Indexing for Interactive Exploration of Big Data Series


K. Zoumpatianos, S. Idreos, and T. Palpanas, “Indexing for Interactive Exploration of Big Data Series,” in Proceedings of the ACM SIGMOD International Conference on Management of Data, Snowbird, Utah, 2014.


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, which 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. The main idea is that instead of building the complete index over the complete data set up-front and querying only later, we interactively and adaptively build parts of the index, only for the parts of the data on which the users pose queries. The net effect is that instead of waiting for extended periods of time for the index creation, users can immediately start exploring the data series. We present a detailed design and evaluation of adaptive data series indexing over both synthetic data and real-world workloads. The results show that our approach can gracefully handle large data series collections, while drastically 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), adaptive data series indexing has already answered 3∗105 queries.

Last updated on 12/22/2014