Many Big Data analytics essentially explore the relationship among interconnected entities, which are naturally represented as graphs. However, due to the irregular data access patterns in the graph computations, it remains a fundamental challenge to deliver highly efficient solutions for large scale graph analytics. Such inefficiency restricts the utilization of many graph algorithms in Big Da...

In this paper, we study the features of big data and analytics. We see how Big Data contributes to mobile networks. give a term in which generally refers large amount digital data. Also, estimate that processed by "Big Data" systems will double every two years. Hence, on networks need be analyzed in-depth according retrieve exciting useful information. provides unprecedented opportunities for i...

While Data Analytics programs are proliferating in many disciplines, traditional academic approaches seem to favor depth of technical skills over soft skills in their approaches to creating Data Analytics programs. Yet business wants an equal focus on communication, relationship development, and other soft skills. This panel will explore the issues in graduate analytics education to develop an ...

The explosive growth of user-contributed reviews in e-Commerce and online social network sites prompts for the design of novel big data analytics frameworks to cope with such a challenge. The main contributions of our research are twofold. First, we design a novel big data analytics framework that leverages distributed computing and streaming to efficiently process big social media data streams...

The sofTware developmenT process produces various types of data such as source code, bug reports, checkin histories, and test cases. Over the past few decades, researchers have used analytics techniques on such data to better understand software quality (for some examples, see the sidebar). Also during that period, Internet access has become widely available, and software services now play an i...

Abstract The relevance and importance of contextualizing data analytics is described. Qualitative characteristics might form the context of quantitative analysis. Topics that are at issue include: contrast, baselining, secondary data sources, supplementary data sources; dynamic and heterogeneous data. In geometric data analysis, especially with the Correspondence Analysis platform, various case...