Interactive time series analytics powered by ONEX

Neamtu, R. and Ahsan, R. and Lovering, C. and Nguyen, C. and Rundensteiner, Elke and Sárközy, Gábor (2017) Interactive time series analytics powered by ONEX. In: 2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017. Proceedings of the ACM SIGMOD International Conference on Management of Data (Part F). Association for Computing Machinery, New York, pp. 1595-1598. ISBN 9781450341974

Preview

Text p1595_neamtu_u.pdf Download (963kB) | Preview

Abstract

Modern applications in this digital age collect a staggering amount of time series data from economic growth rates to electrical household consumption habits. To make sense of it, domain analysts interactively sift through these time series collections in search of critical relationships between and recurring patterns within these time series. The ONEX (Online Exploration of Time Series) system supports effective exploratory analysis of time series collections composed of heterogeneous, variable-length and misaligned time series using robust alignment dynamic time warping (DTW) methods. To assure real-time responsiveness even for these complex and compute-intensive analytics, ONEX precomputes and then encodes time series relationships based on the inexpensive-to-compute Euclidean distance into the ONEX base. Thereafter, based on a solid formal foundation, ONEX uses DTW-enhanced analytics to correctly extract relevant time series matches on this Euclidean-prepared ONEX base. Our live interactive demonstration shows how our ONEX exploratory tool, supported by a rich array of visual interactions and expressive visualizations, enables efficient mining and interpretation of the MATTERS real data collection composed of economic, social, and education data trends across the fifty American states. © 2017 ACM.

Actions (login required)

Edit Item