Guest editorial: a special issue on optimization techniques for business intelligence systems

Business Intelligence refers to computer-based technologies, methodologies, tools, and systems for collecting, integrating, analyzing, and presenting large volumes of information to enable better business decision making. Increasingly, as businesses becomemore automated, data-driven, and real-time, business intelligence architecture evolves to support both strategic and operational levels of business decision making, which requires more advanced techniques and technical support. Aiming at better supporting business decision-making, business intelligence system is naturally a decision support system (DSS) in practice. Among many core techniques of DSS, an optimization-based DSS whose various optimizationmethodologies and techniques are applied, aims to arrive at the best ormost satisfactory decision out of a multitude of possible alternatives for business decision problems. Decision making has become more complicated and difficult in the current rapidly changing business environment. In recent years, both typical decision optimization such as multi-criteria decision making, and new decision intelligence techniques such as particle swarm optimization, have had unimaginable improvements. Particularly, through integrated with knowledge engineering and computational intelligence approaches, hybrid methodologies have been developed to deal with complex, uncertain, and un-structured decision optimization problems in businesses. This Special Issue provides an updated overview of the research field in line with optimization techniques for business intelligence systems. It includes ten papers to present recent developments in methodologies, techniques and applications in optimization techniques for business intelligence systems frommultiple aspects. The Special Issue covers three sub-topics: Intelligent multi-criteria optimization systems (papers 1–3); Optimization methodologies for business intelligence applications (papers 4–7); and Hybrid optimization algorithms by integration with computational intelligence and machine learning techniques (papers 8–10).