Business intelligence can help enterprises manage huge amount of data by extracting, cleaning, integrating, querying and analyzing data, and issuing reports, etc., in order to get valuable comprehensive information to assist in making decisions. It is a kind of data-driven intelligent management tool. Due to the value of data-driven decision making in business intelligence, it has received widespread attention from the corporate and academic communities and has been widely used in research in the fields of customer relationship management, finance, supply chain, and other industries such as finance and energy.

In the era of big data, enterprises will generate a large amount of valuable information in all aspects of management and operation, and how they use that data and turn it into valuable information becomes a decisive factor for enterprises to lead the market. However, traditional data analysis methods cannot effectively use data statistically, resulting in a huge waste of resources. Business intelligence provides management and other stakeholders with information relevant to actions and decisions through advanced information technology such as data mining, data warehousing, and online analytical processing. At present, business intelligence systems often suffer from poor portability and integration and lack scalability and flexibility. With the development of various technologies, the continuous expansion of software scale and its increasing complexity, the research on business intelligence-related issues has become more and more urgent.

This Special Issue will focus on research targeting the data-driven business intelligence applications and information technology, and provide readers with high-quality contributions exploring and discussing the development and optimization of business intelligence systems, innovation, and cutting-edge applications of information technology. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Scientific programming methods and models for data-driven decision-making
• Analysis of data-driven decision making from mathematical programming perspective (fuzzy programming, interval programming, stochastic programming, etc.)
• Interactive multi-objective dynamic programming decision approach
• Interactive data exploration
• Data analysis for decision support
• Business intelligence system design
• Application optimization of business intelligence
• Intelligent optimization models and algorithms for business intelligence
• Scientific programming for business intelligence visualization and representation
• Application and development of data mining models
• Machine and/or deep learning algorithms and applications in business intelligence
• Mining methods and algorithms (classification, regression, clustering, probabilistic modelling)
• Intelligent text mining model using deep neural networks
• Large-scale classification algorithms and large-scale clustering algorithms with application in business intelligence