research-article

A Systematic Review of Big Data Analytics Using Model Driven Engineering

Authors:

Muhammad Nouman Zafar,

Muhammad Waseem AnwarAuthors Info & Claims

ICCBDC '17: Proceedings of the 2017 International Conference on Cloud and Big Data Computing

Pages 1 - 5

https://doi.org/10.1145/3141128.3141138

Published: 17 September 2017 Publication History

Abstract

In this era of information technology, there is a huge and excessive amount of fully distributed, structured and unstructured data which is usually referred as 'Big Data'. This data cannot be easily and directly used for business purposes due to its excessiveness nature. Therefore, it is required to intelligently process this large amount of data to extract desired information and examine pattern to make decisions and predictions for certain business objectives. In this context, Model Driven Engineering (MDE) techniques are frequently applied for Big Data analytics. This paper investigates the latest models, approaches and tools for Big Data analytics using model driven approaches. Particularly, a Systematic Literature Review (SLR) is performed to select and analyze 24 researches published during 2010 to 2017. This leads to identify 18 models, 13 tools, and 10 approaches for big data analytics using model driven approaches. The findings of this SLR are highly valuable for the researchers, students and practitioners of the domain.

References

[1]

Asha, R., Vinay, K., Nikhil, B. 2014. Early Experience with Model-driven Development of MapReduce based Big Data Application. IEEE. (Dec 2014).

[2]

Dominic, B. 2014. Towards Model-Driven Engineering for Big Data Analytics -- An Exploratory Analysis of Domain-Specific Languages for Machine Learning. IEEE. (Jan 2014).).

[3]

Dawn, N.J., Peter, B., Sohail, A. 2013. Engineering Privacy for Big Data Apps with the Unified Modeling Language. IEEE. (June 2013).

Digital Library

[4]

J.G, Enríquez., Raquel, B., F.J, D.M., Javier, T., M.J, Escalona. 2016. Towards an MDE-Based Approach to Test Entity Reconciliation Applications. ACM. (Nov 2016).

Digital Library

[5]

Abel, G., José, M., Elisabetta, N., Damian, A.T. 2016. Towards a UML Profile for Data Intensive Applications. ACM. (Jul 2016).

Digital Library

[6]

Enrico, B., Marco, G., Mauro, I. 2014. Modeling Apache Hive based applications in Big Data architectures. ACM. (Dec 2013).

Digital Library

[7]

Markus, S., Anatolij, Z. 2012. Map/Reduce on EMF Models. ACM. (Oct 2012).

Digital Library

[8]

Xi, Y., Tom, L. 2016. Model Driven Advanced Hybrid Cloud Services for Big Data: Paradigm and Practice. IEEE. (Nov 2016).

[9]

Jinhua, C., Yuxin, W., Qin, J., Jing, T. 2016. Study of Data Analysis Model Based on Big Data Technology. IEEE. (Mar 2016).

[10]

Artem, C., Andrey, K., Shiyong, L. 2015. A Big Data Modeling Methodology for Apache Cassandra. IEEE. (Jun 2015).

[11]

Xuejian, Y., Xueqing, L. 2011. A Multidimensional Data Analysis System Based on MDA for Educational Data Warehousing. IEEE. (Aug 2011).

[12]

Heng, Z., Xinzhou, C., Weiwei, C. 2016. A Novel Precision Marketing Model Based on Telecom Big Data Analysis for Luxury Cars. IEEE. (Sep 2016).

[13]

Yicheng, H., Xingtu, L., Xing, C., Wenzhong, G. 2015. Towards Model Based Approach to Hadoop Deployment and Configuration. IEEE. (Sep 2015).

[14]

Wei, Y., Pan, D., Tarik, T., Jiafu, W., Chaofan, B. 2015. An Unlicensed Taxi Identification Model Based on Big Data Analysis. IEEE. (Nov 2015).

[15]

Marcos, A., Almeida, S., Andrey, S., Alessandra, B., Alexey, C., Ludwig, A. 2014. JUNIPER: Towards Modeling Approach Enabling Efficient Platform for Heterogeneous Big Data Analysis. ACM. (Oct 2014).

Digital Library

[16]

Michele, G., Saeed, T., Damian, A., Tamburri, Elisabetta, N. 2016. Towards A Model-Driven Design Tool for Big Data Architectures. ACM. (May 2016).

Digital Library

[17]

Jinson, Z., Mao, L.H. 2013. 5Ws Model for Big Data Analysis and Visualization. IEEE. (Dec 2013).

[18]

Nemanja, T., Veljko, M., Jakob, S., Anton, K. 2016. Paradigm Shift in Big Data SuperComputing: DataFlow vs. ControlFlow. Springer. (Oct 2016).

[19]

Noriko, E. 2016. Database application model and its service for drug discovery in Model-driven architecture. Springer. (Aug 2016).

[20]

Paola, C., Paolo, G. 2016. Big data analysis for financial risk management. Springer. (Dec 2016).

[21]

Xuemei, T., Libo, L. 2016. Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research. Springer. (Mar 2016).

Digital Library

[22]

Aniello, C., Marco, G., Mauro, I., Francesco. P. 2013. Exploiting mean field analysis to model performances of big data architectures. Elsevier. (Jul 2014).

[23]

Mininath, R.B., Vijaya, R.T. 2016. Analytics, challenges and applications in big data environment: a survey. Taylor and Francis. (Jul 2016).

[24]

Marc G., Christophe J., Ismael T., 2017, Model-driven Engineering IDE for Quality Assessment of Data-intensive Applications. ACM. (April 2017).

Digital Library

Cited By

Bazza HBimonte SGourti ZRizzi SBadir H(2025)An MDA approach for robotic-based real-time business intelligence applicationsData & Knowledge Engineering10.1016/j.datak.2025.102418(102418)Online publication date: Feb-2025
https://doi.org/10.1016/j.datak.2025.102418
Naveed HArora CKhalajzadeh HGrundy JHaggag O(2024)Model driven engineering for machine learning components: A systematic literature reviewInformation and Software Technology10.1016/j.infsof.2024.107423169(107423)Online publication date: May-2024
https://doi.org/10.1016/j.infsof.2024.107423
De Sanctis MIovino LRossi MWimmer M(2022)MIKADO: a smart city KPIs assessment modeling frameworkSoftware and Systems Modeling (SoSyM)10.1007/s10270-021-00907-921:1(281-309)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10270-021-00907-9

Index Terms

A Systematic Review of Big Data Analytics Using Model Driven Engineering
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Modeling and simulation
    1. Model development and analysis

Recommendations

Big Data Analytics
Responsible Big Data Analytics for E-Business Services
ICBDR '21: Proceedings of the 5th International Conference on Big Data Research

This paper examines responsible big data analytics for e-business services and looks at how to use responsible big data analytics to obtain responsible e-business services. It addresses why responsibility matters to big data analytics and e-business ...
Big Data Analytics: A Cognitive Perspectives

Big data are pervasively generated by human cognitive processes, formal inferences, and system quantifications. This paper presents the cognitive foundations of big data systems towards big data science. The key perceptual model of big data systems is ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICCBDC '17: Proceedings of the 2017 International Conference on Cloud and Big Data Computing

September 2017

135 pages

ISBN:9781450353434

DOI:10.1145/3141128

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Northumbria University: University of Northumbria at Newcastle

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 September 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICCBDC 2017

ICCBDC 2017: 2017 International Conference on Cloud and Big Data Computing

September 17 - 19, 2017

London, United Kingdom

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
333
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Bazza HBimonte SGourti ZRizzi SBadir H(2025)An MDA approach for robotic-based real-time business intelligence applicationsData & Knowledge Engineering10.1016/j.datak.2025.102418(102418)Online publication date: Feb-2025
https://doi.org/10.1016/j.datak.2025.102418
Naveed HArora CKhalajzadeh HGrundy JHaggag O(2024)Model driven engineering for machine learning components: A systematic literature reviewInformation and Software Technology10.1016/j.infsof.2024.107423169(107423)Online publication date: May-2024
https://doi.org/10.1016/j.infsof.2024.107423
De Sanctis MIovino LRossi MWimmer M(2022)MIKADO: a smart city KPIs assessment modeling frameworkSoftware and Systems Modeling (SoSyM)10.1007/s10270-021-00907-921:1(281-309)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10270-021-00907-9
Volk MStaegemann DProthmann FTurowski K(2020)Towards an Automatized Way for Modeling Big Data System ArchitecturesBusiness Information Systems10.1007/978-3-030-53337-3_4(46-60)Online publication date: 22-Jul-2020
https://doi.org/10.1007/978-3-030-53337-3_4

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten