International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 74 - Number 17 |
Year of Publication: 2013 |
Authors: Mirko K¨ampf, Jan W. Kantelhardt |
10.5120/12974-0233 |
Mirko K¨ampf, Jan W. Kantelhardt . Hadoop.TS: Large-Scale Time-Series Processing. International Journal of Computer Applications. 74, 17 ( July 2013), 1-8. DOI=10.5120/12974-0233
The paper describes a computational framework for time-series analysis. It allows rapid prototyping of new algorithms, since all components are re-usable. Generic data structures represent different types of time series, e. g. event and interevent time series, and define reliable interfaces to existing big data. Standalone applications, highly scalable MapReduce programs, and User Defined Functions for Hadoop-based analysis frameworks are the major modes of operation. Efficient implementations of univariate and bivariate analysis algorithms are provided for, e. g. , long-term correlation, crosscorrelation and event synchronization analysis on large data sets.