Title :
On Managing Very Large Sensor-Network Data Using Bigtable
Author :
Byunggu Yu ; Cuzzocrea, Alfredo ; Dong Jeong ; Maydebura, S.
Author_Institution :
Dept. of Comput. Sci. & Inf. Technol., Univ. of the District of Columbia, Washington, DC, USA
Abstract :
Recent advances and innovations in smart sensor technologies, energy storage, data communications, and distributed computing paradigms are enabling technological breakthroughs in very large sensor networks. There is an emerging surge of next-generation sensor-rich computers in consumer mobile devices as well as tailor-made field platforms wirelessly connected to the Internet. Billions of such sensor computers are posing both challenges and opportunities in relation to scalable and reliable management of the peta- and exa-scale time series being generated over time. This paper presents a Cloud-computing approach to this issue based on the two well-known data storage and processing paradigms: Bigtable and MapReduce.
Keywords :
cloud computing; data communication; distributed sensors; energy storage; intelligent sensors; mobile computing; time series; Bigtable; Internet; MapReduce; cloud computing approach; consumer mobile devices; data communications; data processing paradigm; data storage; distributed computing paradigms; energy storage; exascale time series; next-generation sensor-rich computers; petascale time series; reliable management; sensor computers; smart sensor technologies; tailor-made field platforms; very large sensor-network data management; Cloud computing; Computers; Distributed databases; Servers; Trajectory; Uncertainty; Bigtable; Cloud Computing; HBase; MapReduce; Sensor Networks;
Conference_Titel :
Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on
Conference_Location :
Ottawa, ON
Print_ISBN :
978-1-4673-1395-7
DOI :
10.1109/CCGrid.2012.150
