Name
Abstract | ||
|---|---|---|
With the development of Internet of Things applications based on sensor data, how to process high speed data stream over large scale history data brings a new challenge. This paper proposes a new programming model RTMR, which improves the real-time capability of traditional batch processing based MapReduce by preprocessing and caching, along with pipelining and localizing. Furthermore, to adapt the topologies to application characteristics and cluster environments, a model analysis based RTMR cluster constructing method is proposed. The benchmark built on the urban vehicle monitoring system shows RTMR can provide the real-time capability and scalability for data stream processing over large scale data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34321-6_57 | ICSOC |
Keywords | Field | DocType |
model analysis,mapreduce-based data stream processing,large scale data,large history data,real-time capability,data stream processing,new challenge,cluster environment,sensor data,large scale history data,high speed data stream,rtmr cluster | Data mining,Pipeline (computing),Data stream mining,Programming paradigm,Computer science,Data stream,Network topology,Preprocessor,Batch processing,Scalability | Conference |
Citations | PageRank | References |
4 | 0.43 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaiyuan Qi | 1 | 13 | 2.47 |
| Zhuofeng Zhao | 2 | 66 | 15.46 |
| Jun Fang | 3 | 19 | 3.26 |
| Yanbo Han | 4 | 500 | 59.74 |