-------- Weitergeleitete Nachricht -------- Betreff: Subject: International Journal of Big Data Intelligence (IJBDI): Vol. 4, No. 2, April 2017 Datum: Sat, 8 Apr 2017 01:15:31 +0800 (CST) Von: CSPlus-Admin cfp-admin@grid.chu.edu.tw Antwort an: CSPlus-Admin cfp-admin@grid.chu.edu.tw An: neumann@wu-wien.ac.at
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------------------------------------------------------- The contents of the latest issue of: International Journal of Big Data Intelligence (IJBDI) Vol. 4, No. 2, 2017 Published: Quarterly in Print and Electronically ISSN online: 2053-1397; ISSN print: 2053-1389;
http://www.inderscience.com/ijbdi -------------------------------------------------------
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.
=========================== IJBDI Vol. 4 No. 2 (2017) ===========================
http://www.inderscience.com/info/inarticletoc.php?jcode=ijbdi&year=2017&...
Article #1 Title: Towards a real-time big data analytics platform for health applications Author: Dillon Chrimes; Mu-Hsing Kuo; Belaid Moa; Wei Hu Journal: Int. J. of Big Data Intelligence, 2017 Vol.4, No.2, pp.61 - 80 Abstract: We established a framework construct to form a big data analytics (BDA) platform using real volumes of health big data. Existing high-performance computing (HPC) architecture was utilised with HBase (noSQL database) and Hadoop (HDFS). Generated noSQL database was emulated from metadata and inpatient profiles of Vancouver Island Health Authority's hospital system. Special adjustments of Hadoop's ecosystem and HBase with the addition of 'salt buckets' to ingest were required. Results revealed that HBase took a week's time to generate âŒ10 TB of data for one billion records via ingestion. Hadoop ingestion into HBase only took three seconds. Both simple and complex queries were less than two seconds, and all queries produced accurate patient data results. Data migration performance requirements of our BDA platform can significantly capture large volumes of data while reducing data retrieval times and its linkages to innovative processes and configurations that met patient data s ecurity/privacy standards are discussed. Keywords: big data analytics; BDA; data mining; healthcare technology; high performance computing; HPC; patient data; simulation; HBase; Hadoop; data retrieval; data security; data privacy; privacy protection; privacy preservation.
Article #2 Title: Security challenges in cloud computing: state-of-art Author: Kriti Bhushan; B.B. Gupta Journal: Int. J. of Big Data Intelligence, 2017 Vol.4, No.2, pp.81 - 107 Abstract: Cloud computing platform provides on-demand access of services, and these services can also be customised, which enables the customers to use these services as per their requirement and they also need to pay according to their uses. Recent advancements in the field of cloud computing and internet technologies have attracted the organisations to shift their businesses on cloud. However, moving data and applications to a third party services provider also raises critical concern for security and privacy. Availability of cloud services is one of the most important security issues which directly affect the business of the cloud service providers and also its customers. Distributed denial of service (DDoS) attack on cloud is the root cause that challenges the availability of cloud services. This paper presents taxonomy of security issues in cloud, taxonomy of DDoS attacks in cloud, and taxonomy of DDoS defence mechanisms in cloud environment. We have also discussed, analysed, and compared some well-known methods for DDoS attack defence in cloud on different parameters that will help the victim to understand the risk and also assists them to select the proper defence technique to protect their cloud system from DDoS attacks. Keywords: cloud computing; DDoS attacks; distributed DoS; denial of service; availability; cloud security; data security; taxonomy; DDoS defence mechanisms.
Article #3 Title: Graph-based semi-supervised classification on very high resolution remote sensing images Author: Yupeng Yan; Manu Sethi; Anand Rangarajan; Ranga Raju Vatsavai; Sanjay Ranka Journal: Int. J. of Big Data Intelligence, 2017 Vol.4, No.2, pp.108 - 122 Abstract: Classification of very high resolution (VHR) remote sensing imagery is a rapidly emerging discipline but faces several challenges owing to the huge scale of the pixel data involved, indiscernibility in the traditionally used features to represent various regions, and the lack of available ground truth data. This paper provides a framework which elegantly overcomes these hurdles by providing a novel semi-supervised learning approach which employs multiscale superpixel tessellation representations of VHR imagery. Superpixels are homogeneous and irregularly shaped regions which form the backbone of our approach and are used to derive novel features by learning a decision tree. Our semi-supervised learning approach works on a superpixel graph and seamlessly combines the large margin capability of a support vector machine (SVM) with a graph-based Laplacian label propagation approach to obtain a novel objective function. Further we also provide a self-contained and easily paralleli sable linear iterative optimisation approach based on the principle of majorisation-minimisation. We evaluate this approach on four different geographic settings with varying neighbourhood types and draw comparisons with the popular and widely used Gaussian multiple instance learning algorithm. Our results showcase several advantages in accuracy and efficiency, which coupled with the ease of model building and inherently parallelisable optimisation make our framework a great choice for deployment in large scale applications like global human settlement mapping and population distribution, and change detection. Keywords: image classification; high resolution images; remote sensing images; superpixel segmentation; image segmentation; ultrametric contour maps; majorisation-minimisation; support vector machines; SVM; graph Laplacian; semi-supervised learning; Gaussian multiple instance learning; label propagation; surrogate function.
Article #4 Title: Optimising the calculation of statistical functions Author: André Rodrigues; Carla Silva; Paulo Borges; Sérgio Silva; Inês Dutra Journal: Int. J. of Big Data Intelligence, 2017 Vol.4, No.1, pp.123 - 138 Abstract: Statistical data analysis methods are well-known for their difficulty in handling large number of instances or large number of parameters. In this paper, we study popular and well-known statistical functions, generally applied to data analysis, and assess their performance as implemented by SPSS, MATLAB, R and our own software, DataIP. We use medium to large datasets and show that DataIP outperforms SPSS, MATLAB and R by several orders of magnitude. We argue that the design and implementation of these functions need to be rethought to adapt to today's data challenges. Keywords: statistical data analysis; statistical functions; performance evaluation; SPSS; MATLAB; optimisation.
=== 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 about 10 weeks.
Note: There are no submission or publication fees for manuscripts submitted to the International Journal of Big Data Intelligence (IJBDI).
=== 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) *************** 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
Vol. 3, No. 2 (2016) *************** pp. 79-91 A framework for collective I/O style optimisations at staging I/O nodes pp. 92-110 Towards cost-effective and high-performance caching middleware for distributed systems pp. 111-121 Rainfall forecasting by relevant attributes using artificial neural networks - a comparative study pp. 122-141 Multi approach for real-time systems specification: case study of GPU parallel systems
Vol. 3, No. 3 (2016) *************** pp. 145-153 Auto-scale: automatic scaling of virtualised resources using neuro-fuzzy reinforcement learning approach pp. 154-162 Service provisioning of flexible advance reservation leases in IaaS clouds pp. 163-175 A trigger-based introspection approach for cloud incident handling pp. 176-181 Discovery of semantic associations in an RDF graph using bi-directional BFS on massively parallel hardware pp. 182-189 Secure multi-owner-based cloud computing scheme for big data pp. 190-201 Distortion-free fragile watermark for relational databases pp. 202-214 PQsel: combining privacy with quality of service in cloud service selection
Vol. 3, No. 4 (2016) *************** pp. 215-227 Generic processing of real-time physiological data in the cloud pp. 228-238 SmartRecruiter: a similarity-based team formation algorithm pp. 239-249 Multivariate adaptive community detection in Twitter pp. 250-256 An enhanced usersâ similarity computation utilising one-class collaborative filtering pp. 257-269 Implementing generic PaaS deployment API: repackaging and deploying applications on heterogeneous PaaS platforms pp. 270-278 Design for medical imaging services platform based on cloud computing pp. 279-287 Do app launch times impact their subsequent commercial success?
Vol. 4, No. 1 (2016) *************** pp. 3-22 PaloPro: a platform for knowledge extraction from big social data and the news pp. 23-35 Optimising column family for OLAP queries in HBase pp. 26-46 A knowledge-based integrated framework for increasing social management intelligence pp. 47-60 HCEm model and a comparative workload analysis of Hadoop cluster ====================================================================
=== 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: Jemal 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
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