Editorial
Intelligent big data processing

https://doi.org/10.1016/j.future.2014.02.003Get rights and content

Introduction

Nowadays, data comes from sensors, lab experiments, simulations, individual archives, enterprise and Internet in all scales and formats. This data flood has outpaced our capability to process, analyze, store and understand these datasets. Such rapid expansion is also accelerated by the dramatic increase in acceptance of social media and networking applications  [1]. Furthermore, it can be foreseen that Internet of things (IoT) applications  [2] will raise the scale of data to an unprecedented level. People and devices (from home coffee machines to cars, to buses, railway stations and airports) are all loosely connected. Trillions of such connected components will generate a huge data ocean, and valuable information must be discovered from the data to help improve quality of life and make our world a better place.

This special issue is in response to the increasing convergence among grid and cloud computing  [3] and big data intelligence; while different approaches exist, challenges and opportunities are numerous in this context. The research papers selected for this special issue represent recent progresses in the field, including works on big data architectures, big data processing systems, big data management, and big data applications and modeling, MapReduce optimization, resources allocation  [4], resource monitoring, energy-aware resource provisioning. All of these papers not only provide novel ideas and state-of-the-art techniques in the field, but also stimulate future research in the sustainable environment.

Section snippets

Architectures and systems

MapReduce is a popular programming paradigm for processing big data. It uses the master–worker model, which is widely used on distributed and loosely coupled systems such as clusters, to solve large problems with task parallelism. With the ubiquity of many-core architectures in recent years and foreseeable future, the many-core platform will be one of the main computing platforms to execute MapReduce programs. The paper by Xuan-Yi Lin and Yeh-Ching Chung entitled “Master–Worker Model for

Big data processing

Knowledge processing has found its successful applications in a very wide range: social network analysis, expert system, data mining, business process management, search engine, etc. The paper by Ziyuan Liu and Georg von Wichert entitled “A Generalizable Knowledge Framework for Semantic Indoor Mapping Based on Markov Logic Networks and Data Driven MCMC”  [7] proposes a generalizable knowledge framework for data abstraction, i.e. finding a compact abstract model for input data using predefined

Big data management

The ability of processing and analyzing big data plays a key role in most modern enterprises today. So, the technology of MapReduce which has the ability to automatically parallelize the application on a cluster of commodity hardware and can efficiently and quickly process terabytes and petabytes of data becomes popular to all sizes of enterprises. The paper by Lu Lu, Xuanhua Shi, Hai Jin, Qiuyue Wang, Daxing Yuan and Song Wu entitled “Morpho: a Decoupled MapReduce Framework for Elastic Cloud

Big data application

As the real-time requirement of data dissemination becomes increasingly significant in many fields, the emergency applications have received increasing attention. The publish/subscribe (pub/sub) paradigm is a key technology for asynchronous data dissemination that is widely used in the emergency applications. It decouples senders and receivers of the emergency applications in space, time, and synchronization, which enable a pub/sub system to seamlessly expand to massive size. The paper by

Conclusions

All of the above papers address either architectures and systems issues for big data processing or intelligent algorithms and optimization techniques or propose novel application models in the various cloud and big data fields. They also trigger further related research and technology improvements in the application of big data. Honorably, this special issue serves as a landmark source for education, information, and reference to professors, researchers and graduate students interested in

Ching-Hsien (Robert) Hsu is a professor in Department of Computer Science and Information Engineering at Chung Hua University, Taiwan, and distinguished chair professor in School of Computer and Communication Engineering at Tianjin University of Technology, China. His research includes high performance computing, cloud computing, parallel and distributed systems, ubiquitous/pervasive computing and intelligence. He has published 200 papers in refereed journals, conference proceedings and book

References (12)

There are more references available in the full text version of this article.

Cited by (0)

Ching-Hsien (Robert) Hsu is a professor in Department of Computer Science and Information Engineering at Chung Hua University, Taiwan, and distinguished chair professor in School of Computer and Communication Engineering at Tianjin University of Technology, China. His research includes high performance computing, cloud computing, parallel and distributed systems, ubiquitous/pervasive computing and intelligence. He has published 200 papers in refereed journals, conference proceedings and book chapters in these areas. He has been involved in more than 100 conferences and workshops as various chairs and more than 200 conferences/workshops as a program committee member. He is the editor-in-chief of international journal of Grid and High Performance Computing, and international journal of Big Data Intelligence, and serving as editorial board for around 20 international journals. He has been acting as an author/co-author or an editor/co-editor of 10 books from Springer, IGI Global, World Scientific and McGraw-Hill. He has also edited a number of special issues at top journals, such as IEEE Transactions on Cloud Computing, IEEE Transactions on Services Computing, Future Generation Computer Systems, Journal of Supercomputing, International Journal of Communication Systems, Automated Software Engineering, Journal of System Architecture, Concurrency and Computation: Practice and Experience, The Knowledge Engineering Review, Internet Research, Information System Frontiers, etc. He was awarded 5 times annual outstanding research award through 2005–2012 and a distinguished award in 2008 for excellence in research from Chung Hua University. He has been serving as executive committee of Taiwan Association of Cloud Computing (TACC) from 2008 to 2012, and executive committee of the IEEE Technical Committee of Scalable Computing (2008–2012). He is a member of Phi Tau Phi Scholastic honor society; IEEE senior member; regional director of the Future Technology Research Association (FTRA); and standing director of Taiwan Association of Cloud Computing (TACC).

View full text