The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Big Data with Cloud Computing: an insight on the computing environment,MapReduce, and programming frameworks
Abstract
The term ‘Big Data’ has spread rapidly in the framework of Data Mining and Business Intelligence. This new scenario can be defined by means of those problems that cannot be effectively or efficiently addressed using the standard computing resources that we currently have. We must emphasize that Big Data does not just imply large volumes of data but also the necessity for scalability, i.e., to ensure a response in an acceptable elapsed time. When the scalability term is considered, usually traditional parallel‐type solutions are contemplated, such as the Message Passing Interface or high performance and distributed Database Management Systems. Nowadays there is a new paradigm that has gained popularity over the latter due to the number of benefits it offers. This model is Cloud Computing, and among its main features we has to stress its elasticity in the use of computing resources and space, less management effort, and flexible costs. In this article, we provide an overview on the topic of Big Data, and how the current problem can be addressed from the perspective of Cloud Computing and its programming frameworks. In particular, we focus on those systems for large‐scale analytics based on the MapReduce scheme and Hadoop, its open‐source implementation. We identify several libraries and software projects that have been developed for aiding practitioners to address this new programming model. We also analyze the advantages and disadvantages of MapReduce, in contrast to the classical solutions in this field. Finally, we present a number of programming frameworks that have been proposed as an alternative to MapReduce, developed under the premise of solving the shortcomings of this model in certain scenarios and platforms.WIREs Data Mining Knowl Discov 2014, 4:380–409. doi: 10.1002/widm.1134 This article is categorized under:Technologies > Classification Technologies > Computer Architectures for Data Mining
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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Amazon.com recommendations: item-to-item collaborative filtering
Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403
Validity of the single processor approach to achieving large scale computing capabilities
Amdahl, G. M. (1967) · Proceedings of the April 18-20, 1967, spring joint computer conference on - AFIPS '67 (Spring) · doi:10.1145/1465482.1465560 · Cited by 4,339
(2007) · doi:10.1145/1294261.1294281 · Cited by 3,447
Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.109 · Cited by 2,410
Madden, S. (2012) · IEEE Internet Computing · doi:10.1109/mic.2012.50 · Cited by 509 · cited 6×
“Its main features are the following ones: (1) addition of a ‘configure’ phase for loading static data in both Map and Reduce tasks; (2) using a higher granularity for the Map tasks; (3) a new ‘Combine’ operation that acts as another level of reduction; and (4) the implementation of a set of programming extensions to M…”
BackgroundMethodInfluentialIntegrated Decision Support Systems: A data warehousing perspective
March, S. T., & Hevner, A. R. (2007) · Decision Support Systems · doi:10.1016/j.dss.2005.05.029 · Cited by 308
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García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016) · Big Data Analytics · doi:10.1186/s41044-016-0014-0 · Cited by 709
Self-citationBig data analytics on Apache Spark
Salloum, S., Dautov, R., Chen, X., Peng, P. X., & Huang, J. Z. (2016) · International Journal of Data Science and Analytics · doi:10.1007/s41060-016-0027-9 · Cited by 428
Fourth Industrial Revolution: technological drivers, impacts and coping methods
Li, G., Hou, Y., & Wu, A. (2017) · Chinese Geographical Science · doi:10.1007/s11769-017-0890-x · Cited by 375
kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data
Maillo, J., Ramírez, S., Triguero, I., & Herrera, F. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.06.012 · Cited by 316
Self-citationResearch Landscape of Business Intelligence and Big Data analytics: A bibliometrics study
Liang, T.-P., & Liu, Y.-H. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.05.018 · Cited by 256
Metadata
- Authors
- Fernández, A., del Río, S., López, V., Bawakid, A., del Jesus, M. J., Benítez, J. M., & Herrera, F.
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2014-09
- Volume
- 4
- Issue
- 5
- Pages
- 380-409
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W1485408073
- Cited by
- 214
- References
- 7
- FWCI
- 87.83
- Type
- journal article
- Language
- en
- Open access
- closed
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