Name
Abstract | ||
|---|---|---|
New technologies and applications in the Information Science are generating more data than ever before. Hence, streaming data processing has become a hot topic in academic research and industrial applications. The problem of offline stream processing is a variant of stream data processing in data warehousing environment. Efficient and scalable methods are needed for solving offline stream processing problems. This paper proposes a MapReduce based framework named MaRAOS (MapReduce for Analyzing of Offline Streams) for analyzing offline streams. Two modules in this framework: Synopses Generation and Query Analyzation are introduced. A web-log dataset is used to illustrate this framework. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.49 | 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) |
Keywords | Field | DocType |
MapReduce-based framework,information science,industrial applications,academic research,data warehousing,stream data processing,offline stream processing problems,MaRAOS,MapReduce for analyzing of offline streams,query modules,query analysis,Web-log dataset,synopses generation | Data warehouse,Data mining,Data structure,Data processing,Computer science,Information science,Emerging technologies,Stream processing,STREAMS,Database,Scalability | Conference |
ISBN | Citations | PageRank |
978-1-5090-4066-7 | 0 | 0.34 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruoyu Chen | 1 | 11 | 6.62 |
| Yangsen Zhang | 2 | 4 | 2.47 |
| RongRong Bi | 3 | 0 | 0.34 |
| Yuru Jiang | 4 | 0 | 0.34 |
| Yanhua Zhang | 5 | 145 | 24.84 |