[AISWorld] Abstract Announcement for International Journal of Big Data Intelligence (IJBDI) Vol. 2, No. 1 (2015)

[Ching-Hsien Hsu]

===  Apologies if you receive multiple copies of this message ===

The contents of the latest issue of:
International Journal of Big Data Intelligence (IJBDI)
Vol. 2, No. 1, 2015
Published: Quarterly in Print and Electronically
ISSN online: 2053-1397; ISSN print: 2053-1389;
Published by INDERSCIENCE Publishers
www.inderscience.com/ijbdi
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Dear Distinguished Colleagues,

The International Journal of Big Data Intelligence (IJBDI) delighted to announce the publication of the latest issue.  We would like to invite you to read the articles.  

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IJBDI Vol. 2 No. 1 (2015)
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http://www.inderscience.com/info/inarticletoc.php?jcode=ijbdi&year=2015&vol=2&issue=1

Special Issue on Big Data and Cloud Computing Challenges
Guest Editors: Professor Dr. V. Vijayakumar, Professor Dr. Rajkumar Buyya, Professor Dr. Jemal Abawajy and Professor Dr. Hamid Arabnia


Pages 2-8:
Paper Title: Optimising virtual machine allocation in MapReduce cloud for improved data locality
Authors: T.P. Shabeera; S.D. Madhu Kumar
Abstract: Big data is getting more attention in today's world. Although MapReduce is successful in processing big data, it has some performance bottlenecks when deployed in cloud. Data locality has an important role among them. The focus of this paper is on improving data locality in MapReduce cloud by allocating adjacent VMs, for executing MapReduce jobs. Good data locality reduces cross network traffic and hence results in high performance. When a user requests for a set of virtual machines (VMs), VMs are chosen based on their physical distance between other VMs. We propose a greedy algorithm for creating cluster of VMs. Greedy methods do not give an optimal solution. The second method for the allocation of VMs is via partitioning around medoids method. Partitioning around medoids method always find a local minimum. This allocation may not be globally optimised. We also present a dynamic programming approach which is guaranteed to find an optimal solution from the users' perspective.

Pages 9-22:
Paper Title: An empirical experimentation towards predicting understandability of conceptual schemas using quality metric
Authors: Naveen Dahiya; Vishal Bhatnagar; Manjeet Singh
Abstract: Data warehouse are used in organisations for efficient information delivery. The quality of a data warehouse is governed by the quality of conceptual, logical and physical data models. Conceptual model forms the base for design of logical/physical models. The conceptual model quality is assessed using quality metrics. The metrics for assessing the quality of conceptual schemas are based on size/structural complexity of schemas. Various statistical techniques show the existence of significant relationship between quality metrics and understanding time of conceptual models. In this paper, the authors analyse the ability of quality metrics in predicting the understandability of conceptual schemas using experimental empirical approach. Various statistical techniques are used for study and analysis. The results of empirical analysis show that few of the metrics are strong indicators for predicting the understandability of conceptual multidimensional models.

Pages 23-36:
Paper Title: Terms analytics service for CouchDB: a document-based NoSQL
Authors: The reality that the scientific, industry and research communities have to deal with is the potential of 'Big Data'. The high-dimensional data (in digitised format) at our disposal can create opportunities such as discovery of new knowledge, creation of new online communities, and improvement on product and services delivery. The challenge however is that there are only few research, architectural designs and tools that can aid data mining processes from NoSQL databases. By focusing on terms and topic mining, this work proposes a data analytics framework that enables knowledge discovery through information retrieval and filtering from document-based NoSQL (specifically, CouchDB). The tool is algorithmically built and tested based on two methodologies namely: the inference-based apriori and the Baum-Welch algorithm. Preliminary test results of the proposed tool are also discussed based on the accuracy of each proposed algorithm where the inference-based apriori model performs better.

Pages 37-44:
Paper Title: Energy aware network scheduling for a data centre
Authors: G.K. Karthikeyan; Prassanna Jayachandran; Neelanarayanan Venkataraman
Abstract: Modern data centre energy consumption accounts for a large amount of operational cost. The idea of data centre energy optimisation deals with the job distribution between computing servers depending upon the workload. This paper projects the job role of communication material in the data centre energy consumption and proposes a scheduling approach that has both network awareness and efficient optimisation of energy consumption. This scheduling approach stabilises the energy consumption of the data centre, traffic demands and individual job performance. The proposed method enhances the server consolidation (to efficiently turn on/off the servers based on the workload) and also the distribution of traffic patterns.

Pages 45-62:
Paper Title: MELA: elasticity analytics for cloud services
Authors: Daniel Moldovan; Georgiana Copil; Hong-Linh Truong; Schahram Dustdar
Abstract: While cloud computing has enabled applications to be designed as elastic cloud services, there is a lack of tools and techniques for monitoring and analysing their elasticity at multiple levels, from the service level to the underlying virtual infrastructure. In this paper, we focus on monitoring and evaluating elasticity of cloud services, crucial for supporting users and automatic elasticity controllers, to understand the services' behaviour, and to develop smarter mechanisms for controlling their elasticity. We define novel concepts, namely elasticity space for describing the elastic behaviour of cloud services, and elasticity pathway for characterising the service's evolution through the elasticity space. We introduce techniques for enriching monitoring information and determining the elasticity space and pathway. Based on the above, we introduce MELA, an elasticity analytics as a service, providing features for monitoring and analysing the elasticity of cloud services in multi-cloud environments. To illustrate our approach, we conduct several experiments on an elastic data-as-a-service for a cloud-based machine-to-machine (M2M) platform.


=== 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.

=== CALL FOR PAPERS ===

The IJBDI invites renowned researchers from various branches of the field to submit manuscripts for publication in the journal.
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. 

Note: There are no submission or publication fees for manuscripts submitted to the International Journal of Big Data Intelligence (IJBDI). All manuscripts are accepted based on a double-blind peer review editorial process.

=== 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 ===

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 at chu.edu.tw

=== Archive Articles (2014) ===

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

=== IJBDI 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