COVER FEATURE GUEST EDITORS’ INTRODUCTION

BigData

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If we apply the Gartner Hype Cycle for Emerging Technologies to big data, the technology has passed through the peak of in� ated expec-

tations and the trough of disillusion-ment, and is now moving steadily along the slope of enlightenment (www.gartner.com/technology /research/met hodolog ies/ hy pe -c yc le.js p). The data� cation of our world means that big data permeates not only sci-ence and engineering, but also such diverse and creative disciplines as the arts and humanities.

For this special theme issue, we have assembled a group of

� ve articles that repre-sent modern trends in big data by examin-ing speci� c technolo-gies and developments

spanning databases, al-gorithms, and applications.

IN THIS ISSUEOne of the early technologies asso-

ciated with the big data revolution is NoSQL. As this part of the hype cycle passed, there was a lack of consensus about what NoSQL denotes, as the term has been used to con� ate a large set of features. Jignesh M. Patel demysti� es this issue in his article “Operational

NoSQL Systems: What’s New and What’s Next?” In addition to providing clear de� nitions and delineations, the article also identi� es promising direc-tions of future research.

In “Renaissance in Database Man-agement: Navigating the Landscape of Candidate Systems,” Venkat Gudivada, Dhana Rao, and Vijay V. Raghavan expand these ideas to pro-vide a uni� ed perspective on modern big data system architectural choices. The authors also provide a glossary of modern database management terms and concepts, and include a very useful checklist of questions to be answered when selecting a big data system design.

In their forward-looking arti-cle “Cognitive Storage for Big Data,” Giovanni Cherubini, Jens Jelitto, and Vinodh Venkatesan propose the notion of a “cognitive data storage” system. Unlike traditional data stor-age systems, a cognitive storage sys-tem is attuned to metrics like data value, popularity, and obsolescence. To do this, a learning algorithm clas-si� es data into di� erent relevance classes and works in concert with a multitiered storage architecture to customize data placement. This proposal has implications for both

Naren Ramakrishnan, Virginia Tech

Ravi Kumar, Google

As data permeates all disciplines, the role

of big data becomes increasingly critical.

This special theme issue’s articles examine

big data technology trends that impact

databases, algorithms, and applications.

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GUEST EDITORS’ INTRODUCTION

scientific and business applications of big data.

In “Nomadic Computing for Big Data Analytics,” Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S.V.N. Vish-wanathan, and Inderjit Dhillon abstract away the specifics of many machine-learning paradigms to define what they call a nomadic paradigm for big data analytics. They demonstrate how to organize parallel computing workloads more effectively than the prevalent MapReduce approach. This idea is illustrated using matrix com-pletion and topic modeling, two widely used machine-learning algorithms.

Finally, Asmaa Elbadrawy, Ago-ritsa Polyzou, Zhiyun Ren, Mackenzie Sweeney, George Karypis, and Huzefa Rangwala focus on big data analytics in the context of another technology that has been on its own hype cycle—namely, massive open online courses (MOOCs). In “Predicting Student Per-formance Using Personalized Analyt-ics,” they posit a means for predicting student retention, in-class assessment,

and grade outcomes. Their results come from both traditional and MOOC course offerings from the University of Minnesota and George Mason Univer-sity, and a Stanford University MOOC.

Technology sage Mark Weiser has said: “The most profound technologies are those that dis-

appear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” In the case of big data, it is straightforward to make the case that although the hype cycle is over, the technologies them-selves are no longer big news because they are widely deployed. It is not a stretch to say that to be successful today, every researcher must become a data scientist!

ABOUT THE AUTHORS

NAREN RAMAKRISHNAN is the Thomas L. Phillips Professor of Engineering at Virginia Tech and director of the university’s Discovery Analytics Center. His research interests include data analytics, recommender systems, and applied machine learning. Ramakrishnan received a PhD in computer science from Purdue University. He is a member of the IEEE Computer Society and ACM, and a big data and analytics area editor for Computer. Contact him at naren@cs.vt.edu.

RAVI KUMAR is a senior staff research scientist at Google. His research inter-ests include data mining, algorithms for massive data, and the theory of com-putation. Kumar received a PhD in computer science from Cornell University. He is a member of ACM and a big data and analytics area editor for Computer.

Selected CS articles and columns are also available for free at http://ComputingNow .computer.org.

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