The Seventh International Conference on Data Mining and Big Data (DMBD'2022) is an international forum allowing researchers and practitioners to exchange the latest theories, algorithms, models, and applications of data mining and big data, as well as artificial intelligence techniques. Data mining refers to examining big data sets to look for relevant or pertinent information. Big data contains huge amounts of data and information and is worthy of in-depth research.

DMBD'2022 is the seventh event, following the Guangzhou, Belgrade, Chiang Mai, Shanghai, Fukuoka and Bali events, at which hundreds of delegates from all over the world gathered to share their latest achievements, innovative ideas, and marvelous designs and their implementations.

This year's main theme is FinTech, and we will pay particular attention to technologies and applications in this area. We will also hold a quantitative trading competition.

The main topics of the conference are as follows:

• Quantitative investment;
• Market microstructure;
• Fraud detection;
• Risk prediction;
• Credit modeling;
• Individual financing;
• Financial theories;
• Financial activities modeling;
• Financial intelligent system.

• Machine learning;
• Statistical learning;
• Supervised learning;
• Unsupervised, self-supervised learning;
• Few-shot learning;
• Transfer learning;
• Reinforcement learning;
• Meta learning;
• Systems for data mining;
• Mining text, semi-structured, spatiotemporal, streaming, graph, web, multimedia data;
• Personalization and recommendation systems;
• Case-based reasoning;
• Similarity-based reasoning.

• Data models and architectures;
• Security, privacy, and trust;
• Data protection and integrity;
• Identity theft, data loss and leakage;
• Legal and ethical issues;
• Data analytics and metrics;
• Data representation and structures;
• Data management and processing;
• Data capturing and acquisition;
• Tools and technologies QoS in big data.

• Social networks analysis;
• Data searching and mining;
• Visualization of data;
• Personal data logging and quantified self;
• Context-aware data;
• Data economics;
• Applications of data mining and big data;
• Methodologies and use cases;
• Usability issues;
• Storages and network requirements;
• Network models and protocols;
• Big data in cloud and IoT;
• Techniques for big data processing.