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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)
Kind regards,
Robert Hsu,
Editor-in-Chief
International Journal of Big Data Intelligence
http://www.inderscience.com/ijbdi
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