A survey on data stream, big data and real-time

被引：0
作者：
Gomes E.H.A. [1 ]
Plentz P.D.M. [1 ]
De Rolt C.R. [2 ]
Dantas M.A.R. [3 ]
机构：
[1] Department of Informatics and Statistics (INE), Federal University of Santa Catarina (UFSC), Florianópolis, SC
[2] Centre of Management and Socioeconomic Science (ESAG), State University of Santa Catarina (UDESC), Florianópolis, SC
[3] Department of Computer Science (DCC), Federal University of Juiz de Fora (UFJF), Juiz de Fora, MG
来源：
International Journal of Networking and Virtual Organisations | 2019年 / 20卷 / 02期
关键词：
Big data; Big data stream tools; Data stream; Real-time; Stream processing; Time constraint;
D O I：
10.1504/IJNVO.2019.097631
中图分类号：
学科分类号：
摘要：
Real-time concept is being widely used by a society that seeks to speed communications, decisions and their daily activities. Even though this term is not used with the necessary conceptual precision, it makes clear the importance that time exerts on computer systems. Nowadays, the big data scenario, this concept is important and used with different meanings, which can define failure or successful of applications. This article aims to present a systematic literature review on the topics of data stream, big data and real-time. For this, we developed a protocol revision in which were determined research questions, the search term, the search source and the inclusion and exclusion criteria of articles. After an extensive study, we classify the articles selected in seven categories according to real-time concept used. Finally, we present a discussion that shows that there is not convergence on real-time concepts in the big data literature. Copyright © 2019 Inderscience Enterprises Ltd.
引用
收藏
页码：143 / 167
页数：24
相关论文
共 83 条
[1]  
Amatriain X., Big & personal: Data and models behind netflix recommendations, Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining Algorithms, Systems, Programming Models and Applications - BigMine'13, pp. 1-6, (2013)
[2]  
Antonic A., Marjanovic M., Pripuzic K., Podnar Zarko I., A mobile crowdsensing ecosystem enabled by CUPUS: Cloud-based publish/subscribe middleware for the internet of things, Future Generation Computer Systems, 56, pp. 607-622, (2016)
[3]  
Ayhan S., Pesce J., Comitz P., Sweet D., Bliesner S., Gerberick G., Predictive analytics with aviation big data, Integrated Communications, Navigation and Surveillance Conference, ICNS, (2013)
[4]  
Babcock B., Babu S., Datar M., Motwani R., Widom J., Models and issues in data stream systems, Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 1-16, (2002)
[5]  
Basanta-Val P.N., Fernandez-Garcia A.J.W., Audsley N.C., Improving the predictability of distributed stream processors, Future Generation Computer Systems, 52, pp. 22-36, (2015)
[6]  
Bouillet E., Kothari R., Kumar V., Mignet L., Nathan S., Ranganathan A., Turaga D.S., Udrea O., Verscheure O., Experience Report: Processing 6 Billion CDRs/Day - From Research to Production, pp. 264-267, (2012)
[7]  
Braik W., Morandat F., Falleri J.-R., Blanc X., Real time streaming pattern detection for eCommerce, Proceedings of the 31st Annual ACM Symposium on Applied Computing - SAC'16, pp. 916-922, (2016)
[8]  
Cao L., Wang Q., Rundensteiner E.A., Interactive outlier exploration in big data streams, Proc. VLDB Endow., pp. 1621-1624, (2014)
[9]  
Chaolong J., Hanning W., Lili W., Research on visualization of multi-dimensional real-time traffic data stream based on cloud computing, Procedia Engineering, 137, pp. 709-718, (2016)
[10]  
Chardonnens T., Cudre-Mauroux P., Grund M., Perroud B., Big data analytics on high Velocity streams: A case study, 2013 IEEE International Conference on Big Data, pp. 784-787, (2013)
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