EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 57 (2023) ITM Web Conf., 57 (2023) 01015 References
Open Access
Issue
ITM Web Conf.
Volume 57, 2023
Fifth International Conference on Advances in Electrical and Computer Technologies 2023 (ICAECT 2023)
Article Number 01015
Number of page(s) 10
Section Software Engineering & Information Technology
DOI https://doi.org/10.1051/itmconf/20235701015
Published online 10 November 2023
  1. Nasution, Mahyuddin KM, Opim Salim Sitompul, and Erna Budhiarti Nababan. Data science. Journal of Physics: Conference Series. Vol. 1566. No. 1. IOP Publishing, (2020). [Google Scholar]
  2. Anagnostopoulos, Eleftherios. Bus Scheduling including Dynamic Events. (2017). [Google Scholar]
  3. Jabbar, Sohail, et al. A methodology of real-time data fusion for localized big data analytics. IEEE Access 6, Pp. 24510-24520 (2018). [CrossRef] [Google Scholar]
  4. Vatrapu, Ravi, et al. Social set analysis: A set theoretical approach to big data analytics. Ieee Access 4, Pp. 2542-2571 (2016). [CrossRef] [Google Scholar]
  5. Erhan, Laura, et al. Analyzing objective and subjective data in social sciences: Implications for smart cities. IEEE Access 7, Pp. 19890-19906 (2019). [CrossRef] [Google Scholar]
  6. Feng, Mingchen, et al. Big data analytics and mining for effective visualization and trends forecasting of crime data. IEEE Access 7, Pp. 106111-106123 (2019). [CrossRef] [Google Scholar]
  7. Emara, Tamer Z., and Joshua Zhexue Huang. “Distributed data strategies to support large-scale data analysis across geo-distributed data centers.” IEEE Access 8, Pp. 178526-178538 (2020). [CrossRef] [Google Scholar]
  8. Zhu, Jiangcheng, et al. Model error correction in data assimilation by integrating neural networks. Big Data Mining and Analytics 2.2, Pp. 83-91 (2019). [CrossRef] [Google Scholar]
  9. Ang, Kenneth Li-Minn, Feng Lu Ge, and Kah Phooi Seng. Big educational data & analytics: Survey, architecture and challenges. IEEE access 8, Pp. 116392-116414 (2020). [CrossRef] [Google Scholar]
  10. Ahmad, Norita, Areeba Hamid, and Vian Ahmed. Data science: Hype and reality. Computer 55.2, Pp. 95-101 (2022). [CrossRef] [Google Scholar]
  11. Mujthaba, G. M., et al. Data Science Techniques, Tools and Predictions. International Journal of Recent Technology and Engineering (IJRTE) 8.6 (2020). [Google Scholar]
  12. Kamlangpuech, Peerapon, and Komate Amphawan. A new system for analyzing contents of computer science courses. 2020 7th International Conference on Advance Informatics: Concepts, Theory and Applications (ICAICTA). IEEE, (2020). [Google Scholar]
  13. Kim, Heonho, et al. Periodicity-oriented data analytics on time-series data for intelligence system. IEEE Systems Journal 15.4, Pp. 4958-4969 (2020). [Google Scholar]
  14. Khalajzadeh, Hourieh, et al. Survey and analysis of current end-user data analytics tool support. IEEE Transactions on Big Data 8.1, Pp. 152-165 (2019). [Google Scholar]
  15. Rawat, Danda B., Ronald Doku, and Moses Garuba. Cybersecurity in big data era: From securing big data to data-driven security. IEEE Transactions on Services Computing 14.6, Pp. 2055-2072 (2019). [Google Scholar]
  16. Menzies, Tim, Jeremy Greenwald, and Art Frank. Data mining static code attributes to learn defect predictors. IEEE transactions on software engineering 33.1, Pp. 2-13 (2006). [Google Scholar]
  17. Saabith, AL Sayeth, Elankovan Sundararajan, and Azuraliza Abu Bakar. Parallel implementation of apriori algorithms on the Hadoop-MapReduce platform-an evaluation of literature. Journal of Theoretical and Applied Information Technology 85.3, PP. 321 (2016). [Google Scholar]
  18. Rghioui, A. N. A. S. S., et al. Symmetric cryptography keys management for 6lowpan networks. Journal of Theoretical and Applied Information Technology 73.3, Pp. 336-345 (2015). [Google Scholar]
  19. Pradeepa, A., and A. S. Thanamani. Parallelized Comprising for Apriori algorithm using Mapreduce framework. International Journal of Advanced Research in Computer and Communication Engineering 2.11, Pp. 4365-4368 (2013). [Google Scholar]
  20. Rokhman, Nur, and Amelia Nursanti. The MapReduce Model on Cascading Platform for Frequent Itemset Mining. IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 12.2, Pp. 149-160 (2018). [CrossRef] [Google Scholar]
  21. Totad, Shashikumar G., et al. Scaling data mining algorithms to large and distributed datasets. Intl J Database Manag Syst 2.2, Pp. 26-35 (2010). [CrossRef] [Google Scholar]
  22. Jayasree, M. Data Mining: Exploring Big Data Using Hadoop and Map Reduce. International Journal of Engineering Science Research-IJESR, Vol. 04. No. 1 (2013). [Google Scholar]
  23. Agrawal, Rakesh, and Ramakrishnan Srikant. Fast algorithms for mining association rules. Proc. 20th int. conf. very large data bases, VLDB. Vol. 1215 (1994). [Google Scholar]
  24. Moens, Sandy, Emin Aksehirli, and Bart Goethals. Frequent itemset mining for big data. 2013 IEEE international conference on big data. IEEE, (2013). [Google Scholar]
  25. Tyagi, Amit K., R. Priya, and A. Rajeswari. Mining big data to predicting future. International Journal of Engineering Research and Applications 5.3, Pp. 14-21 (2015). [Google Scholar]
  26. Mahesh, A. Shinde#1, K. P. Adhiya. Frequent Itemset Mining Algorithms for Big Data using MapReduce Technique, ICGTETM (2016). [Google Scholar]
  27. Abinaya, K. Data mining with big data e-health service using map reduce. International Journal of Advanced Research in Computer and Communication Engineering 4.2, Pp. 123-127 (2015). [Google Scholar]
  28. Anbumalar Smilin.V1, S.P. Siddique Ibrahim. A Survey on Different Techniques for Mining Frequent Itemsets, Vol. 5. Issue 1 (2016). [Google Scholar]
  29. Hemamalini, R., and L. Josephine Mary. An analysis on multi-agent based distributed data mining system. International Journal of Scientific and Research Publications 4.6, Pp. 1-6 (2014). [Google Scholar]
  30. Yoo, Kwan-Hee, Carson K. Leung, and Aziz Nasridinov. Big data analysis and visualization: challenges and solutions. Applied Sciences 12.16, PP. 8248 (2022). [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.