DocumentCode :
267168
Title :
R2Time: A Framework to Analyse Open TSDB Time-Series Data in HBase
Author :
Agrawal, Bikash ; Chakravorty, Antorweep ; Chunming Rong ; Wlodarczyk, Tomasz Wiktor
Author_Institution :
Dept. of Comput. & Electr. Eng., Univ. of Stavanger, Stavanger, Norway
fYear :
2014
fDate :
15-18 Dec. 2014
Firstpage :
970
Lastpage :
975
Abstract :
In recent years, the amount of time series data generated in different domains have grown consistently. Analyzing large time-series datasets coming from sensor networks, power grids, stock exchanges, social networks and cloud monitoring logs at a massive scale is one of the biggest challenges that data scientists are facing. Big data storage and processing frameworks provides an environment to handle the volume, velocity and frequency attributes associated with time-series data. We propose an efficient and distributed computing framework - R2Time for processing such data in the Hadoop environment. It integrates R with a distributed time-series database (Open TSDB) using a MapReduce programming framework (RHIPE). R2Time allows analysts to work on huge datasets from within a popular, well supported, and powerful analysis environment.
Keywords :
Big Data; parallel processing; time series; HBase; Hadoop environment; MapReduce programming framework; OpenTSDB; OpenTSDB time-series data; R2Time; RHIPE; big data processing frameworks; big data storage; data scientists; distributed computing framework; distributed time-series database; frequency attributes; large time-series dataset analysis; velocity attributes; Computational modeling; Data models; Data visualization; Distributed databases; Libraries; Measurement; Programming; HBase; Hadoop; Open TSDB; R; time-series;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference on
Conference_Location :
Singapore
Type :
conf
DOI :
10.1109/CloudCom.2014.84
Filename :
7037792
Link To Document :
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