ANALYSIS OF CHALLENGES, OPEN RESEARCH ISSUES AND TOOLS BASED ON SURVEY OF BIG DATA ANALYTICS

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

A vast terabyte database is built using existing data systems, digitally-equipped technologies and the Internet of Things. This enormous data analysis needs considerable effort to extract decision-making information at several levels. Big data searches have gained significant attention in recent years as localization applications have increased exponentially. Current Big Data systems, however, are disc based and do not meet superior efficiency and short reaction times. As data in distributed memory environments increases, consumers desire data processing to be modest. The additional benefit is that it presents a new perspective for academics to go into open-ended research issues. This idea provides a way by which big data analysis may be carried out within SOMA (Scalable and Operational Memory Analytics) environment. SOMA features a Data Analysis Index System of two levels.

References

[1] A survey of data partitioning and sampling methods to support big data analysis: Mohammad Sultan Mahmud; Joshua Zhexue Huang; Salman Salloum; Tamer Z. Emara; Kuanishbay Sadatdiynov, Big Data Mining and Analytics_2020.

[2] Appling big data based deep learning system to intrusion detection: Wei Zhong; Ning Yu; Chunyu Ai, Big Data Mining and Analytics_2020.

[3] A novel clustering technique for efficient clustering of big data in Hadoop Ecosystem: Sunil Kumar; Maninder Singh, Big Data Mining and Analytics_2019.

[4] Big Educational Data & Analytics: Survey, Architecture and Challenges: Kenneth Li-Minn Ang; Feng Lu Ge; Kah Phooi Seng, IEEE Access_2020.

[5] Error data analytics on RSS range-based localization: Shuhui Yang; Zimu Yuan; Wei Li, Big Data Mining and Analytics_2020.

[6] Model-Based Big Data Analytics-as-a-Service: Take Big Data to the Next Level: Claudio Agostino Ardagna; Valerio Bellandi; Michele Bezzi; Paolo Ceravolo; Ernesto Damiani; Cedric Hebert, IEEE Transactions on Services Computing_2021.

[7] A Novel Data-Driven Situation Awareness Approach for Future Grids—Using Large Random Matrices for Big Data Modeling: Xing He; Lei Chu; Robert Caiming Qiu; Qian Ai; Zenan Ling, IEEE Access_2018.

[8] Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management: Dinithi Nallaperuma; Rashmika Nawaratne; Tharindu Bandaragoda; Achini Adikari; Su Nguyen; Thimal Kempitiya; Daswin De Silva; Damminda Alahakoon; Dakshan Pothuhera, IEEE Transactions on Intelligent Transportation Systems_2019.

[9] A Methodology of Real-Time Data Fusion for Localized Big Data Analytics: Sohail Jabbar; Kaleem R. Malik; Mudassar Ahmad; Omar Aldabbas; Muhammad Asif; Shehzad Khalid; Kijun Han; Syed Hassan Ahmed, IEEE Access_2018.

[10] Hybrid recommender system for tourism based on big data and AI: A conceptual framework: Khalid Al Fararni; Fouad Nafis; Badraddine Aghoutane; Ali Yahyaouy; Jamal Riffi; Abdelouahed Sabri, Big Data Mining and Analytics_2021.

[11] Deep Learning for IoT Big Data and Streaming Analytics: A Survey: Mehdi Mohammadi; Ala Al-Fuqaha; Sameh Sorour; Mohsen Guizani, IEEE Communications Surveys & Tutorials_2018.

[12] Cost-Effective Cloud Server Provisioning for Predictable Performance of Big Data Analytics: Fei Xu; Haoyue Zheng; Huan Jiang; Wujie Shao; Haikun Liu; Zhi Zhou, IEEE Transactions on Parallel and Distributed Systems_2019.

[13] An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management: Sandro Fiore; Donatello Elia; Carlos Eduardo Pires; Demetrio Gomes Mestre; Cinzia Cappiello; Monica Vitali; Nazareno Andrade; Tarciso Braz; Daniele Lezzi; Regina Moraes; Tania Basso; Nádia P. Kozievitch; Keiko Verônica Ono Fonseca; Nuno Antunes; Marco Vieira; Cosimo Palazzo; Ignacio Blanquer; Wagner Meira; Giovanni Aloisio, IEEE Access_ 2019.

Keywords

Data Storage, Visualization, Quantum Computing.