Big Data Analytics

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• Publisher: North Holland
• Year: 2015
• Print ISBN: 9780444634924
• eBook ISBN: 9780444634979
• Topic: Mathematics
• Subtopic: Applied Mathematics
• Index: Mathematics

While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. In common usage, Big Data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. While there are challenges, there are huge opportunities emerging in the fields of Machine Learning, Data Mining, Statistics, Human-Computer Interfaces and Distributed Systems to address ways to analyze and reason with this data. The edited volume focuses on the challenges and opportunities posed by "Big Data" in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. Big data has the potential to help companies improve operations and make faster, more intelligent decisions.- Review of big data research challenges from diverse areas of scientific endeavor- Rich perspective on a range of data science issues from leading researchers- Insight into the mathematical and statistical theory underlying the computational methods used to address big data analytics problems in a variety of domains

A:

Modeling and Analytics

Chapter 1

Document Informatics for Scientific Learning and Accelerated Discovery

Venu Govindaraju*^,1; Ifeoma Nwogu*^,1; Srirangaraj Setlur*^{,1 *} University at Buffalo, SUNY, Buffalo, New York, USA
¹ Corresponding authors: email address: [email protected], [email protected], [email protected]

Abstract

This chapter presents a concept paper that describes methods to accelerate new materials discovery and optimization, by enabling faster recognition and use of important theoretical, computational, and experimental information aggregated from peer-reviewed and published materials-related scientific documents online. To obtain insights for the discovery of new materials and to study about existing materials, research and development scientists and engineers rely heavily on an ever-growing number of materials research publications, mostly available online, and that date back many decades. So, the major thrust of this concept paper is the use of technology to (i) extract “deep” meaning from a large corpus of relevant materials science documents; (ii) navigate, cluster, and present documents in a meaningful way; and (iii) evaluate and revise the materials-related query responses until the researchers are guided to their information destination. While the proposed methodology targets the interdisciplinary field of materials research, the tools to be developed can be generalized to enhance scientific discoveries and learning across a broad swathe of disciplines. The research will advance the machine-learning area of developing hierarchical, dynamic topic models to investigate trends in materials discovery over user-specified time periods. Also, the field of image-based document analysis will benefit tremendously from machine learning tools such as the use of deep belief networks for classification and text separation from document images. Developing an interactive visualization tool that can display modeling results from a large materials network perspective as well as a time-based perspective is an advancement in visualization studies.

Keywords

Accelerated discovery

Digital document analysis

Probabilistic topic models

Scientific learning

Visualization

1 Introduction

In June 2011, the White House announced the Materials Genome Initiative (MGI), as a critical effort to enhance America’s global competitiveness, by bolstering the U.S. advanced manufacturing enterprise (White House Materials Genome Initiative (MGI), 2011). MGI was launched as a presidential initiative to aid businesses discover, develop, and deploy new materials twice as fast. “The invention of silicon circuits and lithium ion batteries made computers and iPods and iPads possible, but it took years to get those technologies from the drawing board to the market place,” said the President as he announced the Initiative. “We can do it faster.” Accelerating the pace of discovery and deployment of advanced material...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Preface
• A: Modeling and Analytics
• B: Applications and Infrastructure
• Index