possible with the state-of-the-art indexing methods and for very large data series collections. In this paper, we present the first adaptive indexing mechanism, specifically tailored to solve the problem of indexing and querying very large data series collections. We present a detailed design and evaluation of our method using approximate and exact query algorithms with both synthetic and real data sets. Adaptive indexing significantly outperforms previous solutions, gracefully handling large data series collections, reducing the data to query delay: By the time state-of-the-art indexing techniques finish indexing 1 billion data series (and before answering even a single query), our method has already answered \(3*10^5\) queries.

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    This paper is an extended version of [22]. It describes an exact search algorithm and a new full index construction method, both outperforming the state of the art. It also includes more detailed discussions and additional experiments.


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We would like to thank Prof. Volker Beckmann for providing us the Astro data set [60].

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Correspondence to Kostas Zoumpatianos.

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Zoumpatianos, K., Idreos, S. & Palpanas, T. ADS: the adaptive data series index. The VLDB Journal 25, 843–866 (2016).

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  • Data Series
  • Query Processing
  • Leaf Size
  • Indexing Cost
  • Query Answering