Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Such datasets are often from various sources (Variety) yet unstructured such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions and web logs, and are of large size (Volume) with fast data in/out (Velocity). More importantly, big data has to be of high value (Value) and establish trust in it for business decision making. The IEEE International Conference on Big Data Science and Engineering 2022 will be held in October 2022, Wuhan, China. The goal of this conference is to promote community-wide discussion for identifying advanced applications, technologies and theories for big data. We seek submissions of papers that invent novel techniques, investigate new applications, introduce advanced methodologies, propose promising research directions and discuss approaches for unsolved issues.

The topics include, but are not limited to the following:
• Trust semantics, metrics and models
• Systems, models and algorithms
• Big data novel theories, algorithms and applications
• Big data standards
• Big data mining and analytics
• Big data infrastructure, MapReduce and cloud computing
• Big data visualization
• Big data curation and management
• Big data semantics, scientific discovery and intelligence
• Big data performance analysis and large-scale deployment
• Security, privacy, trust, and legal issues to big data
• Big data vs. big business and big industry
• Large data stream processing on cloud
• Large incremental datasets on cloud
• Distributed and federated datasets
• NoSQL data storage and DB scalability
• Big data placement, scheduling, and optimization
• Distributed file systems for big data
• MapReduce for big data processing, resource scheduling and SLA
• Performance characterization, evaluation and optimization
• Simulation and debugging systems and tools for MapReduce and big data
• Multiple source data processing and integration with MapReduce
• Storage and computation management of big data
• Large-scale big data workflow management
• Mobility for big data
• Sensor network and social network for big data
• Big data applications