ZTE

ZTE Communications ›› 2015, Vol. 13 ›› Issue (1): 50-59.DOI: 10.3969/j.issn.1673-5188.2015.01.007

• Review • Previous Articles     Next Articles

Big-Data Processing Techniques and Their Challenges in Transport Domain

Aftab Ahmed Chandio1,3, Nikos Tziritas1, Cheng-Zhong Xu1.2   

  1. 1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
    2. Department of Electrical and Computer Engineering, Wayne State University, MI 48202, USA;
    3. Institute of Mathematics and Computer Science, University of Sindh, Jamshoro 70680, Pakistan
  • Received:2015-01-26 Online:2015-03-25 Published:2015-03-25
  • About author:Aftab Ahmed Chandio (aftabac@siat.ac.cn) is a doctoral student at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. He is also a lecturer at the Institute of Mathematics and Computer Science, University of Sindh, Pakistan. His research interests include cloud computing, big data, parallel and distributed systems, scheduling, energy optimization, workload characterization, and mapmatching strategies for GPS trajectories.
    Nikos Tziritas (nikolaos@siat.ac.cn) received his PhD degree from the University of Thessaly, Greece, in 2011. He is currently a postdoctoral researcher at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. He researches scheduling, load-balancing and replication in CDNs as well as energy optimization and resource management in WSNs and cloud computing systems.
    Cheng-Zhong Xu (cz.xu@siat.ac.cn) received his PhD degree from the University of Hong Kong in 1993. He is currently a tenured professor at Wayne State University and director of the Institute of Advanced Computing and Data Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. His research interests include parallel and distributed systems and cloud computing. He has published more than 200 papers in journals and conference proceedings. He was nominated for Best Paper at 2013 IEEE High Performance Computer Architecture (HPCA) and 2013 ACM High Performance Distributed Computing (HPDC). He serves on a number of journal editorial boards, including IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cloud Computing, Journal of Parallel and Distributed Computing and China Science Information Sciences. He was a recipient of the Faculty Research Award, Career Development Chair Award, and the President’s Award for Excellence in Teaching of WSU. He was also a recipient of the“Outstanding Overseas Scholar” award of NSFC.
  • Supported by:
    This work was supported in part by the National Basic Research Program (973 Program, No.2015CB352400) , NSFC under grant U1401258 and U.S NSF under grant CCF-1016966.

Big-Data Processing Techniques and Their Challenges in Transport Domain

Aftab Ahmed Chandio1,3, Nikos Tziritas1, Cheng-Zhong Xu1.2   

  1. 1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
    2. Department of Electrical and Computer Engineering, Wayne State University, MI 48202, USA;
    3. Institute of Mathematics and Computer Science, University of Sindh, Jamshoro 70680, Pakistan
  • 作者简介:Aftab Ahmed Chandio (aftabac@siat.ac.cn) is a doctoral student at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. He is also a lecturer at the Institute of Mathematics and Computer Science, University of Sindh, Pakistan. His research interests include cloud computing, big data, parallel and distributed systems, scheduling, energy optimization, workload characterization, and mapmatching strategies for GPS trajectories.
    Nikos Tziritas (nikolaos@siat.ac.cn) received his PhD degree from the University of Thessaly, Greece, in 2011. He is currently a postdoctoral researcher at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. He researches scheduling, load-balancing and replication in CDNs as well as energy optimization and resource management in WSNs and cloud computing systems.
    Cheng-Zhong Xu (cz.xu@siat.ac.cn) received his PhD degree from the University of Hong Kong in 1993. He is currently a tenured professor at Wayne State University and director of the Institute of Advanced Computing and Data Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. His research interests include parallel and distributed systems and cloud computing. He has published more than 200 papers in journals and conference proceedings. He was nominated for Best Paper at 2013 IEEE High Performance Computer Architecture (HPCA) and 2013 ACM High Performance Distributed Computing (HPDC). He serves on a number of journal editorial boards, including IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cloud Computing, Journal of Parallel and Distributed Computing and China Science Information Sciences. He was a recipient of the Faculty Research Award, Career Development Chair Award, and the President’s Award for Excellence in Teaching of WSU. He was also a recipient of the“Outstanding Overseas Scholar” award of NSFC.
  • 基金资助:
    This work was supported in part by the National Basic Research Program (973 Program, No.2015CB352400) , NSFC under grant U1401258 and U.S NSF under grant CCF-1016966.

PDF

Abstract: This paper describes the fundamentals of cloud computing and current big-data key technologies. We categorize big-data processing as batch-based, stream-based, graph-based, DAG-based, interactive-based, or visual-based according to the processing technique. We highlight the strengths and weaknesses of various big-data cloud processing techniques in order to help the big-data community select the appropriate processing technique. We also provide big data research challenges and future directions in aspect to transportation management systems.

Key words: big-data, cloud computing, transportation management systems, MapReduce, bulk synchronous parallel

摘要： This paper describes the fundamentals of cloud computing and current big-data key technologies. We categorize big-data processing as batch-based, stream-based, graph-based, DAG-based, interactive-based, or visual-based according to the processing technique. We highlight the strengths and weaknesses of various big-data cloud processing techniques in order to help the big-data community select the appropriate processing technique. We also provide big data research challenges and future directions in aspect to transportation management systems.

关键词: big-data, cloud computing, transportation management systems, MapReduce, bulk synchronous parallel