[Cfp] Fwd: [CSPlus] International Journal of Big Data Intelligence, Summary of 2014 articles

-------- Weitergeleitete Nachricht -------- Betreff: [CSPlus] International Journal of Big Data Intelligence, Summary of 2014 articles Datum: Thu, 22 Jan 2015 18:56:48 +0800 Von: cfp@grid.chu.edu.tw An: neumann@wu-wien.ac.at

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International Journal of Big Data Intelligence ISSN: 2053-1397 (online); 2053-1389 (print) http://www.inderscience.com/ijbdi

Dear Distinguished Colleagues,

The International Journal of Big Data Intelligence (IJBDI) delighted to provide summary of articles that published in 2014. We would like to invite you to read the articles.

================================================================================================== Vol. 1, No. 1/2 pp. 3-17: Big data (lost) in the cloud pp. 18-35: Designing and implementing a cloud-hosted SaaS for data movement and sharing with SlapOS pp. 36-49: Multi-source streaming-based data accesses for MapReduce systems pp. 50-64: A new approach for accurate distributed cluster analysis for Big Data: competitive K-Means pp. 65-73: Peculiarities of numerical algorithms parallel implementation for exa-flops multicomputers pp. 74-88: Towards quality-of-service driven consistency for Big Data management pp. 89-102: D-CEP4CMA: a dynamic architecture for cloud performance monitoring and analysis via complex event processing pp. 103-113: An extended analytical study of Arabic sentiments pp. 114-126: Health big data analytics: current perspectives, challenges and potential solutions Vol. 1, No. 3 pp. 127-140: Migrating enterprise applications to the cloud: methodology and evaluation pp. 141-150: A parallel tag affinity computation for social tagging systems using MapReduce pp. 151-165: Innesto: a multi-attribute searchable consistent key/value store pp. 166-171: Anomaly digging approach based on massive RFID data in transportation logistics pp. 172-180: Current trends in predictive analytics of big data Vol. 1, No. 4 pp. 181-191: Intelligent big data analysis: a review pp. 192-204: Cloud computing for brain segmentation - a perspective from the technology and evaluations pp. 205-214: Provenance for business events PP. 215-229: Should infrastructure clouds be priced entirely on performance? An EC2 case study PP. 230-243: Total exchange routing on hierarchical dual-nets ==================================================================================================

pp. 3-17: Title: Big data (lost) in the cloud

Author: Beniamino Di Martino; Rocco Aversa; Giuseppina Cretella; Antonio Esposito; Joanna Kołodziej

Abstract: The big data era poses a critically difficult challenge and striking development opportunities to high performance computing (HPC). The major problem is an efficient transformation of the massive data of various types into valuable information and meaningful knowledge. Computationally-effective HPC is required in a fast-increasing number of data-intensive domains. With its special features of self-service and pay-as-you-use, cloud computing (CC) offers suitable abstractions to manage the complexity of the analysis of large data in various scientific and engineering domains. This paper surveys briefly the most recent developments on CC support for solving the big data problems. It presents a comprehensive critical analysis of the existing solutions and shows the further possible directions of the research in this domain including new generation multi-datacentre cloud architectures for the storage and management of the huge big data streams.

Keywords: distributed data centres; resource provisioning; big data workflow; cloud computing; high performance computing; MapReduce; Hadoop; data analysis; data curation; data storage; data management.

DOI: 10.1504/IJBDI.2014.063840 ------------------------------

pp. 18-35: Title: Designing and implementing a cloud-hosted SaaS for data movement and sharing with SlapOS

Author: Walid Saad; Heithem Abbes; Mohamed Jemni; Christophe Cérin

Abstract: For over a decade, the data requirements of e-Science applications increase drastically with the emergence of data-intensive applications. Several tools and frameworks have been developed to manage and handle the big amount of data for the grid platforms. However, the use of these tools by the basic scientist and the grid computing community is not well adopted because of the complexity of the installation and configuration processes. Recently, an open source distributed operating system for clouds emerged, namely SlapOS. The aim of SlapOS is to hide the complexity of IT infrastructures and software deployments from users. In this work, we propose a cloud-hosted data grid using the SlapOS cloud. Through a software as a service (SaaS) solution, users can request and install automatically any data movement and sharing tools like Stork and Bitdew without any intervention of a system administrator. The entire solution is now running in production into the SlapOS cloud ! at Paris 13 University. Intensive experiments have been conducted on the Grid '5000 testbed to validate our approach.

Keywords: data-intensive applications; big data management; software as a service; SaaS; Stork; Bitdew; SlapOS; grid tools; cloud federation; software integration; cloud computing; data movement; data sharing; open source; distributed operating system; grid computing.

DOI: 10.1504/IJBDI.2014.063860 ------------------------------ pp. 36-49: Title: Multi-source streaming-based data accesses for MapReduce systems

Author: Jiadong Wu; Bo Hong

Abstract: The MapReduce programming model, along with its open-source implementation - Hadoop - has provided a cost effective solution for many data intensive applications. Hadoop stores data distributively and exploits data locality by assigning tasks to where data is stored. In many cases, however, accessing remote data (rack-local and off-rack) is inevitable. In this paper we are evaluating the possibility of improving the remote data accessing performance by streaming data from multiple available replicas. The proposed design consists of a circular buffer, a slice reader and an enhanced DataNode. Such system is capable of adapting to both the static performance variance caused by network topology as well as dynamic variance caused by congestion. Extensive experiments show that multisource streaming can significantly improve the throughput of remote data access and accelerate the related map tasks by 10%-20%. For systems with heterogenous network links, upto 4× speedup w! as observed.

Keywords: MapReduce; multisource streaming; remote data access; network topology; congestion; big data.

DOI: 10.1504/IJBDI.2014.063843 ------------------------------ pp. 50-64: Title: A new approach for accurate distributed cluster analysis for Big Data: competitive K-Means

Author: Rui Máximo Esteves; Thomas Hacker; Chunming Rong

Abstract: The tremendous growth in data volumes has created a need for new tools and algorithms to quickly analyse large datasets. Cluster analysis techniques, such as K-Means can be distributed across several machines. The accuracy of K-Means depends on the selection of seed centroids during initialisation. K-Means++ improves on the K-Means seeder, but suffers from problems when it is applied to large datasets. In this paper, we describe a new algorithm and a MapReduce implementation we developed that addresses these problems. We compared the performance with three existing algorithms and found that our algorithm improves cluster analysis accuracy and decreases variance. Our results show that our new algorithm produced a speedup of 76 ± 9 times compared with the serial K-Means++ and is as fast as the streaming K-Means. Our work provides a method to select a good initial seeding in less time, facilitating fast accurate cluster analysis over large datasets.

Keywords: k-means clustering; k-means++; streaming k-means; MapReduce; distributed cluster analysis; big data; initial seeding.

DOI: 10.1504/IJBDI.2014.063844 ------------------------------ pp. 65-73: Title: Peculiarities of numerical algorithms parallel implementation for exa-flops multicomputers

Author: Victor E. Malyshkin

Abstract: Peculiarities of numerical algorithms parallel implementation for exa-flops multicomputers were considered and the appropriate examples were given. The problems of such big data processing were analysed and also, a solution was suggested. Problems of parallel implementation of large scale numerical models on a rectangular mesh were demonstrated by the parallelisation of the particle-in-cell method (PIC). For similar problems solution, the fragmentation of big data and computations were suggested. Fragmentation automatically provides different useful properties of a target program including dynamic load balancing on the basis of fragments migration from overloaded into underloaded processor elements of a multicomputer.

Keywords: numerical algorithms; big data processing; parallel programming; large scale numerical models; exa-flops computation; fragmentation; fragmented programs; dynamic load balancing; processes migration; modelling; particle-in-cell; PIC; multicomputers.

DOI: 10.1504/IJBDI.2014.063837 ------------------------------ pp. 74-88: Title: Towards quality-of-service driven consistency for Big Data management

Author: Álvaro García-Recuero; Sérgio Esteves; Luís Veiga

Abstract: With the advent of Cloud Computing, Big Data management has become a fundamental challenge during the deployment and operation of distributed highly available and fault-tolerant storage systems such as the HBase extensible record-store. These systems can provide support for geo-replication, which comes with the issue of data consistency among distributed sites. In order to offer a best-in-class service to applications, one wants to maximise performance while minimising latency. In terms of data replication, that means incurring in as low latency as possible when moving data between distant data centres. Traditional consistency models introduce a significant problem for systems architects, which is specially important to note in cases where large amounts of data need to be replicated across wide-area networks. In such scenarios it might be suitable to use eventual consistency, and even though not always convenient, latency can be partly reduced and traded for consis! tency guarantees so that data-transfers do not impact performance. In contrast, this work proposes a broader range of data semantics for consistency while prioritising data at the cost of putting a minimum latency overhead on the rest of non-critical updates. Finally, we show how these semantics can help in finding an optimal data replication strategy for achieving just the required level of data consistency under low latency and a more efficient network bandwidth utilisation.

Keywords: cloud storage; data consistency; replication; geo-replication; data storage; NoSQL; quality-of-service; QoS; big data management; data semantics; latency; network bandwidth.

DOI: 10.1504/IJBDI.2014.063853 ------------------------------ pp. 89-102: Title: D-CEP4CMA: a dynamic architecture for cloud performance monitoring and analysis via complex event processing

Author: Afef Mdhaffar; Riadh Ben Halima; Mohamed Jmaiel; Bernd Freisleben

Abstract: This paper presents a dynamic monitoring and analysis architecture for Cloud computing environments. It collects and analyses Cloud parameters to detect performance degradations. The proposed dynamic architecture, called D-CEP4CMA, is based on the methodology of complex event processing (CEP). It is designed to dynamically switch between different centralised and distributed CEP architectures depending on the load/memory of the CEP machine and network traffic conditions in the observed cloud environment. Experimental results demonstrate the efficiency of D-CEP4CMA and its performance in terms of precision and recall in comparison to centralised and distributed CEP architectures.

Keywords: cloud computing; complex event processing; CEP; performance analysis; dynamic architecture; big data intelligence; cloud performance; performance monitoring.

DOI: 10.1504/IJBDI.2014.063842 ------------------------------ pp. 103-113: Title: An extended analytical study of Arabic sentiments

Author: Nawaf A. Abdulla; Mahmoud Al-Ayyoub; Mohammed Naji Al-Kabi

Abstract: Due to the evolution of Web 2.0 technology, internet users are more capable of posting their comments and reviews to express their opinions and feelings about everything. Hence, the necessity of automatically identifying the polarity (be it positive, negative, or neutral) of these comments arose and new interdisciplinary field called sentiment analysis (SA) emerged. Unluckily, many studies were conducted on the English language whereas those on the Arabic language are quite few. In addition, the publicly available datasets and testing tools for SA of Arabic text are rare. In this paper, a relatively large dataset of Arabic comments is manually collected and annotated. The source is one of the most widely used social networks in the Arab world, Yahoo!-Maktoob. A comprehensive analysis of this dataset is presented and two popular classifiers, support vector machine (SVM) and Naive Bayes (NB) are used for empirical experimentations. The results show that SVM outperfor! ms NB and achieves a 64% accuracy level.

Keywords: social networking; document-level sentiment analysis; Arabic text analysis; opinion mining; Arabic comments; support vector machine; SVM; naive Bayes.

DOI: 10.1504/IJBDI.2014.063845 ------------------------------ pp. 114-126: Title: Health big data analytics: current perspectives, challenges and potential solutions

Author: Mu-Hsing Kuo; Tony Sahama; Andre W. Kushniruk; Elizabeth M. Borycki; Daniel K. Grunwell

Abstract: Modern health information systems can generate several exabytes of patient data, the so called 'health big data', per year. Many health managers and experts believe that with the data, it is possible to easily discover useful knowledge to improve health policies, increase patient safety and eliminate redundancies and unnecessary costs. The objective of this paper is to discuss the characteristics of health big data as well as the challenges and solutions for health big data analytics (BDA) - the process of extracting knowledge from sets of health big data - and to design and evaluate a pipelined framework for use as a guideline/reference in health BDA.

Keywords: healthcare technology; big data analytics; BDA; data mining; cloud computing; health information systems; patient data; health big data.

DOI: 10.1504/IJBDI.2014.063835 ------------------------------ pp. 127-140: Title: Migrating enterprise applications to the cloud: methodology and evaluation

Author: Steve Strauch; Vasilios Andrikopoulos; Dimka Karastoyanova; Frank Leymann; Nikolay Nachev; Albrecht Stäbler

Abstract: Migrating existing on-premise applications to the cloud is a complex and multi-dimensional task and may require adapting the applications themselves significantly. For example, when considering the migration of the database layer of an application, which provides data persistence and manipulation capabilities, it is necessary to address aspects like differences in the granularity of interactions and data confidentiality, and to enable the interaction of the application with remote data sources. In this work, we present a methodology for application migration to the cloud that takes these aspects into account. In addition, we also introduce a tool for decision support, application refactoring and data migration that assists application developers in realising this methodology. We evaluate the proposed methodology and enabling tool using a case study in collaboration with an IT enterprise.

Keywords: data migration; application migration; decision support; database layer; application refactoring; cloud computing; interaction granularity; data confidentiality; remote data sources.

DOI: 10.1504/IJBDI.2014.066319 ------------------------------ pp. 141-150: Title: A parallel tag affinity computation for social tagging systems using MapReduce

Author: Hyunwoo Kim; Taewhi Lee; Hyoung-Joo Kim

Abstract: Tag affinity is the relationship between tags. It is a useful information for search and recommendation in social tagging systems. Tag affinity is measured by several types of tag cooccurrence frequency. The computation of tag affinity is a time-consuming task as the tagging information is accumulated. To alleviate this problem, we propose a parallel tag affinity computation method using MapReduce. We present MapReduce algorithms for computing three types of tag affinity measures: macro, micro, and bigram tag cooccurrence frequency. Our experimental results show that the proposed MapReduce-based approach not only significantly outperforms existing methods based on a relational database but also provides high scalability. To the best of our knowledge, this approach is the first tag affinity computation on MapReduce.

Keywords: parallelisation; social tagging; MapReduce; Hadoop; parallel tag affinity; tag cooccurrence frequency; bigram; big data.

DOI: 10.1504/IJBDI.2014.066322 ------------------------------ pp. 151-165: Title: Innesto: a multi-attribute searchable consistent key/value store

Author: Mahdi Tayarani Najaran; Norman C. Hutchinson

Abstract: Key/value data storage systems serve as the fundamental component of scalable cloud-based services. However, the scalability of existing key/value datastores comes at the cost of a narrow data access API with relaxed data consistency. We present Innesto, a distributed key/value datastore that provides search as part of its API. Search allows data items to be retrieved based on constraints on multiple different attributes. Innesto's strong consistency data model and its transactional interface bring much of the power of traditional relational databases to cloud-scale performance. Isolation between transactions can be performed using either traditional locks or using lock-free synchronisation based on clock vectors. Our evaluation of Innesto shows that it offers these extra features with competitive performance compared to an industrial key/value datastore such as Cassandra which offers an inferior feature set.

Keywords: key/value stores; cloud storage; multiattribute search; consistency; scalability; one-round transaction; big data; cloud computing; data storage.

DOI: 10.1504/IJBDI.2014.066323 ------------------------------ pp. 166-171: Title: Anomaly digging approach based on massive RFID data in transportation logistics

Author: Xiaohua Cao; Xiejun Zhang

Abstract: In modern transportation logistics, anomaly significantly lowers the efficiency of production and the quality of service. Massive RFID data is produced to record the states of materials in transportation logistics. The data is of multi-attribute, randomness and various dimensions so that it is difficult to find out anomalies from these data. A deviation-based clustering approach is proposed to dig anomalies. Firstly, the features of RFID data are discussed from multi-attribute perspectives including time, location, data, sequence and path. Next, against the randomness and various dimensions of state data, a clustering approach is presented to unify the dimensions of state data and dig anomalies from random state data. The results show that the proposed approach can efficiently find more than 91.2% of anomalies among transportation logistics.

Keywords: anomaly digging; massive RFID data; radio frequency identification; deviation models; clustering; transport logistics; big data.

DOI: 10.1504/IJBDI.2014.066324 ------------------------------ pp. 172-180: Title: Current trends in predictive analytics of big data

Author: Tomasz Wiktor Wlodarczyk; Thomas J. Hacker

Abstract: Predictive analytics is a driving force motivating considerable interest in big data. Although there is clear interest in big data, the adoption rate of analytical techniques fuelled by big data that can extract knowledge and value from these data is less well understood. In this paper, we present a quantitative analysis of trends in publications related to predictive analytics, predictive modelling, big data and data intensive computing. Our evaluation shows an increasing popularity of big data in scientific publications, with ten-fold increase in the last three years. Concomitantly, we find that predictive analytics are connected with this trend, with two-fold increase in the last three years, but also a seven-fold increase in the same period when used in context with big data. We also classify the main application domains for big data and predictive analytics. Contrary to popular belief that big data is focused primarily on social media and business intelligence! , our analysis found that almost half of scientific publications using predictive analytics were in healthcare, smart services, the internet of things, and weather and environment. Our results indicate the early adoption of big data-based analytics in these domains.

Keywords: big data; data intensive computing; predictive analytics; predictive modelling; elemental data; time series.

DOI: 10.1504/IJBDI.2014.066326 ------------------------------ pp. 181-191: Title: Intelligent big data analysis: a review

Author: Chun-Wei Tsai; Ya-Lan Yang; Ming-Chao Chiang; Chu-Sing Yang

Abstract: Big data analysis is definitely an urgent task for most information systems. Its importance and potentials can be found in many recent studies. They buzzed with this research issue because the data we collect and create are increasing at an unprecedented rate. Thus, the data analysis process has to be reconsidered. In this paper, we will first give a brief discussion of big data from different perspectives, such as size of data, characteristics of data, and source of data. Then, data mining and other information retrieval technologies for big data will be addressed. A brief review of other computational intelligence technologies for big data will also be given. Finally, open issues and future research trends using computational intelligence technologies are presented to show their potentials for big data.

Keywords: big data analysis; computational intelligence; data mining; information retrieval; soft computing; intelligent data analysis; information systems.

DOI: 10.1504/IJBDI.2014.066957 ------------------------------ pp. 192-204: Title: Cloud computing for brain segmentation - a perspective from the technology and evaluations

Author: Victor Chang

Abstract: This paper examines how cloud computing can be used in the area of brain segmentation with regard to satisfactory technical and user evaluations. It explains eleven APIs associated with each brain segment and the process of capturing data for each segment. Functionality and experiments associated with each API are discussed. Dancing is to capture data more easily. Results are used to explain why some segments are more active in dancing, with two evaluations undertaken. The first evaluation is the use of brain segmentations developed for medical cloud computing education (MCCE) and results confirm that cloud computing offers a 20% improvement in learning satisfaction. The second evaluation is focused on recapturing a lost skill. Results confirm that volunteers have their heartbeat, blood pressure, emotion, body coordination and vision at their peak. Benefits of using cloud brain segmentation technology are presented to illustrate positive impacts to healthcare infor! matics, education and cost reduction.

Keywords: healthcare cloud; brain segmentation; cloud computing; medical cloud; computing education; dancing; learner satisfaction; heartbeat; blood pressure; emotions; body coordination; vision; healthcare informatics; healthcare education; cost reduction.

DOI: 10.1504/IJBDI.2014.066954 ------------------------------ pp. 205-214: Title: Provenance for business events

Author: Rafat Hammad; Ching-Seh Wu

Abstract: In today's business environment, applications generate massive amounts of business data at various levels of granularity. During execution of business processes, a number of issues may occur, e.g., system failures, process failures, service failures, or human errors, that can result in the processes not executing as expected, and as a result not adhering to the required compliance concerns. Business provenance is an emerging concept which gives the flexibility to capture information required to address a specific compliance or performance goal. This paper discusses the importance of data provenance and presents a framework to capture, model, and persists provenance for business events data. We propose a method to model the business events in such a way that can be used for continuous compliance monitoring and for historical root cause analysis. We present a design of our proposed framework and its components along with a prototype implementation.

Keywords: provenance management; data streams; real-time monitoring; distributed event-based systems; message dependence graph; root cause analysis; business data; business provenance; data provenance; compliance monitoring.

DOI: 10.1504/IJBDI.2014.066956 ------------------------------ pp. 215-229: Title: Should infrastructure clouds be priced entirely on performance? An EC2 case study

Author: John O'Loughlin; Lee Gillam

Abstract: The increasing number of public clouds, the large and varied range of VMs they offer, and the provider specific terminology used for describing performance characteristics, makes price/performance comparisons difficult. Large performance variation of identically priced instances can lead to clouds being described as 'unreliable' and 'unpredictable'. In this paper, we suggest that instances might be considered mispriced with respect to their deliverable performance - even when provider supplied performance ratings are taken into account. We demonstrate how CPU model determines instance performance, show associations between instance classes and sets of CPU models, and determine class-to-model performance characteristics. We show that pricing based on CPU models may significantly reduce, but not eliminate, price/performance variation. We further show that CPU model distribution differs across different AZs and so it may be possible to obtain better price/performance ! in some AZs by determining proportions of models found per AZ. However, the resources obtained in an AZ are account dependent, displays random variation and is subject to abrupt change.

Keywords: cloud computing; virtual machines; performance; pricing; probability; brokers; infrastructure clouds; public clouds; CPU models; price-performance variation.

DOI: 10.1504/IJBDI.2014.066955 ------------------------------ pp. 230-243: Title: Total exchange routing on hierarchical dual-nets

Author: Yamin Li; Wanming Chu

Abstract: The hierarchical dual-net (HDN) is a newly proposed interconnection network for building extra large scale supercomputers. The HDN is constructed based on a symmetric product graph (called base network), such as three-dimensional torus and n-dimensional hypercubes. A k-level hierarchical dual-net, HDN(B, k, S), is obtained by applying k-time dual constructions on the base network B. S defines a supernode set that adjusts the scale of the system. The node degree of HDN(B, k, S) is d0 + k, where d0 is the node degree of the base network. The HDN is node and edge symmetric and can contain huge number of nodes with small node-degree and short diameter. The total exchange, or all-to-all personalised communication, is one of the most dense communication patterns and is at the heart of numerous applications and programming models in parallel computing. In this paper, we show that the total exchange routing can be done on HDN efficiently and extra large scale HDNs can be i! mplemented easily.

Keywords: interconnection networks; total exchange routing; hierarchical dual nets; HDN; supercomputers; all-to-all personalised communication.

DOI: 10.1504/IJBDI.2014.066958 ------------------------------

=== About IJBDI ===

International Journal of Big Data Intelligence (IJBDI) is a peer reviewed journal publishing original and high-quality articles covering a wide range of topics in big data intelligence. The journal has a distinguished editorial board with extensive academic qualifications, ensuring high scientific standards.

=== Prospective Authors ===

The IJBDI invites you to consider submitting a manuscript for inclusion in this journal. Prospective authors are encouraged to submit an electronic version of original, unpublished manuscripts. Accepted papers of IJBDI will undergo language copyediting, typesetting, and reference validation in order to provide the highest publication quality. The average reviewing process is less than 10 weeks. No publication charges!

=== IJBDI Coverage ===

Topics of interest include:

-The 5Vs of the data landscape: volume, variety, velocity, veracity, value -Big data science and foundations, analytics, visualisation and semantics -Software and tools for big data management -Security, privacy and legal issues specific to big data -Big data economy, QoS and business models -Intelligence and scientific discovery -Software, hardware and algorithm co-design, high-performance computing -Large-scale recommendation systems and graph analysis -Algorithmic, experimental, prototyping and implementation -Data-driven innovation, computational modelling and data integration -Data intensive computing theorems and technologies -Modelling, simulation and performance evaluation -Hardware and infrastructure, green data centres/environmental-friendly perspectives -Computing, scheduling and resource management for sustainability -Complex applications in areas where massive data is generated

The journal welcomes comprehensive survey papers on timely topics.

=== Special Issue ===

Special Issue is an effective way to draw attention to specific topics. Experienced researchers and practitioners are welcome to propose, organize, and guest edit special section (3-4 papers) / issue (6-8 papers) around topics of their interest and expertise.

Once you propose a Special Issue (SI), you will be the Lead Guest Editor of the Special Issue. We look forward to your stimulating proposals and working with you in ensuring the SI bright.

We will be pleased to assist with all questions on the organization of a Special Issue to its publication. Enquiries and special issue proposals should be directed to the editor Prof. Robert Hsu at chh@chu.edu.tw

=== Editorial Board ===

Advisory Editors: Rajkumar Buyya (University of Melbourne) Wuchun Feng (Virginia Tech) Tarek El-Ghazawi (George Washington University) Sanjay Ranka (University of Florida) Geoffrey Fox (Indiana University) I-Ling Yen (University of Texas at Dallas) Kai Hwang (University of Southern California) Albert Zomaya (University of Sydney) Viktor Prasanna (University of Southern California) Philip S. Yu (University of Illinois at Chicago) Sartaj Sahni (University of Florida) Jeffrey Tsai (University of Illinois at Chicago)

Associate Editors: Jemal Abawajy (Deakin University, Australia) Nik Bessis (University of Derby, UK) Irena Bojanova (University of Maryland University College, USA) Yeh-Ching Chung (National Tsing Hua University, Taiwan) Ernesto Damiani (Università degli Studi di Milano, Italy) Thomas J. Hacker (Purdue University, USA) Marcin Paprzycki (Systems Research Institute, Poland)

Regional Editors: Pavan Balaji (Argonne National Laboratory, USA) Jinjun Chen (University of Technology, Sydney, Australia) Beniamino Di Martino (Seconda Universitá di Napoli, Italy) Bhekisipho Twala (University of Johannesburg, South Africa) Cho-Li Wang (The University of Hong Kong, Hong Kong SAR, China)

Editorial Board: Bernady Apduhan (Kyushu Sangyo University, Japan) Viraj Bhat (Yahoo, USA) Jian-Nong Cao (Hong Kong Polytechnic University, Hong Kong SAR, China) Christophe Cerin (University of Paris 13, France) Yuri Demchenko (University of Amsterdam, Netherlands) Bin Guo (Northwestern Polytechnical University, China) Hung-Chang Hsiao (National Cheng Kung University, Taiwan) Runhe Huang (Hosei University, Japan) Patrick Hung (University of Ontario Institute of Technology, Canada) Bahman Javadi (University of Western Sydney, Australia) Hai Jiang (Arkansas State University, USA) Hai Jin (Huazhong University of Science and Technology, China) Alex Mu-Hsing kuo (University of Victoria, Canada) Che-Rung Lee (National Tsing Hua University, Taiwan) Hui Lei (IBM T. J. Watson Research Center, USA) Victor Leung (The University of British Columbia, Canada) Keqin Li (State University of New York at New Paltz, USA) Keqiu Li (Dalian University of Technology, China) Qingwei Li (University of South Florida, USA) Xiaoming li (University of Delaware, USA) Chun-Yuan Lin (Chang Gung University, Taiwan) Shiyong Lu (Wayne State University, USA) Jianhua Ma (Hosei University, Japan) Prabhat K. Mahanti (University of New Brunswick, Canada) Victor Malyskin (Institute of Computational Mathematics and Mathematical Geophysics, RAS, Russian Federation) Onur Mutlu (Carnegie Mellon University, USA) Yonghong Peng (University of Bradford, UK) Pit Pichappan (Al-Imam Muhammad Ibn Saud University, Saudi Arabia) Seungmin Rho (Sungkyul University, Republic of Korea) Frode Eika Sandnes (Oslo and Akershus University College of Applied Sciences, Norway) Luis Veiga (Instituto Superior Técnico and INESC-ID Lisboa, Portugal) Monica Wachowicz (University of New Brunswick, Canada) Honggang Wang (University of Massachusetts Dartmouth, USA) Shangguang Wang (Beijing University of Posts and Telecommunications, China) Yufeng Wang (Nanjing University of Posts and Telecommunications, China) Tomasz Wiktor Wlodarczyk (University of Stavanger, Norway) Jinsong Wu (Alcatel-Lucent, China) Feng Xia (Dalian University of Technology, China) Yang Xiang (Deakin University, Australia) Chu-Sing Yang (National Cheng Kung University, Taiwan) Laurence T. Yang (St Francis Xavier University, Canada) Neil Y. Yen (The University of Aizu, Japan) Shui Yu (Deakin University, Australia) Zhiwen Yu (Northwestern Polytechnical University, China) Daqiang Zhang (Tongji University, China) Jia Zhang (Carnegie Mellon University, USA) Hong Zhu (Oxford Brookes University, UK)

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