Heterogeneous Data and Big Data Analytics

Heterogeneity is one of major features of big data and heterogeneous data result in problems in data integration and Big Data analytics. This paper introduces data processing methods for heterogeneous data and Big Data analytics, Big Data tools, some traditional data mining (DM) and machine learning (ML) methods. Deep learning and its potential in Big Data analytics are analysed. The benefits of the confluences among Big Data analytics, deep learning, high performance computing (HPC), and heterogeneous computing are presented. Challenges of dealing with heterogeneous data and Big Data analytics are also discussed.

[1]  Fernando Almeida,et al.  The main challenges and issues of big data management , 2013 .

[2]  Shai Ben-David,et al.  Understanding Machine Learning: From Theory to Algorithms , 2014 .

[3]  Robert Kabacoff,et al.  R in Action: Data Analysis and Graphics with R , 2015 .

[4]  Ahmed Elragal,et al.  Big Data Analytics: A Literature Review Paper , 2014, ICDM.

[5]  K U Jaseena,et al.  ISSUES , CHALLENGES , AND SOLUTIONS : BIG DATA MINING , 2014, NETCOM 2014.

[6]  S. R,et al.  Data Mining with Big Data , 2017, 2017 11th International Conference on Intelligent Systems and Control (ISCO).

[7]  Galit Shmueli,et al.  Data Mining in Excel: Lecture Notes and Cases , 2005 .

[8]  Luís Torgo,et al.  Data Mining with R: Learning with Case Studies , 2010 .

[9]  Yanchang Zhao R and Data Mining: Examples and Case Studies , 2012 .

[10]  Alexandros Labrinidis,et al.  Challenges and Opportunities with Big Data , 2012, Proc. VLDB Endow..

[11]  Shu-Ching Chen,et al.  Computational Health Informatics in the Big Data Age , 2016, ACM Comput. Surv..

[12]  Laijo John Pullokkaran Analysis of data virtualization & enterprise data standardization in business intelligence , 2013 .

[13]  Vasant Honavar,et al.  Learning classifiers from distributed, semantically heterogeneous, autonomous data sources , 2004 .

[14]  Cynthia Rudin,et al.  Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society , 2014 .

[15]  Marek Obitko,et al.  Semantic Heterogeneity Reduction for Big Data in Industrial Automation , 2014, ITAT.

[16]  Yusuf Perwej,et al.  An Experiential Study of the Big Data , 2017 .

[17]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[18]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[19]  M. E Student,et al.  Review on "Data Mining with Big Data" , 2014 .

[20]  Dorian Pyle,et al.  Data Preparation for Data Mining , 1999 .

[21]  Juan Zhang,et al.  Toward Effective Big Data Analysis in Continuous Auditing , 2015 .

[22]  M. Anusha,et al.  Big Data-Survey , 2016 .

[23]  Ramesh C. Jain,et al.  Situation recognition: an evolving problem for heterogeneous dynamic big multimedia data , 2012, ACM Multimedia.

[24]  Peter Harrington,et al.  Machine Learning in Action , 2012 .