Name
Abstract | ||
|---|---|---|
Finding similar trends among time series data is critical for applications ranging from financial planning to policy making. The detection of these multifaceted relationships, especially time warped matching of time series of different lengths and alignments is prohibitively expensive to compute. To achieve real time responsiveness on large time series datasets, we propose a novel paradigm called Online Exploration of Time Series (ONEX) employing a powerful one-time preprocessing step that encodes critical similarity relationships to support subsequent rapid data exploration. Since the encoding of a huge number of pairwise similarity relationships for all variable lengths time series segments is not feasible, our work rests on the important insight that clustering with inexpensive point-to-point distances such as the Euclidean Distance can support subsequent time warped matching. Our ONEX framework overcomes the prohibitive computational costs associated with a more robust elastic distance namely the DTW by applying it over the surprisingly compact knowledge base instead of the raw data. Our comparative study reveals that ONEX is up to 19% more accurate and several times faster than the state-of-the-art. Beyond being a highly accurate and fast domain independent solution, ONEX offers a truly interactive exploration experience supporting novel time series operations. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.14778/3021924.3021933 | PVLDB |
Field | DocType | Volume |
Time series,Pairwise comparison,Data mining,Computer science,Euclidean distance,Ranging,Preprocessor,Knowledge base,Cluster analysis,Database,Encoding (memory) | Journal | 10 |
Issue | ISSN | Citations |
3 | 2150-8097 | 5 |
PageRank | References | Authors |
0.43 | 22 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rodica Neamtu | 1 | 9 | 4.26 |
| Ramoza Ahsan | 2 | 5 | 0.76 |
| Elke A. Rundensteiner | 3 | 4076 | 700.65 |
| Gábor N. Sárközy | 4 | 543 | 69.69 |