References
- W. L. Low, M. L. Lee, and T. W. Ling, “A knowledge-based approach for duplicate elimination in data cleaning,” vol. 26, pp. 585–606, 2001.
- R. Kune, P. K. Konugurthi, A. Agarwal, R. R. Chillarige, and R. Buyya, “The anatomy of big data computing,” Softw. - Pract. Exp., vol. 46, no. 1, pp. 79–105, 2016. doi: 10.1002/spe.2374
- J. L. Leevy, T. M. Khoshgoftaar, R. A. Bauder, and N. Seliya, “A survey on addressing high - class imbalance in big data,” J. Big Data, 2018.
- R. Casado and M. Younas, “Emerging trends and technologies in big data processing,” no. October 2014, pp. 2078–2091, 2015.
- H. Xiong, G. Pandey, M. Steinbach, and V. Kumar, “Enhancing Data Analysis with Noise Removal,” vol. X, no. X, pp. 1–36.
- Crowd Flower, “CrowdFlower 2017 Data Science Report.” p. 14, 2017.
- J. I. Maletic and A. Marcus, “Data Cleansing Data Cleansing A Prelude to to Knowledge Discovery,” 1998.
- “Data cleansing.”
- “A Data-Cleaning Tool for Building Better Prediction Models _ Data Science Institute.”
- D. Blazquez and J. Domenech, “Technological Forecasting & Social Change Big Data sources and methods for social and economic analyses,” Technol. Forecast. Soc. Chang., vol. 130, no. March 2017, pp. 99–113, 2018. doi: 10.1016/j.techfore.2017.07.027
- B. D. Analysis, “CHAPTER Research Trends in Big Data Analysis,” 2020.
- S. Madan, “k-DDD Measure and MapReduce Based Anonymity Model for Secured Privacy-Preserving Big Data Publishing,” vol. 27, no. 2, pp. 177–199, 2019.
- C. Zheng and H. Huang, “Analysis of technology diffusion in agricultural industry cluster based on system dynamics and simulation model,” vol. 0529, 2018.
- J. Gong, “Association feature mining algorithm of web accessing data in big data environment,” vol. 0529, 2018.
- H. Yan, “Mass data storage and sharing algorithm in distributed heterogeneous environment,” vol. 0529, 2018.
- H. Galhardas, “Data Cleaning and Transformation - Tools.”
- Mabu, Audu Musa, Rajesh Prasad, and Raghav Yadav. “Mining gene expression data using data mining techniques: A critical review.” Journal of Information and Optimization Sciences (2019): 1-20. doi: 10.1080/02522667.2018.1555311
- C. L. P. Chen and C. Zhang, “Data-intensive applications , challenges, techniques and technologies : A survey on Big Data,” Inf. Sci. (Ny)., vol. 275, pp. 314–347, 2014. doi: 10.1016/j.ins.2014.01.015
- M. S. A. L. Karim, “An Efficient Distributed Algorithm for Big Data Processing,” 2017.
- A. Oussous, F. Benjelloun, A. Ait, and S. Belfkih, “Big Data technologies : A survey,” J. King Saud Univ. - Comput. Inf. Sci., vol. 30, no. 4, pp. 431–448, 2018.
- S. Wang and Y. Hou, “Microelectronics Reliability Big data analysis for distributed computing job scheduling and reliability evaluation,” vol. 94, no. January, pp. 41–45, 2019.
- C. Reuter, A. L. Hughes, M. Kaufhold, and C. Reuter, “Social Media in Crisis Management : An Evaluation and Analysis of Crisis Informatics Research Social Media in Crisis Management : An Evaluation and Analysis of Crisis Informatics,” Int. J. Human–Computer Interact., vol. 34, no. 4, pp. 280–294, 2018. doi: 10.1080/10447318.2018.1427832
- H. Lin and W. Ho, “Cultural Effects on Use of Online Social Media for Health-Related Information Acquisition and Sharing in Taiwan Cultural Effects on Use of Online Social Media for Health-Related Information Acquisition and Sharing in Taiwan,” Int. J. Human–Computer Interact., vol. 34, no. 11, pp. 1063–1076, 2018. doi: 10.1080/10447318.2017.1413790
- J. Dean and S. Ghemawat, “MapReduce : Simplified Data Processing on Large Clusters,” pp. 1–13.
- V. Kalavri and V. Vlassov, “MapReduce : Limitations , Optimizations and Open Issues MapReduce : Limitations , Optimizations and Open Issues,” no. May, 2015.
- K. Lee and Y. Lee, “Parallel Data Processing with MapReduce : A Survey,” vol. 40, no. 4, pp. 11–20, 2011.
- R. Anandkrishna and D. Kumar, “Improving Mapreduce for Incremental Processing Using Map Data Storage,” Procedia - Procedia Comput. Sci., vol. 87, pp. 288–293, 2016. doi: 10.1016/j.procs.2016.05.163
- U. Sivarajah, M. M. Kamal, Z. Irani, and V. Weerakkody, “Critical analysis of Big Data challenges and analytical methods,” J. Bus. Res., vol. 70, pp. 263–286, 2017. doi: 10.1016/j.jbusres.2016.08.001
- N. Khan et al., “Big Data : Survey , Technologies , Opportunities , and Challenges,” vol. 2014, 2014.
- O. Azeroual, G. Saake, and E. Schallehn, “International Journal of Information Management Analyzing data quality issues in research information systems via data pro fi ling,” Int. J. Inf. Manage., vol. 41, no. January, pp. 50–56, 2018. doi: 10.1016/j.ijinfomgt.2018.02.007
- E. Rahm, “Data Cleaning : Problems and Current Approaches.”
- E. Yur and V. Vasil, “Analytical Review of Data Visualization Methods in Application to Big Data,” vol. 2013, 2013.
- R. Khedri, F. Chiang, and K. Eddin, “An Algebraic Approach Towards Data Cleaning $,” Procedia - Procedia Comput. Sci., vol. 21, pp. 50–59, 2013. doi: 10.1016/j.procs.2013.09.009
- Poonia, Ramesh C., and Linesh Raja. “On-Demand Routing Protocols for Vehicular Cloud Computing.” Vehicular Cloud Computing for Traffic Management and Systems. IGI Global, 2018. 151-177. doi: 10.4018/978-1-5225-3981-0.ch007