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Publisher: Inderscience Publishers   (Total: 443 journals)

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Showing 1 - 200 of 443 Journals sorted alphabetically
African J. of Accounting, Auditing and Finance     Hybrid Journal   (Followers: 13)
African J. of Economic and Sustainable Development     Hybrid Journal   (Followers: 14)
Afro-Asian J. of Finance and Accounting     Hybrid Journal   (Followers: 8, SJR: 0.195, CiteScore: 0)
American J. of Finance and Accounting     Hybrid Journal   (Followers: 23)
Asian J. of Management Science and Applications     Hybrid Journal   (Followers: 4)
Atoms for Peace: an Intl. J.     Hybrid Journal   (Followers: 3)
Electronic Government, an Intl. J.     Hybrid Journal   (Followers: 17, SJR: 0.424, CiteScore: 1)
EuroMed J. of Management     Hybrid Journal  
European J. of Cross-Cultural Competence and Management     Hybrid Journal   (Followers: 7)
European J. of Industrial Engineering     Hybrid Journal   (Followers: 10, SJR: 0.595, CiteScore: 1)
European J. of Intl. Management     Hybrid Journal   (Followers: 2, SJR: 0.3, CiteScore: 1)
Global Business and Economics Review     Hybrid Journal   (Followers: 3, SJR: 0.154, CiteScore: 0)
Interdisciplinary Environmental Review     Hybrid Journal   (Followers: 3)
Intl. J. of Abrasive Technology     Hybrid Journal   (Followers: 2, SJR: 0.279, CiteScore: 0)
Intl. J. of Accounting and Finance     Hybrid Journal   (Followers: 18)
Intl. J. of Accounting, Auditing and Performance Evaluation     Hybrid Journal   (Followers: 16, SJR: 0.14, CiteScore: 0)
Intl. J. of Ad Hoc and Ubiquitous Computing     Hybrid Journal   (Followers: 8, SJR: 0.21, CiteScore: 1)
Intl. J. of Adaptive and Innovative Systems     Hybrid Journal   (Followers: 1)
Intl. J. of Additive and Subtractive Materials Manufacturing     Hybrid Journal   (Followers: 5)
Intl. J. of Advanced Intelligence Paradigms     Hybrid Journal   (Followers: 5, SJR: 0.144, CiteScore: 1)
Intl. J. of Advanced Mechatronic Systems     Hybrid Journal   (Followers: 3, SJR: 0.132, CiteScore: 0)
Intl. J. of Advanced Media and Communication     Hybrid Journal   (Followers: 26, SJR: 0.124, CiteScore: 0)
Intl. J. of Advanced Operations Management     Hybrid Journal   (Followers: 9, SJR: 0.163, CiteScore: 0)
Intl. J. of Aerodynamics     Hybrid Journal   (Followers: 32)
Intl. J. of Agent-Oriented Software Engineering     Hybrid Journal   (Followers: 3)
Intl. J. of Agile and Extreme Software Development     Hybrid Journal   (Followers: 5)
Intl. J. of Agile Systems and Management     Hybrid Journal   (Followers: 5, SJR: 0.878, CiteScore: 3)
Intl. J. of Agricultural Resources, Governance and Ecology     Hybrid Journal   (Followers: 2, SJR: 0.152, CiteScore: 0)
Intl. J. of Agriculture Innovation, Technology and Globalisation     Hybrid Journal  
Intl. J. of Alternative Propulsion     Hybrid Journal   (Followers: 14)
Intl. J. of Applied Cryptography     Hybrid Journal   (Followers: 9, SJR: 0.455, CiteScore: 3)
Intl. J. of Applied Decision Sciences     Hybrid Journal   (Followers: 1, SJR: 0.275, CiteScore: 1)
Intl. J. of Applied Management Science     Hybrid Journal   (Followers: 4, SJR: 0.229, CiteScore: 0)
Intl. J. of Applied Nonlinear Science     Hybrid Journal   (Followers: 1)
Intl. J. of Applied Pattern Recognition     Hybrid Journal   (Followers: 8)
Intl. J. of Applied Systemic Studies     Hybrid Journal   (SJR: 0.129, CiteScore: 0)
Intl. J. of Arab Culture, Management and Sustainable Development     Hybrid Journal   (Followers: 7)
Intl. J. of Artificial Intelligence and Soft Computing     Hybrid Journal   (Followers: 11)
Intl. J. of Arts and Technology     Hybrid Journal   (Followers: 6, SJR: 0.225, CiteScore: 1)
Intl. J. of Auditing Technology     Hybrid Journal   (Followers: 5)
Intl. J. of Automation and Control     Hybrid Journal   (Followers: 11, SJR: 0.189, CiteScore: 1)
Intl. J. of Automation and Logistics     Hybrid Journal   (Followers: 5)
Intl. J. of Automotive Composites     Hybrid Journal   (Followers: 5)
Intl. J. of Automotive Technology and Management     Hybrid Journal   (Followers: 6, SJR: 0.374, CiteScore: 1)
Intl. J. of Autonomic Computing     Hybrid Journal   (Followers: 2)
Intl. J. of Autonomous and Adaptive Communications Systems     Hybrid Journal   (Followers: 3, SJR: 0.128, CiteScore: 0)
Intl. J. of Aviation Management     Hybrid Journal   (Followers: 7)
Intl. J. of Banking, Accounting and Finance     Hybrid Journal   (Followers: 16, SJR: 0.137, CiteScore: 0)
Intl. J. of Behavioural Accounting and Finance     Hybrid Journal   (Followers: 11)
Intl. J. of Behavioural and Healthcare Research     Hybrid Journal   (Followers: 8)
Intl. J. of Bibliometrics in Business and Management     Hybrid Journal   (Followers: 2)
Intl. J. of Big Data Intelligence     Hybrid Journal   (Followers: 24)
Intl. J. of Bio-Inspired Computation     Hybrid Journal   (Followers: 1, SJR: 0.721, CiteScore: 4)
Intl. J. of Bioinformatics Research and Applications     Hybrid Journal   (Followers: 16, SJR: 0.157, CiteScore: 0)
Intl. J. of Biomechatronics and Biomedical Robotics     Hybrid Journal   (Followers: 4)
Intl. J. of Biomedical Engineering and Technology     Hybrid Journal   (Followers: 5, SJR: 0.205, CiteScore: 1)
Intl. J. of Biomedical Nanoscience and Nanotechnology     Hybrid Journal   (Followers: 7)
Intl. J. of Biometrics     Hybrid Journal   (Followers: 5, SJR: 0.155, CiteScore: 0)
Intl. J. of Biotechnology     Hybrid Journal   (Followers: 6, SJR: 0.269, CiteScore: 1)
Intl. J. of Blockchains and Cryptocurrencies     Hybrid Journal  
Intl. J. of Bonds and Derivatives     Hybrid Journal   (Followers: 1)
Intl. J. of Business and Data Analytics     Hybrid Journal  
Intl. J. of Business and Emerging Markets     Hybrid Journal   (Followers: 2)
Intl. J. of Business and Globalisation     Hybrid Journal   (Followers: 3, SJR: 0.263, CiteScore: 1)
Intl. J. of Business and Systems Research     Hybrid Journal   (Followers: 1, SJR: 0.104, CiteScore: 0)
Intl. J. of Business Competition and Growth     Hybrid Journal   (Followers: 5)
Intl. J. of Business Continuity and Risk Management     Hybrid Journal   (Followers: 15)
Intl. J. of Business Environment     Hybrid Journal   (Followers: 3)
Intl. J. of Business Excellence     Hybrid Journal   (Followers: 4, SJR: 0.274, CiteScore: 1)
Intl. J. of Business Forecasting and Marketing Intelligence     Hybrid Journal   (Followers: 6)
Intl. J. of Business Governance and Ethics     Hybrid Journal   (Followers: 6, SJR: 0.171, CiteScore: 0)
Intl. J. of Business Information Systems     Hybrid Journal   (Followers: 17, SJR: 0.266, CiteScore: 1)
Intl. J. of Business Innovation and Research     Hybrid Journal   (Followers: 11, SJR: 0.28, CiteScore: 1)
Intl. J. of Business Intelligence and Data Mining     Hybrid Journal   (Followers: 30, SJR: 0.249, CiteScore: 2)
Intl. J. of Business Intelligence and Systems Engineering     Hybrid Journal  
Intl. J. of Business Performance and Supply Chain Modelling     Hybrid Journal   (Followers: 18, SJR: 0.18, CiteScore: 0)
Intl. J. of Business Performance Management     Hybrid Journal   (Followers: 9, SJR: 0.197, CiteScore: 1)
Intl. J. of Business Process Integration and Management     Hybrid Journal   (Followers: 12, SJR: 0.149, CiteScore: 1)
Intl. J. of Chinese Culture and Management     Hybrid Journal   (Followers: 4)
Intl. J. of Circuits and Architecture Design     Hybrid Journal   (Followers: 6)
Intl. J. of Cloud Computing     Hybrid Journal   (Followers: 25)
Intl. J. of Cognitive Biometrics     Hybrid Journal   (Followers: 3)
Intl. J. of Cognitive Performance Support     Hybrid Journal   (Followers: 4)
Intl. J. of Collaborative Engineering     Hybrid Journal   (Followers: 1)
Intl. J. of Collaborative Enterprise     Hybrid Journal   (Followers: 1)
Intl. J. of Collaborative Intelligence     Hybrid Journal   (Followers: 3)
Intl. J. of Communication Networks and Distributed Systems     Hybrid Journal   (Followers: 7, SJR: 0.177, CiteScore: 1)
Intl. J. of Comparative Management     Hybrid Journal  
Intl. J. of Competitiveness     Hybrid Journal   (Followers: 3)
Intl. J. of Complexity in Applied Science and Technology     Hybrid Journal  
Intl. J. of Complexity in Leadership and Management     Hybrid Journal   (Followers: 28)
Intl. J. of Computational Biology and Drug Design     Hybrid Journal   (Followers: 1, SJR: 0.231, CiteScore: 1)
Intl. J. of Computational Complexity and Intelligent Algorithms     Hybrid Journal   (Followers: 2)
Intl. J. of Computational Economics and Econometrics     Hybrid Journal   (Followers: 5)
Intl. J. of Computational Intelligence in Bioinformatics and Systems Biology     Hybrid Journal   (Followers: 13)
Intl. J. of Computational Intelligence Studies     Hybrid Journal   (Followers: 3)
Intl. J. of Computational Materials Science and Surface Engineering     Hybrid Journal   (Followers: 6, SJR: 0.135, CiteScore: 0)
Intl. J. of Computational Science and Engineering     Hybrid Journal   (Followers: 2, SJR: 0.373, CiteScore: 1)
Intl. J. of Computational Systems Engineering     Hybrid Journal   (Followers: 1)
Intl. J. of Computational Vision and Robotics     Hybrid Journal   (Followers: 5, SJR: 0.129, CiteScore: 0)
Intl. J. of Computer Aided Engineering and Technology     Hybrid Journal   (Followers: 3, SJR: 0.131, CiteScore: 0)
Intl. J. of Computer Applications in Technology     Hybrid Journal   (Followers: 1, SJR: 0.225, CiteScore: 1)
Intl. J. of Computers in Healthcare     Hybrid Journal   (Followers: 3)
Intl. J. of Computing Science and Mathematics     Hybrid Journal   (Followers: 1, SJR: 0.299, CiteScore: 1)
Intl. J. of Continuing Engineering Education and Life-Long Learning     Hybrid Journal   (Followers: 5, SJR: 0.196, CiteScore: 0)
Intl. J. of Convergence Computing     Hybrid Journal   (Followers: 2)
Intl. J. of Corporate Governance     Hybrid Journal   (Followers: 5)
Intl. J. of Corporate Strategy and Social Responsibility     Hybrid Journal   (Followers: 6)
Intl. J. of Creative Computing     Hybrid Journal   (Followers: 1)
Intl. J. of Critical Accounting     Hybrid Journal   (Followers: 3)
Intl. J. of Critical Computer-Based Systems     Hybrid Journal   (Followers: 1, SJR: 0.127, CiteScore: 0)
Intl. J. of Critical Infrastructures     Hybrid Journal   (Followers: 2, SJR: 0.173, CiteScore: 1)
Intl. J. of Data Analysis Techniques and Strategies     Hybrid Journal   (Followers: 17, SJR: 0.23, CiteScore: 0)
Intl. J. of Data Mining and Bioinformatics     Hybrid Journal   (Followers: 19, SJR: 0.217, CiteScore: 1)
Intl. J. of Data Mining, Modelling and Management     Hybrid Journal   (Followers: 14, SJR: 0.209, CiteScore: 0)
Intl. J. of Data Science     Hybrid Journal   (Followers: 10)
Intl. J. of Decision Sciences, Risk and Management     Hybrid Journal   (Followers: 9)
Intl. J. of Decision Support Systems     Hybrid Journal   (Followers: 2)
Intl. J. of Design Engineering     Hybrid Journal   (Followers: 12)
Intl. J. of Digital Culture and Electronic Tourism     Hybrid Journal   (Followers: 6)
Intl. J. of Digital Enterprise Technology     Hybrid Journal   (Followers: 1)
Intl. J. of Digital Signals and Smart Systems     Hybrid Journal   (Followers: 2)
Intl. J. of Diplomacy and Economy     Hybrid Journal   (Followers: 6)
Intl. J. of Dynamical Systems and Differential Equations     Hybrid Journal   (Followers: 1, SJR: 0.184, CiteScore: 0)
Intl. J. of Earthquake and Impact Engineering     Hybrid Journal   (Followers: 4)
Intl. J. of Economic Policy in Emerging Economies     Hybrid Journal   (Followers: 4, SJR: 0.134, CiteScore: 1)
Intl. J. of Economics and Accounting     Hybrid Journal   (Followers: 1)
Intl. J. of Economics and Business Research     Hybrid Journal   (Followers: 5, SJR: 0.129, CiteScore: 0)
Intl. J. of Education Economics and Development     Hybrid Journal   (Followers: 5, SJR: 0.156, CiteScore: 0)
Intl. J. of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 10, SJR: 0.225, CiteScore: 1)
Intl. J. of Electronic Banking     Hybrid Journal   (Followers: 5)
Intl. J. of Electronic Business     Hybrid Journal   (Followers: 2, SJR: 0.24, CiteScore: 0)
Intl. J. of Electronic Customer Relationship Management     Hybrid Journal   (Followers: 3, SJR: 0.148, CiteScore: 0)
Intl. J. of Electronic Democracy     Hybrid Journal   (Followers: 2)
Intl. J. of Electronic Finance     Hybrid Journal   (Followers: 5, SJR: 0.155, CiteScore: 0)
Intl. J. of Electronic Governance     Hybrid Journal   (SJR: 0.142, CiteScore: 1)
Intl. J. of Electronic Healthcare     Hybrid Journal   (Followers: 2, SJR: 0.254, CiteScore: 1)
Intl. J. of Electronic Marketing and Retailing     Hybrid Journal   (Followers: 7, SJR: 0.249, CiteScore: 1)
Intl. J. of Electronic Security and Digital Forensics     Hybrid Journal   (Followers: 8, SJR: 0.137, CiteScore: 0)
Intl. J. of Electronic Transport     Hybrid Journal   (Followers: 9)
Intl. J. of Embedded Systems     Hybrid Journal   (Followers: 5, SJR: 0.48, CiteScore: 1)
Intl. J. of Emergency Management     Hybrid Journal   (Followers: 11, SJR: 0.185, CiteScore: 0)
Intl. J. of Energy Technology and Policy     Hybrid Journal   (Followers: 7, SJR: 0.224, CiteScore: 0)
Intl. J. of Engineering Management and Economics     Hybrid Journal   (Followers: 4)
Intl. J. of Engineering Systems Modelling and Simulation     Hybrid Journal   (Followers: 8, SJR: 0.175, CiteScore: 0)
Intl. J. of Enterprise Network Management     Hybrid Journal   (SJR: 0.118, CiteScore: 0)
Intl. J. of Entrepreneurial Venturing     Hybrid Journal   (Followers: 1, SJR: 0.308, CiteScore: 1)
Intl. J. of Entrepreneurship and Innovation Management     Hybrid Journal   (Followers: 29, SJR: 0.255, CiteScore: 1)
Intl. J. of Entrepreneurship and Small Business     Hybrid Journal   (Followers: 30, SJR: 0.401, CiteScore: 1)
Intl. J. of Environment and Health     Hybrid Journal   (Followers: 5, SJR: 0.181, CiteScore: 0)
Intl. J. of Environment and Pollution     Hybrid Journal   (Followers: 2, SJR: 0.215, CiteScore: 1)
Intl. J. of Environment and Sustainable Development     Hybrid Journal   (Followers: 17, SJR: 0.132, CiteScore: 0)
Intl. J. of Environment and Waste Management     Hybrid Journal   (Followers: 4, SJR: 0.175, CiteScore: 0)
Intl. J. of Environment, Workplace and Employment     Hybrid Journal   (Followers: 6, SJR: 0.117, CiteScore: 0)
Intl. J. of Environmental Engineering     Hybrid Journal   (Followers: 6)
Intl. J. of Environmental Policy and Decision Making     Hybrid Journal   (Followers: 2)
Intl. J. of Environmental Technology and Management     Hybrid Journal   (Followers: 1, SJR: 0.141, CiteScore: 0)
Intl. J. of Exergy     Hybrid Journal   (Followers: 3, SJR: 0.396, CiteScore: 1)
Intl. J. of Experimental and Computational Biomechanics     Hybrid Journal   (Followers: 8)
Intl. J. of Experimental Design and Process Optimisation     Hybrid Journal   (Followers: 7)
Intl. J. of Export Marketing     Hybrid Journal   (Followers: 3)
Intl. J. of Financial Engineering and Risk Management     Hybrid Journal   (Followers: 4)
Intl. J. of Financial Innovation in Banking     Hybrid Journal   (Followers: 3)
Intl. J. of Financial Markets and Derivatives     Hybrid Journal   (Followers: 5)
Intl. J. of Financial Services Management     Hybrid Journal   (Followers: 1)
Intl. J. of Food Safety, Nutrition and Public Health     Hybrid Journal   (Followers: 21)
Intl. J. of Forensic Engineering     Hybrid Journal   (Followers: 3)
Intl. J. of Forensic Engineering and Management     Hybrid Journal   (Followers: 3)
Intl. J. of Foresight and Innovation Policy     Hybrid Journal   (Followers: 7, SJR: 0.115, CiteScore: 0)
Intl. J. of Functional Informatics and Personalised Medicine     Hybrid Journal   (Followers: 4)
Intl. J. of Fuzzy Computation and Modelling     Hybrid Journal   (Followers: 2)
Intl. J. of Gender Studies in Developing Societies     Hybrid Journal   (Followers: 5)
Intl. J. of Global Energy Issues     Hybrid Journal   (Followers: 8, SJR: 0.199, CiteScore: 0)
Intl. J. of Global Environmental Issues     Hybrid Journal   (Followers: 3, SJR: 0.153, CiteScore: 0)
Intl. J. of Global Warming     Hybrid Journal   (Followers: 2, SJR: 0.259, CiteScore: 1)
Intl. J. of Globalisation and Small Business     Hybrid Journal   (Followers: 14, SJR: 0.233, CiteScore: 1)
Intl. J. of Governance and Financial Intermediation     Hybrid Journal  
Intl. J. of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 3)
Intl. J. of Green Economics     Hybrid Journal   (Followers: 6, SJR: 0.209, CiteScore: 0)
Intl. J. of Grid and Utility Computing     Hybrid Journal   (SJR: 0.341, CiteScore: 2)
Intl. J. of Happiness and Development     Hybrid Journal   (Followers: 8)
Intl. J. of Healthcare Policy     Hybrid Journal  
Intl. J. of Healthcare Technology and Management     Hybrid Journal   (Followers: 7, SJR: 0.139, CiteScore: 0)
Intl. J. of Heavy Vehicle Systems     Hybrid Journal   (Followers: 7, SJR: 0.23, CiteScore: 0)
Intl. J. of High Performance Computing and Networking     Hybrid Journal   (Followers: 4, SJR: 0.428, CiteScore: 1)
Intl. J. of High Performance Systems Architecture     Hybrid Journal   (Followers: 6, SJR: 0.116, CiteScore: 0)
Intl. J. of Higher Education and Sustainability     Hybrid Journal   (Followers: 5)
Intl. J. of Hospitality and Event Management     Hybrid Journal   (Followers: 4)
Intl. J. of Human Factors and Ergonomics     Hybrid Journal   (Followers: 20, SJR: 0.117, CiteScore: 0)
Intl. J. of Human Factors Modelling and Simulation     Hybrid Journal   (Followers: 17)
Intl. J. of Human Resources Development and Management     Hybrid Journal   (Followers: 29, SJR: 0.162, CiteScore: 0)
Intl. J. of Human Rights and Constitutional Studies     Hybrid Journal   (Followers: 14)
Intl. J. of Humanitarian Technology     Hybrid Journal  
Intl. J. of Hybrid Intelligence     Hybrid Journal  
Intl. J. of Hydrology Science and Technology     Hybrid Journal   (Followers: 8, SJR: 0.43, CiteScore: 2)
Intl. J. of Hydromechatronics     Hybrid Journal  
Intl. J. of Image Mining     Hybrid Journal   (Followers: 1)
Intl. J. of Immunological Studies     Hybrid Journal   (Followers: 1)
Intl. J. of Indian Culture and Business Management     Hybrid Journal  
Intl. J. of Industrial and Systems Engineering     Hybrid Journal   (Followers: 11, SJR: 0.34, CiteScore: 1)

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Similar Journals
Journal Cover
International Journal of Big Data Intelligence
Number of Followers: 24  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2053-1389 - ISSN (Online) 2053-1397
Published by Inderscience Publishers Homepage  [443 journals]
  • A computational Bayesian approach for estimating density functions based
           on noise-multiplied data
    • Authors: Masaru Unno, Hua Xu, Hiroaki Mukaidani
      Pages: 143 - 152
      Abstract: In this big data era, an enormous amount of personal and company information can be easily collected by third parties. Sharing the data with the public and allowing data users to access the data for data mining often bring many benefits to the public. However, sharing the microdata with the public usually causes the issue of data privacy. Protecting data privacy through noise-multiplied data is one of the approaches studied in the literature. This paper introduces the B-M L2014 Approach for estimating the density function of the original data based on noise-multiplied microdata. This paper shows applications of the B-M L2014 Approach and demonstrates that the statistical information of the original data can be retrieved from their noise-multiplied data reasonably while the disclosure risk is under control. The B-M L2014 Approach provides a new data mining technique for big data when data privacy is concerned.
      Keywords: big data mining; data anonymisation; privacy-preserving; microdata confidentiality; noise-multiplied data
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 143 - 152
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100880
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • New algorithms for inferring gene regulatory networks from time-series
           expression data on Apache Spark
    • Authors: Yasser Abduallah, Jason T.L. Wang
      Pages: 153 - 162
      Abstract: Gene regulatory networks (GRNs) are crucial to understand the inner workings of the cell and the complexity of gene interactions. Numerous algorithms have been developed to infer GRNs from gene expression data. As the number of identified genes increases and the complexity of their interactions is uncovered, gene networks become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to analyse copious amounts of experimental data from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here we present two new algorithms for reverse engineering GRNs in a cloud environment. The algorithms, implemented in Spark, employ an information-theoretic approach to infer GRNs from time-series gene expression data. Experimental results show that one of our new algorithms is faster than, yet as accurate as, two existing cloud-based GRN inference methods.
      Keywords: network inference; systems biology; spark; big data; MapReduce; gene regulatory networks; GRN; time-series; gene expression; big data intelligence
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 153 - 162
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100881
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • A scalable system for executing and scoring K-means clustering techniques
           and its impact on applications in agriculture
    • Authors: Nevena Golubovic, Chandra Krintz, Rich Wolski, Balaji Sethuramasamyraja, Bo Liu
      Pages: 163 - 175
      Abstract: We present Centaurus - a scalable, open source, clustering service for K-means clustering of correlated, multidimensional data. Centaurus provides users with automatic deployment via public or private cloud resources, model selection (using Bayesian information criterion), and data visualisation. We apply Centaurus to a real-world, agricultural analytics application and compare its results to the industry standard clustering approach. The application uses soil electrical conductivity (EC) measurements, GPS coordinates, and elevation data from a field to produce a 'map' of differing soil zones (so that management can be specialised for each). We use Centaurus and these datasets to empirically evaluate the impact of considering multiple K-means variants and large numbers of experiments. We show that Centaurus yields more consistent and useful clusterings than the competitive approach for use in zone-based soil decision-support applications where a 'hard' decision is required.
      Keywords: K-means clustering; cloud computing
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 163 - 175
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100883
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • Scalable mining, analysis and visualisation of protein-protein
           interaction networks
    • Authors: Shaikh Arifuzzaman, Bikesh Pandey
      Pages: 176 - 187
      Abstract: Proteins are linear chain biomolecules that are the basis of functional networks in all organisms. Protein-protein interaction (PPI) networks are networks of protein complexes formed by biochemical events and electrostatic forces. PPI networks can be used to study diseases and discover drugs. The causes of diseases are evident on a protein interaction level. For instance, elevation of interaction edge weights of oncogenes is manifested in cancers. The availability of large datasets and need for efficient analysis necessitate the design of scalable methods leveraging modern high-performance computing (HPC) platforms. In this paper, we design a lightweight framework on a distributed-memory parallel system to study PPI networks. Our framework supports automated analytics based on methods for extracting signed motifs, computing centrality, and finding functional units. We design message passing interface (MPI)-based parallel methods and workflow, scalable to large networks. To the best of our knowledge, these capabilities collectively make our tool novel.
      Keywords: protein interaction; biological networks; network visualisation; massive networks; HPC systems; network mining
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 176 - 187
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100884
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • Optimising NBA player signing strategies based on practical constraints
           and statistics analytics
    • Authors: Lin Li, Yihang Zhao, Ramya Nagarajan
      Pages: 188 - 201
      Abstract: In National Basketball Association (NBA), how to comprehensively measure a player's performance and how to sign talented players with reasonable contracts are always challenging. Due to various practical constraints such as the salary cap and the players' on-court minutes, no teams can sign all desired players. To ensure the team's competency on both offence and defence sides, player's efficiency must be comprehensively evaluated. This research studied the key indicators widely used to measure player efficiency and team performance. Through data analytics, the most frequently referred statistics including player efficiency rating, defence rating, real plus minus, points, rebounds, assists, blocks, steals, etc. were chosen to formulate the prediction of the team winning rate in different schemes. Based on the models trained and tested, two player selection strategies were proposed according to different objectives and constraints. Experimental results show that the developed team winning rate prediction models have high accuracy and the player selection strategies are effective.
      Keywords: optimisation; prediction; regression; linear programming; statistics analytics; constraints
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 188 - 201
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100885
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • Text visualisation for feature selection in online review analysis
    • Authors: Keerthika Koka, Shiaofen Fang
      Pages: 202 - 211
      Abstract: Opinion spamming is a reality, and it can have unpleasant consequences in the retail industry. While there are, several promising research works done on identifying the fake online reviews from genuine online reviews, there have been few involving visualisation and visual analytics. The purpose of this work is to show that feature selection through visualisation is at least as powerful as the best automatic feature selection algorithms. This is achieved by applying radial chart visualisation technique to the online review classification into fake and genuine reviews. Radial chart and the colour overlaps are used to explore the best feature selection through visualisation for classification. Parallel coordinate visualisation of the review data is also explored and compared with radial chart results. The system gives a structure to each text review based on certain attributes, compares how different or similar the structure of the different or same categories are, and highlights the key features that contribute to the classification the most. Our visualisation technique helps the user get insights into the high dimensional data by providing means to eliminate the worst features right away, pick some best features without statistical aids, understand the behaviour of the dimensions in different combinations.
      Keywords: text visualisation; feature selection; radial chart; online review analysis
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 202 - 211
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100887
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • Network traffic driven storage repair
    • Authors: Danilo Gligoroski, Katina Kralevska, Rune E. Jensen, Per Simonsen
      Pages: 212 - 223
      Abstract: Recently we constructed an explicit family of locally repairable and locally regenerating codes. Their existence was proven by Kamath et al. but no explicit construction was given. Our design is based on HashTag codes that can have different sub-packetisation levels. In this work we emphasise the importance of having two ways to repair a node: repair only with local parity nodes or repair with both local and global parity nodes. We say that the repair strategy is network traffic driven since it is in connection with the concrete system and code parameters: the repair bandwidth of the code, the number of I/O operations, the access time for the contacted parts and the size of the stored file. We show the benefits of having repair duality in one practical example implemented in Hadoop. We also give algorithms for efficient repair of the global parity nodes.
      Keywords: vector codes; repair bandwidth; repair locality; exact repair; parity-splitting; global parities; Hadoop
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 212 - 223
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100888
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • DeepSim: cluster level behavioural simulation model for deep learning
    • Authors: Yuankun Shi, Kevin J. Long, Kaushik Balasubramanian, Zhaojuan Bian, Adam Procter, Ramesh Illikkal
      Pages: 224 - 233
      Abstract: We are witnessing an explosion of AI use cases driving the computer industry, and especially datacentre and server architectures. As Intel faces fierce competition in this emerging technology space, it is critical that architecture definitions and directions are driven with data from proper tools and methodologies, and insights are drawn from end-to-end holistic analysis at the datacentre levels. In this paper, we introduce DeepSim, a cluster-level behavioural simulation model for deep learning. DeepSim, which is based on the Intel CoFluent simulation framework, uses timed behavioural models to simulate complex interworking between compute nodes, networking, and storage at the datacentre level, providing a realistic performance model of real-world image recognition applications based on the popular deep learning framework Caffe. The end-to-end simulation data from DeepSim provides insights which can be used for architecture analysis driving future datacentre architecture directions. DeepSim enables scalable system design, deployment, and capacity planning through accurate performance insights. Results from preliminary scaling studies (e.g., node scaling and network scaling) and what-if analyses (e.g., Xeon with HBM and Xeon Phi with dual OPA) are presented in this paper. The simulation results are correlated well with empirical measurements, achieving an accuracy of 95%.
      Keywords: deep learning; datacentre; behavioural simulation; AlexNet; architecture analysis; performance analysis; server srchitecture
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 224 - 233
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100892
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • MapReduce-based fuzzy very fast decision tree for constructing
           prediction intervals
    • Authors: Ojha Manish Kumar, Kumar Ravi, Vadlamani Ravi
      Pages: 234 - 247
      Abstract: We propose the fuzzy version of very fast decision tree (VFDT) to predict prediction intervals and compared them with those generated by traditional VFDT. The proposed fuzzy VFDT is able to capture intrinsic features of VFDT as well as uncertainties available in data. The VFDT and fuzzy VFDT were trained using the lower upper bound estimation (LUBE) method in order to generate prediction intervals. We also implemented VFDT; developed and implemented fuzzy VFDT using Apache Hadoop MapReduce framework, where multiple slave nodes build a VFDT and fuzzy VFDT model. The developed models were tested on six datasets taken from the web. We conducted sensitivity analysis by studying the influence of the window size of the data stream, number of bins in discretisation on the final results. Results demonstrated that the proposed MapReduce-based fuzzy VFDT and VFDT can construct high-quality prediction intervals precisely and quickly.
      Keywords: very fast decision tree; VFDT; fuzzy VFDT; MapReduce; prediction interval; big data; Hadoop; stream data
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 234 - 247
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100894
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • Real-time event search using social stream for inbound tourist
           corresponding to place and time
    • Authors: Ruriko Kudo, Miki Enoki, Akihiro Nakao, Shu Yamamoto, Saneyasu Yamaguchi, Masato Oguchi
      Pages: 248 - 258
      Abstract: Since the decision was made to hold the Olympic Games in 2020 in Tokyo, the number of foreign tourists visiting the city has been increasing rapidly. Accordingly, tourists have been seeking more sightseeing information. While guidebooks are good for pointing out popular tourist attractions, it is more difficult for tourists to get information on local events and spots that are just becoming popular. We developed a tourist information distribution system that sends information corresponding to places and times. The system extracts event information from social media streams in a per place and time manner and provides it to tourists. In order to extract useful information, we performed event classification using actual Twitter data. We examined how to distribute the events in order to make the system more user-friendly. We also developed an information supplement function using external information.
      Keywords: Twitter; social networking service; SNS; local event; sightseeing information; event name; information extract; external information; Mecab; support vector machine; SVM; random forest
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 248 - 258
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100896
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • Two-channel convolutional neural network for facial expression
           recognition using facial parts
    • Authors: Hui Wang, Jiang Lu, Lucy Nwosu, Ishaq Unwala
      Pages: 259 - 268
      Abstract: This paper proposes the design of a facial expression recognition system based on the deep convolutional neural network by using facial parts. In this work, a solution for facial expression recognition that uses a combination of algorithms for face detection, feature extraction and classification is discussed. The proposed method builds a two-channel convolutional neural network model in which facial parts are used as inputs: the extracted eyes are used as inputs to the first channel, while the mouth is the input into the second channel. Feature information from both channels converges in a fully connected layer which is used to learn global information from these local features and is then used for expression classification. Experiments are carried out on the Japanese female facial expression dataset and the extended Cohn-Kanada dataset to determine the expression recognition accuracy for the proposed facial expression recognition system based on facial parts. The results achieved shows that the system provides state of art classification accuracy with 97.6% and 95.7% respectively when compared to other methods.
      Keywords: facial expression recognition; convolutional neural networks; facial parts
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 259 - 268
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100897
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • Efficient clustering techniques on Hadoop and Spark
    • Authors: Sami Al Ghamdi, Giuseppe Di Fatta
      Pages: 269 - 290
      Abstract: Clustering is an essential data mining technique that divides observations into groups where each group contains similar observations. K-means is one of the most popular clustering algorithms that has been used for over 50 years. Due to the current exponential growth of the data, it became a necessity to improve the efficiency and scalability of K-means even further to cope with large-scale datasets known as big data. This paper presents K-means optimisations using triangle inequality on two well-known distributed computing platforms: Hadoop and Spark. K-means variants that use triangle inequality usually require caching extra information from the previous iteration, which is a challenging task to achieve on Hadoop. Hence, this work introduces two methods to pass information from one iteration to the next on Hadoop to accelerate K-means. The experimental work shows that the efficiency of K-means on Hadoop and Spark can be significantly improved by using triangle inequality optimisations.
      Keywords: K-means; Hadoop; Spark; MapReduce; efficient clustering; triangle inequality K-means
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 269 - 290
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100898
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • A hybrid power management schema for multi-tier data centres
    • Authors: Aryan Azimzadeh, Babak Maleki Shoja, Nasseh Tabrizi
      Pages: 291 - 296
      Abstract: Data centres play an important role in the operation and management of IT infrastructures but because of their huge power consumption, it raises an issue of great concern as it relates to global warming. This paper explores the sleep state of data centres' servers under specific conditions such as setup time and identifies an optimal number of servers potentially to increase energy efficiency. We use a dynamic power management policy-based model with the optimal number of servers that is required in each tier while increasing servers' setup time after sleep mode. The reactive approach is used to validate the results and energy efficiency by calculating the average power consumption of each server under specific sleep mode and setup time. Our method uses average power consumption to calculate the normalised-performance-per-watt in order to evaluate the power efficiency. The results indicate that the schema reported in this paper can improve power efficiency in data centres with high setup time servers.
      Keywords: power; green; management; schema; multi-tier
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 291 - 296
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100905
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • Predicting hospital length of stay using neural networks
    • Authors: Thanos Gentimis, Ala' J. Alnaser, Alex Durante, Kyle Cook, Robert Steele
      Pages: 297 - 306
      Abstract: Accurate prediction of hospital length of stay can provide benefits for hospital resource planning and quality-of-care. We describe the utilisation of neural networks for predicting the length of hospital stay for patients with various diagnoses based on selected administrative and clinical attributes. An all-condition neural network, that can be applied to all patients and not limited to a specific diagnosis, is trained to predict whether patient stay will be long or short in terms of the median length of stay as the cut-off between long and short, and predicted at the time the patient leaves the intensive care unit. In addition, neural networks are trained to predict whether patients of 14 specific common primary diagnoses will have a long or short stay, as defined as greater than or less than or equal to the median length of stay for that particular condition. Our dataset is drawn from the MIMIC III database. Our prediction accuracy is approximately 80% for the all-condition neural network and the neural networks for specific conditions generally demonstrated higher accuracy and all clearly out-performed any linear model.
      Keywords: length of stay; health analytics; neural networks; MIMIC III
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 297 - 306
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100893
      Issue No: Vol. 6, No. 3/4 (2019)
       
  • Towards an automation of the fact-checking in the journalistic web
           context
    • Authors: Edouard Ngor Sarr, Ousmane Sall, Aminata Maiga, Mouhamadou Saliou Diallo
      Pages: 307 - 321
      Abstract: Is Fact checking automatisable? Apparently, yes, since numerous moved forward noted in the search and the analysis of digital data. However, this task which in priori seemed to be simple, turns out rather binding. Indeed, automate the check of facts combine and requires at the same time very advanced knowledge in analysis of data, in search of web data, the web technologies, in image processing and sometimes in automatic natural language processing (NLP). Nevertheless, the latter years, numerous researches are led to deepen such an analysis. In this article, having revisited the state of the art concerning the question, we identify and diagnose in detail the obstacles before concluding with an explanation of the methods.
      Keywords: fact-checking; data journalism; semantic web
      Citation: International Journal of Big Data Intelligence, Vol. 6, No. 3/4 (2019) pp. 307 - 321
      PubDate: 2019-07-19T23:20:50-05:00
      DOI: 10.1504/IJBDI.2019.100906
      Issue No: Vol. 6, No. 3/4 (2019)
       
 
 
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