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Research Ideas for the Journal of Big Data and Computational Science

Dr. Chia-Lin Chang, Dr. Michael McAleer, Dr. Wing-Keung WONG ,

Chia-Lin Chang 1, Michael McAleer 2*, Wing Keung Wong 3
1National Chung Hsing University, Taiwan
2National Tsing Hua University, Taiwan
3Asia University, Taiwan

*Corresponding author:

Michael McAleer, National Tsing Hua University, Taiwan, Email:

Journal Big Data and Computational Science (JBDCS) is a recently established open access peer reviewed multi-disciplinary journal for cognate disciplines in the Sciences and Social Sciences that will be inaugurated in 2018. The journal focuses on disseminating the latest innovative theoretical and applied research in the analysis of big data using advanced and innovative computational science analytics and techniques, as well as their relationships to cognate disciplines across a wide range of areas in the Sciences and Social Sciences. The intention of JBDCS is to publish innovative and high quality theoretical and applied papers, including case studies, on a wide range of topics in the analysis of big data and the use of advanced and innovative techniques in the mathematical and computational and sciences that are directly relevant for academics, researchers, and practitioners alike. Advances in the various fields of informatics across a wide range of cognate disciplines are driving a major expansion in big data analytics and innovative developments in computational science. Much of the innovative and advanced research on big data has focused on the application of advanced computational science methods for a better understanding of models and their underlying stochastic processes. JBDCS seeks academically rigorous papers that will appeal to theoreticians and also have direct relevance to practitioners and policy makers across a wide range of cognate disciplines in the Sciences and Social Sciences. Contributions that use rigorous analytical, mathematical and statistical methods based on panel data, cross section data, time series data, simulated numerical data, or case study data, in the empirical testing of theoretical models arising frombig data and computational science, are strongly encouraged. The journal seeks academically rigorous papers that will appeal to theoreticians and also have direct relevance to policy makers and practitioners in big data and computational science.

JBDCS encompasses a wide spectrum of innovative topics in the disciplines of big data and computational science, and cognate disciplines that include, but are not restricted to, the following:

(i) Big Data Analytics
(ii) Computational Science
(iii) Computer Simulations
(iv) Computational Modelling
(v) Informatics
(vi) Bioengineering
(vii) Molecular Engineering
(viii) Bioimaging Modulations
(ix) Proteomics
(x) Genomics
(xi) Machine Learning
(xii) Algorithms
(xiii) Molecular Medicine
(xiv) Imaging Informatics
(xv) Economics
(xvi) Finance
(xvii) Management
(xviii) Management Science
(xix) Marketing
(xx) Accounting Research
(xxi) Quantitative Methods
(xxii) Time Series Analysis
(xxiii) Cross Section Data Analysis
(xxiv) Dynamic Panel Data Models
(xxv) Statistics
(xxvi) Mathematics
(xxvii) Operations Research
(xxviii) Engineering.
(xxix) Economics
(xxx) Finance
(xxxi) Management
(xxxii) Management Science
(xxxiii) Marketing
(xxxiv) Accounting Research
(xxxv) Quantitative Methods
(xxxvi) Time Series Analysis
(xxxvii) Cross Section Data Analysis
(xxxviii) Dynamic Panel Data Models
(xxxix) Statistics
(xl) Mathematics
(xli) Operations Research
(xlii) Engineering.

The intention of JBDCS is to publish articles that are connected to, but are not limited to, the following:
(1) Computational Science
(2) Computer Programming
(3) Big Data Analytics
(4) Simulations
(5) Numerical Analysis
(6) Theoretical and Applied Mathematics
(7) Theoretical and Applied Statistics
(8) Theoretical and Applied Econometrics
(9) Environmental Science
(10) Fossil Fuels
(11) Carbon Emissions
(12) Climate Change and Global Warming
(13) Environmental Management
(14) Financial Decision Making
(15) Financial Risk Analysis
(16) Financial Risk Management
(17) Economics
(18) Finance
(19) Financial Econometrics
(20) Forecasting
(21) Energy Economics
(22) Energy Finance
(23) Renewable and Sustainable Energyv (24) Green Energy
(25) Agricultural Commodities
(26) Management
(27) Management Science
(28) Marketing
(29) Operations Research
(30) Engineering
(31) Financial Engineering
(32) Bioengineering
(33) Big Data Analytics
(34) Data Mining
(35) Informatics
(36) Imaging Informatics
(37) Molecular Engineering
(38) Docking simulations
(39) Bioimaging and modulations
(40) Proteomics
(41) Genomics

Big Data can be defined as involving two components, namely Computer Software and Data Analytics:
(i) data sets that are so complex that standard computer software for dealing with them are inadequate; New computer software and hardware facilities are required that include, but are not restricted to, the following:

(1) increasing the size of memory on computer software;
(2) faster computers;
(3) expand the capacity of standard commercial software, such as SAS, SPSS, MATLAB, R, Revolution R Enterprise (RRE), and S language;
(4) bootstrap methods;
(5) numerical calculation;
(6) optimal subsampling algorithms, including alternative time scales estimation.

(ii) Novel and advanced techniques data analytics are required for purposes of processing, locating, searching, discovering, capturing, checking, storing, updating, protecting, retrieving, sending, sharing, transferring, receiving, extracting, estimating, modelling, evaluating, and predicting data. New model specifications are required that include, but are not restricted to, the following:

(1) continuous time series models for dynamic time series nano data;
(2) continuous space models for cross section nano data;
(3) continuous time series and continuous space models for dynamic time series cross-section nano panel data models;
(4) continuous time Cox proportional hazard models;
(5) Bayesian spatio-temporal geostatistical models;
(6) optimal subsampling models, including alternative time scales estimation.

Some research areas of significant academic, theoretical, practical and public policy interest that are of substantial interest to JBDCS include, but are to restricted to, the following:

(1) Data information can be collected in many different ways, including panel data, cross section data, time series data, numerical data, simulated data, bootstrap data, and data collected from case studies. This naturally leads to analytical and technical considerations of Big Data and Computational Science, and cognate disciplines in the Sciences and Social Sciences.

(2) As discussed above, Big Data can be defined as involving two components, the first of which is Computer Software. Data sets can be so complex that standard computer software for dealing with them are inadequate. Consequently, new computer software and hardware facilities, such as increasing the size of memory on computer software, faster computers, expanding the capacity of standard commercial software, bootstrap methods, numerical calculation, and optimal subsampling algorithms, among others, need to be developed.

(3) The second component in the analysis of Big Data is the use of Data Analytics that require novel and advanced computational, mathematical, statistical and econometric techniques for purposes of processing, locating, searching, discovering, capturing, checking, storing, updating, protecting, retrieving, sending, sharing, transferring, receiving, extracting, estimating, modelling, evaluating, and predicting Big Data.

(4) The application of innovative and technical developments in computer software and data analytics are especially important for analysing and testing theoretical models and approaches in numerous disciplines in the Sciences and Social Sciences.

(5) Data that arise from panels, cross sections, time series, numerical analysis, simulations, bootstraps, and empirical case studies need to be understood. Big data issues arising from such data sources, especially countably finite but exhaustive data sets that can be downloaded from the internet include, but are to restricted to, the following:searched, discovered, located, captured, processed, checked, stored, updated, protected, retrieved, distributed, shared, transferred, received, extracted, analysed, estimated, modelled, evaluated, and predicted.

JBDCS invites authors to submit manuscripts to be considered for inclusion in Volume 1(1), as well as in future volumes and issues.

As can be seen from the suggestions given above, there are numerous possible researchtopics that arise from the disciplines of big data and computational science, and numerous cognate disciplines, that can be applied to analyse important and critical issues related to the topical issues that are intended to be published in JBDCS.

The journal is confident that academics, researchers, advanced graduate students, practitioners and policy makers can create, develop, establish and use many more exciting research topics that use a wide range ofpossible data options to estimate and test academic and intellectual theories, and evaluateempirical regularities and practical case studies in big data and computational science, and cognate fields in the Sciences and Social Sciences.

The editorial staff at JBDCS hopes that these and other important areas of research in big data and computational science, and cognate disciplines, will attract interesting, highquality, innovative and challenging submissions.

It is a genuine challenge, and an honour and pleasure,for the three co-editorialists to have been appointed the Co-Editor-in-Chief, Editor-in-Chief, and Co-Editor-in-Chief, respectively, of the Journal of Big Data and Computational Science (JBDCS).

We look forward to working with the active and vibrant members of the International Advisory Board, Editorial Board, extensive reviewing panels, and contributors to make JBDCS an accessible and leading outlet for high quality academic, theoretical, and practical research in all areas of big data and computational science, and their relationships to cognate disciplines in the Sciences and Social Sciences.

For financial support, the authors wish to thank the National Science Council, Ministry of Science and Technology (MOST), Taiwan, and the Australian Research Council.

Published: 18 October 2017

Copyright:

© 2017 Chang et al.. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.