Challenges in Business Intelligence
A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".
Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 33619
Special Issue Editor
Interests: financial markets; trading strategies; valuation; sustainability
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Business intelligence (BI) includes a bundle of mathematical models mainly related to statistics and operations research that exploit large amounts of data to extract useful knowledge for complex decision-making processes in firms and organizations. Such data play a key role in the development of BI platforms, and likewise, both researchers and practitioners have to face situations where data come from different and heterogeneous sources: the internet, emails, unstructured texts, web images, financial and administrative data, stock markets, cashier operations from supermarkets, etc.
This Special Issue will contribute to bringing original research in the area of business intelligence, thus enabling the publication of research related to new methodological approaches to deal with new challenges in the topic, or making publicly available large databases that allow the application and comparison of competitive BI techniques.
Prof. Dr. Francisco Guijarro
Guest Editor
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- data analytics
- big data
- data cleaning
- metadata
- management information system
- data warehouse
- unstructured data
- benchmarking
- data mining