 International Journal of Business Intelligence and Data Mining (IJBIDM)
ISSN (Online): 1743-8195 - ISSN (Print): 1743-8187
Click to download IJBIDM leaflet Abstracting/Indexing Services and Journal Lists
The IJBIDM publishes and disseminates knowledge on an international basis in the areas of business intelligence, intelligent data analysis, and data mining. It provides a forum for state-of-the-art developments and research, as well as current innovative activities in business intelligence, data analysis and mining. In contrast to other journals, IJBIDM focuses on the application of data analysis and mining techniques in business applications.
Intelligent data analysis provides powerful and effective tools for problem solving in a variety of business modelling tasks. IJBIDM is devoted to 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.
 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.
Subject Coverage
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
Specific Notes for Authors
Submitted papers should not have been previously published nor be currently
under consideration for publication elsewhere. (N.B. Conference papers may
only be submitted if the paper was not originally copyrighted and if it has
been completely re-written).
All papers are refereed through a double blind process. A guide for authors,
sample copies and other relevant information for submitting papers are
available in the Full Submission Guidelines
web-page.
AUTHORS MUST SUBMIT THEIR PAPERS THROUGH THE ON LINE SUBMISSION SYSTEM, OTHERWISE THEIR PAPERS WOULD NOT BE CONSIDERED FOR PUBLICATION All papers must be submitted online. To submit a paper, please go to
Online Submissions of Papers.
If you experience any problems submitting your paper online,
please contact submissions@inderscience.com,
describing the exact problem you experience. Please include in your email the title of the Journal.
Editors and Members of the Editorial Board
Editor in Chief Dr. David Taniar
Monash University
Clayton School of Information Technology
Clayton, Victoria 3800
AUSTRALIA
david.taniar@infotech.monash.edu.au
Editorial Board Members Prof. Michael R. BertholdMi
University of Konstanz
Department of Computer and Information Science
BOX M712
78457 Konstanz
GERMANY Dr. A. Fazel FamiliA.
Institute for Information Technology
National Research Council of Canada
Building M-50, Montreal Rd
Ottawa Ont. K1A 0R6
CANADA Prof. Tu Bao HoTu
Japan Advanced Institute of Science and Technology (JAIST)
School of Knowledge Science
1-1 Asahidai, Nomi
Ishikawa 923-1292
JAPAN Prof. Lakhmi C. JainLa
Professor of Knowledge-Based Engineering, Founding Director of the KES Centre
University of South Australia
Division of Information Technology, Engineering and the Environment
School of Electrical and Information Engineering
Mawson Lakes Campus
Adelaide, South Australia 5095
AUSTRALIA Dr. Hillol KarguptaHi
Associate Professor, Computer Science and Electrical Engineering Department
University of Maryland Baltimore County
1000 Hilltop Circle
Baltimore MD 21250
USA Dr. Vasile PaladeVa
Lecturer
Oxford University
Computing Laboratory
Wolfson Building
Parks Road
Oxford OX1 3QD
UK Prof. John F. RoddickJo
Flinders University
School of Computer Science, Engineering and Mathematics
PO Box 2100
Adelaide, South Australia 5001
AUSTRALIA Prof. Yong ShiYo
Graduate School, Chinese Academy of Sciences
University of Nebraska at Omaha
College of Information Science and Technology
Omaha NE 68182-0572
USA Dr. Miguel-Angel SiciliaMi
Computer Science Department
University of Alcalá
Madrid
SPAIN Prof. Lipo WangLi
Nanyang Technological University
School of Electrical and Electronic Engineering
Block S1, 50 Nanyang Avenue
Singapore 639798
SINGAPORE Dr. Ruili WangRu
Massey University
School of Engineering and Advanced Technology
Private Bag 11222
Palmerston North
NEW ZEALAND Prof. Xindong WuXi
Chair, Department of Computer Science
University of Vermont
33 Colchester Avenue
351 Votey Building
Burlington VT 05405
USA
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