A. Data Science 
Topics of particular interest include, but are not limited to:

• Data sensing, fusion and mining

• Data representation, dimensionality reduction, processing and proactive service layers

• Stream data processing and integration

• Data analytics and new machine learning theories and models

• Knowledge discovery from multiple information sources

• Statistical, mathematical and probabilistic modeling and theories

• Information visualization and visual data analytics

• Information retrieval and personalized recommendation

• Data provenance and graph analytics

• Parallel and distributed data storage and processing infrastructure

• MapReduce, Hadoop, Spark, scalable computing and storage platforms

• Security, privacy and data integrity in data sharing, publishing and analysis

• Big Data, data science and cloud computing

• Innovative applications in business, finance, industry and government cases   
B. Data Systems 
Topics of particular interest include, but are not limited to: 
• Data-intensive applications and their challenges

• Scalable computing platform such as Hadoop and Spark

• Storage and file systems

• High performance data access toolkits

• Fault tolerance, reliability, and availability

• Meta-data management

• Remote data access

• Programming models, abstractions for data intensive computing

• Compiler and runtime support

• Data capturing, management, and scheduling techniques

• Future research challenges of data intensive systems

• Performance optimization techniques

• Replication, archiving, preservation strategies

• Real-time data intensive systems

• Network support for data intensive systems

• Challenges and solutions in the era of multi/many-core platforms

• Stream data computing

• Green (Power efficient) data intensive systems

• Security, Privacy and Trust in Data

• Data intensive computing on accelerators and GPUs

• HPC system architecture, programming models and run-time systems for data intensive applications

• Productivity tools, performance measuring and benchmark for data intensive systems

• Big Data, cloud computing and data intensive systems

• Innovative data intensive applications such as Health, Energy, Cybersecurity, Transport, Food, Soil and Water, Resources, Advanced Manufacturing, Environmental Change, and etc.