Study on big data analytics research domains

Data Analytics is the trending domain that analyses data to observe patterns and predict future outcomes. The outcomes are based upon analysis of past and current trends and behaviors. Data analytics deals with both descriptive and predictive analyses of data. Descriptive Data Analytics summarizes the data, it's behavior and draws useful conclusion from it. Predictive Data Analytics is the branch of data analytics that predicts future outcomes based on the current and historical data. These future predictions are drawn by observing patterns followed for past data and outcomes for the past events for similar scenarios. In this paper, various branches of data analytics have been discussed. Big data analytics architecture gives an overview of the various tools and system structure involved in big data analytics. Big data analytics is closely related to data mining and hence, implements data mining algorithms. Latter part of the paper covers machine learning algorithms and neural networks for training the dataset to recognize patterns for the modeled data and predict outcomes based on the training and pattern recognition. Modeling of data using neural networks helps in generating accurate and exhaustive outcomes.

[1]  Ian T. Foster,et al.  Efficient and Secure Transfer, Synchronization, and Sharing of Big Data , 2014, IEEE Cloud Computing.

[2]  Amandeep Khurana.  Bringing Big Data Systems to the Cloud , 2014, IEEE Cloud Computing.

[3]  Rajeev Agrawal,et al.  Challenges of data integration and interoperability in big data , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[4]  Domenico Talia,et al.  Clouds for Scalable Big Data Analytics , 2013, Computer.

[5]  D. Chiadmi,et al.  A method for modelling and organazing ETL processes , 2012, Second International Conference on the Innovative Computing Technology (INTECH 2012).

[6]  Gang Chen,et al.  R-Store: A scalable distributed system for supporting real-time analytics , 2014, 2014 IEEE 30th International Conference on Data Engineering.

[7]  Alan Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[8]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[9]  Subrata Das,et al.  Distributed big data search for analyst queries and data fusion , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[10]  Scott Shenker,et al.  Spark: Cluster Computing with Working Sets , 2010, HotCloud.

[11]  Tomasz Janowski,et al.  Interoperability in Big, Open, and Linked Data--Organizational Maturity, Capabilities, and Data Portfolios , 2014, Computer.

[12]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[13]  J. Hilbe.  Logistic Regression Models , 2009 .

[14]  D. Richardson,et al.  Poisson regression analysis of ungrouped data , 2005, Occupational and Environmental Medicine.

[15]  Zahir Tari,et al.  A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis , 2014, IEEE Transactions on Emerging Topics in Computing.