Networking for Big Data - IEEE Xplore

4 downloads 0 Views 248KB Size Report
work on big data falls in data mining, machine learning, and data analysis. However, these amazing top-level killer applica- tions would not be possible without ...
NETWORK_GUEST_EDIT-Yu.qxp_Layout 1 7/17/14 2:31 PM Page 4

GUEST EDITORIAL

Networking for Big Data

Shui Yu

B

Xiaodong Lin

ig data is an emerging hot research topic due to its pervasive application in human society, such as government, climate, finance, and science. Currently, most research work on big data falls in data mining, machine learning, and data analysis. However, these amazing top-level killer applications would not be possible without the underneath support of networking due to their extremely large volume and computing complexity, especially when real-time or near-real-time applications are demanded. To date, big data is still quite mysterious to various research communities; in particular, the networking perspective for big data is seldom tackled to the best of our knowledge. Many problems are yet to be addressed, such as optimal network topology for big data, parallel structures and algorithms for big data computing, information retrieval in big data, and network security and privacy issues in big data. The purpose of this Special Issue is to explore the possible important networking issues in big data, and present possible promising research directions of big data from the networking aspect. In response to the Call for Papers, we were pleased to see 69 submissions from 21 countries and areas all over the world, which was far more than we expected. The large number of submissions also reflects the importance of this research field. After consulting with the Editor-in-Chief, we painfully rejected many valuable articles, and squeezed the accepted papers in two separate issues. This issue includes eight articles in four categories. The first four articles fall in architecture and the general issue of big data. The first article, “Building a Network Highway for Big Data: Architecture and Challenges” by He et al., tackles the essential problems of a network highway for big data, from data source to data transportation, and to data center. The second article, “Big Data: Transforming the Design Philosophy of Future Internet” by Yin et al., presents the possible impact on the design of the future Internet from big data, and envisions the features of the future Internet. The third article, “Search in the Universe of Big Networks and Data” by Gelenbe and Abdelrahman, discusses the issue of searching information in an infinitely large space, and the related computing time and energy. The fourth article, “Spatial Big Data and Wireless Networks: Experiences, Applications, and Research Challenges” by Jardak et al., explores the possible applications of spatial big data in the emerging wireless networking field. The second category is made up of two articles about system design and support for big data applications. The fifth article, “Monitoring and Analyzing Big Traffic Data of a Large-Scale Cellular Network with Hadoop” by Liu et al., presents a Hadoop-based system built for big data processing, and the real-world evaluation that demonstrates the effectiveness of their system. The sixth article, “Toward Integrating Overlay and Physical Networks for Robust Parallel Processing Architecture” by Suto et al., points out the problems of overlay-based parallel processing architecture for big data applications, and proposes a novel method to address the problem.

4

Jelena Misic

The third category is about big data privacy protection from the network perspective. The seventh article, “Toward Efficient and Privacy-Preserving Computing in the Big Data Era” by Lu et al., explores the privacy issues in big data, and introduces a novel protocol to meet the needs of information mining in big data. The fourth category is about big data application in social networks. Last but not least, the eighth article, “CAP: Community Activity Prediction Based on Big Data Analysis” by Zhang et al., presents a novel community-centric framework for event prediction based on big data analysis. We would like to thank all the authors who submitted their research work to this Special Issue. We would also like to acknowledge the contribution of many experts in this field who have participated in the review process, and offered comments and suggestions to the authors to improve their work. In particular, we would like to express our sincere appreciation to the Editor-inChief, Professor Sherman Shen, for his constructive suggestions and timely guidance during the life cycle of this Special Issue. Finally, we hope our readers will enjoy reading the articles in this collection, and further explore these promising and mainly uncharted research fields.

Biographies SHUI YU [SM’13] ([email protected]) received his Ph.D. degree from Deakin University, Victoria, Australia, in 2004. He is currently a senior lecturer with the School of Information Technology, Deakin University. He has published more than 100 peer reviewed papers, including in top journals and top conferences, such as IEEE TPDS, IEEE TIFS, IEEE TFS, IEEE TMC, and IEEE INFOCOM. His research interests include networking theory, network security, and mathematical modeling. He serves on the editorial boards of IEEE Transactions on Parallel and Distributed Systems, IEEE Communications Surveys and Tutorials, and IEEE Access. He is a member of AAAS. XIAODONG LIN [SM’09] ([email protected]) received his Ph.D. degree in information engineering from Beijing University of Posts and Telecommunications, China, and his Ph.D. degree (with Outstanding Achievement in Graduate Studies Award) in electrical and computer engineering from the University of Waterloo, Canada. He is currently an associate professor with the Faculty of Business and Information Technology, University of Ontario Institute of Technology (UOIT), Canada. His research interests include wireless network security, applied cryptography, computer forensics, software security, and wireless networking and mobile computing. He has won Best Paper Awards at several conferences, including ICCCN 2009, BodyNets 2010, and ICC 2007. He was a recipient of the prestigious NSERC Canada Graduate Scholarships (CGS) Doctoral, and selected as a university nominee for the NSERC Doctoral Prize (Engineering and Computer Sciences category). JELENA MISIC [SM’08] ([email protected]) is a professor of computer science at Ryerson University, Toronto, Ontario, Canada. She has published three books, 25 book chapters, more than 100 papers in archival journals, and more than 140 papers at international conferences in the areas of wireless networks, in particular wireless personal area network and wireless sensor network protocols, performance evaluation, and security. She serves on the editorial boards of IEEE Transactions on Vehicular Technology, IEEE Network, Computer Networks, Ad Hoc Networks, and Wiley Security and Communication Networks.

IEEE Network • July/August 2014