International Journal of Business Intelligence and Data Mining

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Editor in Chief: Dr. David Taniar
ISSN online: 1743-8195
ISSN print: 1743-8187
4 issues per year
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IJBIDM provides a forum for state-of-the-art developments and research as well as current innovative activities in business intelligence, data analysis and mining. Intelligent data analysis provides powerful and effective tools for problem solving in a variety of business modelling tasks. IJBIDM highlights intelligent techniques used for business modelling, including all areas of data visualisation, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, data mining techniques, tools and applications, neurocomputing, evolutionary computing, fuzzy techniques, expert systems, knowledge filtering, and post-processing.

 



 Topics covered include

  • Data extraction/reporting/cleaning/pre-processing
  • OLAP, decision analysis, causal modelling
  • Reasoning under uncertainty, noise in data
  • Business intelligence cycle
  • Model specification/selection/estimation
  • Web technology, mining, agents
  • Fuzzy, neural, evolutionary approaches
  • Genetic algorithms, machine learning, expert/hybrid systems
  • Bayesian inference, bootstrap, randomisation
  • Exploratory/automated data analysis
  • Knowledge-based analysis, statistical pattern recognition
  • Data mining algorithms/processes
  • Classification, projection, regression, optimisation clustering
  • Information extraction/retrieval, human-computer interaction
  • Multivariate data visualisation, tools

 

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Business Intelligence:

  • Data extraction and reporting
  • OLAP
  • Data cleaning and pre-processing
  • Decision analysis
  • Causal modelling
  • Reasoning under uncertainty
  • Uncertainty and noise in data
  • Business intelligence cycle, and model specification/selection/estimation
  • Web technology, mining and agents

Intelligent Techniques:

  • Fuzzy, neural, and evolutionary approaches
  • Genetic algorithms
  • Machine learning
  • Expert systems
  • Hybrid systems
  • Bayesian inference, bootstrap and randomisation

Data Analysis and Data Mining:

  • Exploratory and automated data analysis
  • Knowledge-based analysis
  • Statistical pattern recognition
  • Data mining algorithms and processes
  • Classification, projection, regression, optimisation clustering
  • Information extraction and retrieval
  • Multivariate data visualisation

Applications and Tools:

  • Visualisation tools
  • Applications (e.g. commerce, engineering, finance, manufacturing, science)
  • Human-computer interaction in intelligence data analysis
  • Business intelligence and data analysis systems and tools

 

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Objectives

Business intelligence and data mining share many common issues. IJBIDM aims to stimulate the exchange of ideas and interaction between these related fields of interest. It is intended to be the premier technical publication in the field, providing a resource collection relevant common methods and techniques and a forum for unifying the diverse constituent research communities in business intelligence and intelligent data analysis. Advances in data gathering, distribution and analysis have also created a need for an application of intelligent data analysis techniques to solve business modelling problems.

IJBIDM publishes original research results, surveys and tutorials of important areas and techniques, detailed descriptions of significant applications, technical advances and news items concerning use of intelligent data analysis technique in business applications. IJBIDM puts a heavy emphasis on new data analysis architectures, methodologies, and techniques and their applications in business.


Readership

IJBIDM provides a forum for the examination of issues related to the research and applications of intelligent data analysis in business. IJBIDM is targeted at academic, researchers, and IT professionals. This journal provides a vehicle to help business analysts and IT professionals to disseminate information and to learn from each other’s work. Readers will be well informed of the latest development in research and practice in intelligent data analysis and data mining and its applications in business problems. Readers will be able to learn established knowledge in data analysis techniques through comprehensive survey articles. Readers will have the opportunity to learn future direction in business intelligence and data mining
Contents

IJBIDM is devoted to the publications of high quality papers on theoretical developments and practical applications in business intelligence, data analysis and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. Special issues are devoted to current issues in business intelligence and techniques. IJBIDM also publishes best papers from international conferences in the areas relevant to the journal.

Papers published in IJBIDM are geared heavily towards applications (use of intelligence data analysis and mining techniques in business applications), with an anticipated split of 70% of the papers published being applications-oriented, research and the remaining 30% containing more theoretical research.


 

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Browse issues

Vol. 7
Vol. 6
Vol. 5
Vol. 4
Vol. 3
Vol. 2

 

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 IJBIDM is indexed in:

 

 IJBIDM is listed in:

 

Editor in Chief

  • Taniar, David, Monash University, Australia
    (david.taniar@infotech.monash.edu.au)

    EB Members

    • Berthold, Michael R., University of Konstanz, Germany
    • Famili, A. Fazel, National Research Council of Canada, Canada
    • Ho, Tu Bao, Japan Advanced Institute of Science and Technology (JAIST), Japan
    • Jain, Lakhmi C., University of Canberra, Australia
    • Kargupta, Hillol, University of Maryland Baltimore County, USA
    • Palade, Vasile, Oxford University, UK
    • Roddick, John F., Flinders University, Australia
    • Shi, Yong, University of Nebraska at Omaha, USA
    • Sicilia, Miguel-Angel, University of Alcalá, Spain
    • Wang, Lipo, Nanyang Technological University, Singapore
    • Wang, Ruili, Massey University, New Zealand
    • Wu, Xindong, University of Vermont, USA

     

    A few essentials for publishing in this journal

     

    • Submitted articles should not have been previously published or be currently under consideration for publication elsewhere.
    • Conference papers may only be submitted if the paper has been completely re-written (taken to mean more than 50%) and the author has cleared any necessary permissions with the copyright owner if it has been previously copyrighted.
    • All our articles are refereed through a double-blind process.
    • All authors must declare they have read and agreed to the content of the submitted article. A full statement of our Ethical Guidelines for Authors is available.

     

    Submission process

     

    All articles for this journal must be submitted using our online submissions system.

    Read our information on preparing and submitting articles.