Name
Abstract | ||
|---|---|---|
Ubiquitous computing environments generate a large amount of data at the edges of networks, e.g., sensors, which measure or monitor the real world. To analyze such data, we needed to transmit the data to a cluster of high-performance servers, e.g., data centers and cloud computing. However, the cost of data transmission is heavy. With the aim of enabling ubiquitous computing environments to analyze the data, we propose using MapReduce, which is a popular approach to analyzing data in big data processing that have been designed for high-performance servers. The framework proposed in this paper deploys programs for data processing at the nodes that contain the target data as a map step and executes the programs with the local data. Finally, it aggregates the results of the programs to certain nodes as a reduction step. The architecture of the framework, its basic performance, and its application are also described here. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IUCC-CSS.2016.010 | 2016 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS) |
Keywords | Field | DocType |
ubiquitous MapReduce processing,data processing,high-performance servers | Big data processing,Architecture,Data processing,Data transmission,Computer science,Server,Computer network,Ubiquitous computing,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-5090-5567-8 | 0 | 0.34 |
References | Authors | |
9 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ichiro Satoh | 1 | 882 | 96.32 |