The Seventh International Conference on Data Mining and Big Data (DMBD'2022) serves as an international forum for researchers and practitioners to exchange the latest advantages in theories, algorithms, models, and applications of data mining and big data as well as artificial intelligence techniques.

The DMBD'2022 is the seventh event after the preceding Guangzhou, Belgrade, Chiang Mai, Shanghai, Fukuoka and Bali events where more than hundreds of delegates from all over the world get together and share their latest achievements, innovative ideas, marvellous designs and excel implementations.

The main theme of this year is FinTech, and special attention will be given to technologies and applications in this area. DMBD 2022 aims to collect a wide range of articles from both data mining and its application to finance, it encourages original research papers of high quality that focus on novel ways of using AI techniques to solve financial problems.

For details about the conference, please visit the DMBD official website at https://iasei.org/dmbd2022

DMBD 2022 is technically co-sponsored and supported by RRCS. RRCS is an International, peer-reviewed, open-access journal of computer science, published biannually online by Universal Wiser Publisher (UWP). Click here to learn more about how to submit to RRCS. 

Topics of interest at the conference are as follows:

1. FinTech

Quantitative investment

Market microstructure

Fraud detection

Risk prediction

Credit modelling

Individual financing

Financial theories

Financial activities modelling

Financial intelligent system

2. Data mining

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

3. Big Data

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

4. Applications

Social networks analysis

Data searching and mining

Visualisation 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