-------- Weitergeleitete Nachricht --------
The contents of the latest issue of:
International Journal of Big Data Intelligence (IJBDI)
Vol. 3, No. 1, 2016
Published: Quarterly in Print and Electronically
ISSN online: 2053-1397; ISSN print: 2053-1389;
http://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. 3 No. 1 (2016)
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http://www.inderscience.com/info/inarticletoc.php?jcode=ijbdi&year=2016&vol=3&issue=1
Article #1
Title: Data behaviours model for Big Data visual analytics
Author: Jinson Zhang; Mao Lin Huang
Journal: Int. J. of Big Data Intelligence, 2016 Vol.3, No.1, pp.1 - 17
Abstract:
Big Data is composed of text, image, video, audio, mobile or other forms of data collected from multiple datasets, and is rapidly growing in size and complexity. It has created a huge volume of multidimensional data within a very short time period. This raises several new challenges, including; how to classify Big Data for multiple datasets, how to analyse Big Data for different forms of data, and how to visualise Big Data without the loss of information. In this paper, we extended our 5Ws density methods to Big Data behaviours analysis and visualisation. Our approach classifies Big Data into the 5Ws dimensions based on the data behaviours, and then further creates the 5Ws densities to measure Big Data patterns across multiple datasets for any form of data. We also establish non-dimensional data axes as additional parallel axes for Big Data visualisation. The experimental results have shown that the proposed new model has significantly improved the accuracy of Big Data visual
isation, and has large potential benefits and applications.
Article #2
Title: Learning-based text classifiers using the Mahalanobis distance for correlated datasets
Author: Noopur Srivastava; Shrisha Rao
Journal: Int. J. of Big Data Intelligence, 2016 Vol.3, No.1, pp.18 - 27
Abstract:
We present a novel approach to text categorisation with the aid of the Mahalanobis distance measure for classification. For correlated datasets, classification using the Euclidean distance is not very accurate. The use of the Mahalanobis distance exploits the correlation in data for the purpose of classification. For achieving this on large datasets, an unsupervised dimensionality reduction technique, principal component analysis (PCA) is used prior to classification using the k-nearest neighbours (kNN) classifier. As kNN does not work well for high-dimensional data, and moreover computing correlations for huge and sparse data is inefficient, we use PCA to obtain a reduced dataset for the training phase. Experimental results show improvement in classification accuracy and a significant reduction in error percentage by using the proposed algorithm on huge datasets, in comparison with classifiers using the Euclidean distance.
Article #3
Title: Computer network traffic prediction: a comparison between traditional and deep learning neural networks
Author: Tiago Prado Oliveira; Jamil Salem Barbar; Alexsandro Santos Soares
Journal: Int. J. of Big Data Intelligence, 2016 Vol.3, No.1, pp.28 - 37
Abstract:
This paper compares four different artificial neural network approaches for computer network traffic forecast, such as: 1) multilayer perceptron (MLP) using the backpropagation as training algorithm; 2) MLP with resilient backpropagation (Rprop); (3) recurrent neural network (RNN); 4) deep learning stacked autoencoder (SAE). The computer network traffic is sampled from the traffic of the network devices that are connected to the internet. It is shown herein how a simpler neural network model, such as the RNN and MLP, can work even better than a more complex model, such as the SAE. Internet traffic prediction is an important task for many applications, such as adaptive applications, congestion control, admission control, anomaly detection and bandwidth allocation. In addition, efficient methods of resource management, such as the bandwidth, can be used to gain performance and reduce costs, improving the quality of service (QoS). The popularity of the newest deep learning metho
ds have been increasing in several areas, but there is a lack of studies concerning time series prediction, such as internet traffic.
Article #4
Title: Automated validation of structured large databases: an illustration of material code bulk validation
Author: Ravindra Patankar; Sandeep Dulluri
Journal: Int. J. of Big Data Intelligence, 2016 Vol.3, No.1, pp.38 - 50
Abstract:
The accumulation of data and henceforth the storage is growing at an exponential phase owing to the decrease in the memory costs and increasingly complex business processes. With the increased data, typically there would be an increase in the complexity of validating the data. Often, the complexity and effort in validation of large scale databases grows nonlinearly with the increase in database size (Lee et al., 1999). In this paper, we discuss a novel methodology for bulk validation of large scale structured databases. The approach we propose is generic and has been tested in a real time environment. We present an illustration of validation on a material codes validation problem faced by a Fortune 100 enterprise. The demonstration would highlight the heterogeneity, and scale-scope of data validation related problems and henceforth tackling these problems effectively via application of machine learning techniques on Big Data.
Article #5
Title: Corporate governance fraud detection from annual reports using big data analytics
Author: G. Sudha Sadasivam; Mutyala Subrahmanyam; Dasaraju Himachalam; Bhanu Prasad Pinnamaneni; S. Maha Lakshme
Journal: Int. J. of Big Data Intelligence, 2016 Vol.3, No.1, pp.51 - 60
Abstract:
Financial reports of corporations publicise their performance. This in turn motivates manipulation of financial statements. Falsification of financial statements over prolonged period results in sudden collapse of multi-national companies, long-term economic loss to government and loss of trust of public. Detecting management frauds using normal audit procedures is time expensive as huge volume of data needs to be analysed. Hence additional analytical procedures should be used. The proposed work aims at automated analysis of annual reports using MapReduce paradigm to identify fraudulent companies. Annual reports of companies from public repositories are parsed to extract features for preparing a score card. Principal component analysis is applied on the score card to extract the principal features to train support vector machine. Experimental results show that 90% accuracy can be achieved using 10% to 25% of the principal features. Using MapReduce paradigm for feature extract
ion and classification improves the time efficiency by 85%.
Article #6
Title: Privacy models for big data: a survey
Author: Nancy Victor; Daphne Lopez; Jemal H. Abawajy
Journal: Int. J. of Big Data Intelligence, 2016 Vol.3, No.1, pp.61 - 75
Abstract:
Big data is the next big thing in computing. As this data cannot be processed using traditional systems, it poses numerous challenges to the research community. Privacy is one of the important concerns with data, be it traditional data or big data. This paper gives an overview of big data, the challenges with big data and the privacy preserving data sharing and publishing scenario. We focus on the various privacy models that can be extended to big data domain. A description of each privacy model with its benefits and drawbacks is discussed in the review. This survey will contribute much to the benefit of researchers and industry players in uncovering the critical areas of big data privacy.
=== 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. IJBDI provides a rapid forum for the dissemination of original research as well as review/survey articles. 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@chu.edu.tw
=== Archive Articles ===
Vol. 1, No. 1 (2014)
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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
Vol. 1, No. 2 (2014)
***************
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 (2014)
***************
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 (2014)
***************
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
Vol. 2, No. 1 (2015)
***************
pp. 2-8: Optimising virtual machine allocation in MapReduce cloud for improved data locality
pp. 9-22: An empirical experimentation towards predicting understandability of conceptual schemas using quality metric
pp. 23-36: Terms analytics service for CouchDB: a document-based NoSQL
PP. 37-44: Energy aware network scheduling for a data centre
PP. 45-62: MELA: elasticity analytics for cloud services
Vol. 2, No. 2 (2015)
***************
pp. 70-80: Heterogeneity-aware scheduler for stream processing frameworks
pp. 81-90: Robust fingerprinting codes for database using non-adaptive group testing
pp. 91-105: A semantic cloud infrastructure for data-intensive medical research
pp. 106-116: Why rank-level fusion? And what is the impact of image quality?
pp. 117-126: Malicious traffic analysis on mobile devices: a hardware solution
pp. 127-141: A platform for big data analytics on distributed scale-out storage system
Vol. 2, No. 3 (2015)
***************
pp. 145-156: A survey on big data processing infrastructure: evolving role of FPGA
pp. 157-167: Benchmarking multi-GPU communication using the shallow water equations
pp. 168-182: Unstructured data mining: use case for CouchDB
pp. 183-200: DLS: a cloud-hosted data caching and prefetching service for distributed metadata access
pp. 201-221: Classification and comparison of NoSQL big data models
Vol. 2, No. 4 (2015)
***************
pp. 223-235: Framework for handling personal data: analysis of buying information by questionnaire
pp. 236-249: A reference architecture for big data solutions - introducing a model to perform predictive analytics using big data technology
pp. 250-261: Big data analysis of swimming pools' impact on household electric intensity in San Antonio, Texas
pp. 262-284: Energy-aware service provisioning in volunteers clouds
pp. 285-302: A case-based reasoning approach for pattern detection in Malaysia rainfall data
Vol. 3, No. 1 (2016)
***************
pp. 1-17: Data behaviours model for Big Data visual analytics
pp. 18-27: Learning-based text classifiers using the Mahalanobis distance for correlated datasets
pp. 28-37: Computer network traffic prediction: a comparison between traditional and deep learning neural networks
pp. 38-50: Automated validation of structured large databases: an illustration of material code bulk validation
pp. 51-60: Corporate governance fraud detection from annual reports using big data analytics
pp. 61-75: Privacy models for big data: a survey
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=== IJBDI Editorial Board ===
Advisory Editors:
Rajkumar Buyya (University of Melbourne)
Wing-Kwong Chan (City University of Hong Kong)
Rong N. Chang (IBM Research, USA)
Hui Lei (IBM T. J. Watson Research Center)
Vijay Raghavan (University of Louisiana at Lafayette)
Sanjay Ranka (University of Florida)
Domenico Talia (Università della Calabria)
Jeffrey Tsai (University of Illinois at Chicago)
Hongji Yang (Bath Spa University)
Yuanyuan Yang (Stony Brook University)
Philip Yu (University of Illinois at Chicago)
Albert Zomaya (The University of Sydney)
Associate Editors:
James Abawajy (Deakin University)
Nik Bessis (Edge Hill University)
Christophe Cerin (University of Paris 13)
Thomas J. Hacker (Purdue University)
Hung-Chang Hsiao (National Cheng Kung University)
Patrick K. Hung (University of Ontario Institute of Technology)
Hai Jin (Huazhong University of Science and Technology)
Victor Leung (The University of British Columbia)
Keqin Li (State University of New York at New Paltz)
Grace Lin (III, Taiwan)
Marcin Paprzycki (Polish Academy of Sciences)
Marcello Trovati (University of Derby)
Yang Xiang (Deakin University)
Regional Editors:
Pavan Balaji (Argonne National Laboratory)
Jinjun Chen (University of technology Sydney)
Beniamino Di Martino (Second University of Naples)
Bhekisipho Twala (University of Johannesburg)
Cho-Li Wang (The University of Hong Kong)
Editorial Board:
Eyhab Al-Masri (University of Waterloo)
Amir H. Alavi (Michigan State University)
Bernady O. Apduhan (Kyushu Sangyo University)
Yuri Demchenko (University of Amsterdam )
Wei Hu (Nanjing University)
Jun Huang (Chongqing University of Posts and Telecommunications)
Runhe Huang (Hosei University)
Bahman Javadi (University of Western Sydney)
Chunxiao Jiang (Tsinghua University)
Hai Jiang (Arkansas State University)
Alex Mu-Hsing Kuo (University of Victoria)
Che-Rung Lee (National Tsing Hua University)
Keqiu Li (Dalian University of Technology)
PRABHAT MAHANTI (UNIVERSITY OF NEW BRUNSWICK)
Victor Malyskin (Russian Academy of Sciences)
Stelios Sotiriadis (University of Toronto)
Luis Veiga (Instituto Superior Técnico)
Monica Wachowicz (University of New Brunswick)
Honggang Wang (University of Massachusetts Dartmouth)
Shangguang Wang (Beijing University of Posts and Telecommunications)
Yufeng Wang (Nanjing University of Posts and Telecommunications)
Tomasz Wiktor Wlodarczyk (University of Stavanger)
Jinsong Wu (University de Chile)
Feng Xia (Dalian University of Technology)
Chu-Sing Yang (National Cheng Kung University)
Shui Yu (Deakin University)
Daqiang Zhang (Tongji University)
Hong Zhu (Oxford Brookes University)
Kind regards,
Robert Hsu,
Editor-in-Chief
International Journal of Big Data Intelligence
http://www.inderscience.com/ijbdi