Limitations of Big Data Partitions Technology

Nguyen Huyen Trang
Pages 11-19
Abstract Views 993
PDF Downloads 624
Crossref Citations -
DOI https://doi.org/10.47738/jads.v1i1.7

Abstract


Big data is defined as the amount of data that is needed by new technology and architecture so that it is possible to extract the large amount of data provided by the analysis process. Due to its enormous size it is increasingly difficult for perfect analysis using existing traditional techniques. This technology is a solution for several problems that require a distributed system for storage needs because a problem cannot be solved in one machine. Since Big Data has become the latest technology in a market that brings tremendous profits to business organizations, it becomes possible when there are specific challenges and problems and it will continue to expand. This article introduces big data technology, and explains its partition limitations.


Keywords


Big data; Partitions; Technology; Data science

Full Text:

PDF

References


[1]    D. Arunachalam, N. Kumar, and J. P. Kawalek, “Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice,” Transp. Res. Part E Logist. Transp. Rev., vol. 114, pp. 416–436, 2018, doi: 10.1016/j.tre.2017.04.001.

[2]    F. Bajaber, S. Sakr, O. Batarfi, A. Altalhi, and A. Barnawi, “Benchmarking big data systems: A survey,” Comput. Commun., vol. 149, pp. 241–251, 2020, doi: 10.1016/j.comcom.2019.10.002.

[3]    F. Wang, Q. Wang, F. Nie, Z. Li, W. Yu, and F. Ren, “A linear multivariate binary decision tree classifier based on K-means splitting,” Pattern Recognit., vol. 107, 2020, doi: 10.1016/j.patcog.2020.107521.

[4]    T. Oates and D. Jensen, “Large Datasets Lead to Overly Complex Models: An Explanation and a Solution,” Proc. Fourth Int. Conf. Knowl. Discov. Data Min., no. Quinlan, pp. 294–298, 1998.

[5]    L. Hall, N. Chawla, and K. Bowyer, “Combining decision trees learned in parallel,” Work. Notes KDD-97 Work. Distrib. Data Min., pp. 10–15, 1998, [Online]. Available: http://www.cse.nd.edu/~nchawla/papers/DDM98.pdf.

[6]    F. Lu, Z. Shi, L. Gu, H. Jin, and L. T. Yang, “An adaptive multi-level caching strategy for Distributed Database System,” Futur. Gener. Comput. Syst., vol. 97, pp. 61–68, 2019, doi: 10.1016/j.future.2018.11.050.

[7]    D. V. Patil and R. S. Bichkar, “An Optimistic Data Mining Approach for Handling Large Data Set using Data Partitioning Techniques,” Int. J. Comput. Appl., vol. 24, no. 3, pp. 29–33, 2011, doi: 10.5120/2930-3878.

[8]    Shirkhorshidi, Ali Seyed, et al. "Big data clustering: a review." International conference on computational science and its applications. Springer, Cham, pp. 707-720, 2014. doi: 10.1007/978-3-319-09156-3_49

[9]    Anchalia, Prajesh P., Anjan K. Koundinya, and N. K. Srinath. "MapReduce design of K-means clustering algorithm." 2013 International Conference on Information Science and Applications (ICISA). IEEE, pp. 1-5, 2013. doi: 10.1109/ICISA.2013.6579448

[10]    Zhao, Weizhong, Huifang Ma, and Qing He. "Parallel k-means clustering based on mapreduce." IEEE international conference on cloud computing. Springer, Berlin, Heidelberg, pp. 674-679, 2009. doi: 10.1007/978-3-642-10665-1_71

[11]    M. M. Degree, C. Science, and A. C. Lecture, “Data Mining : Concepts and,” vol. 05, p. 703, 2012, doi: 10.1093/nar/gku1019.

[12]    X. Chen and Y. Yang, “Diffusion K-means clustering on manifolds: Provable exact recovery via semidefinite relaxations,” Appl. Comput. Harmon. Anal., vol. 1, pp. 1–45, 2020, doi: 10.1016/j.acha.2020.03.002.

[13]    He, Yaobin, et al. "MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data." Frontiers of Computer Science, vol. 8, no. 1, pp. 83-99, 2014. doi: 10.1007/s11704-013-3158-3

[14]    Agrawal, Rajeev, and Christopher Nyamful. "Challenges of big data storage and management." Global Journal of Information Technology: Emerging Technologies, vol. 6, no. 1, pp. 1-10, 2016. doi: 10.18844/gjit.v6i1.383




DOI: https://doi.org/10.47738/jads.v1i1.7

Refbacks

  • There are currently no refbacks.


Barcode

Journal of Applied Data Sciences

ISSN:2723-6471 (Online)
Publisher:Bright Publisher
Website:http://bright-journal.org/JADS
Email:taqwa@amikompurwokerto.ac.id (principal contact)
  support@bright-journal.org (technical issues)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0