Big data is a collection of data that cannot be captured, managed, and processed by conventional software tools in a certain period of time. It is a massive, high growth rate and diversified information asset that needs new processing mode to have stronger decision-making power, insight, and discovery power as well as process optimization ability.

Generally speaking, big data analysis can be carried out through several methods, such as web crawlers, databases and data warehouses, analysis, visualization, and big data mining algorithms. Among them, web crawlers are a popular analysis technology in recent years. They can extract numerical value or text data from a web page and format it into analysis data. In addition to using Python language, this technology can also be implemented by some software packages. Big data mining algorithms needs to deal with a large number of data. And the speed of dealing is also considered. These algorithms include optimization technology (such as Gradient Descent Algorithm, Particle Swarm Optimization Algorithm, etc.), mathematical modeling or other prediction technology (such as Neural Network, Bayesian Classification, Rough Set, Decision Tree, etc.), decision analysis technology (such as Grey Decision, Multi Criteria Decision, etc.), Performance evaluation technology (such as Data Envelopment Analysis, Fuzzy Comprehensive Evaluation, etc.). Because these data mining algorithms have been rarely studied together with big data in the past, especially applied to engineering problems, there are great challenges when applying these data mining algorithms to the area of big data.

The purpose of this Special Issue is to collate original research and review articles with a focus on the combination of big data and data mining technology to solve the challenges of applying data mining algorithms to big data problems. The issues involved include big data, mathematical modeling and various data mining algorithms, which are applied in different fields such as engineering and management.

Potential topics include but are not limited to the following:

• Information and network theorem
• Algorithm model of Internet of things based on AI
• Mathematical function of objective programming
• Production and operation function models
• Quality control and fuzzy evaluation
• Multi criteria decision making and fuzzy evaluation
• Differential equation and prediction
• Capital asset pricing models
• Social network models
• Other computing methods (e.g., rough set or chaos theory) related to engineering and management