Call for Participation: IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018), Turin, Italy (Oct 1-4)

The IEEE International Conference on Data Science and Advanced Analytics (DSAA) aims to be the flagship annual meeting spanning the interdisciplinary field of Data Science. DSAA focuses on the science of data science, as well as the implications of the science for applications to industry, government, and society. From the science side, DSAA spans all of the component fields of data science, including statistics, probabilistic and mathematical modeling, machine learning, data mining and knowledge discovery, complexity science, network science, business analytics, data management, infrastructure and storage, retrieval and search, security, privacy and ethics. From the applications side, DSAA aims both to show researchers important problems and issues that are revealed by real applications, and to show practitioners and users how the science can be applied to realize value. DSAA is intended to reflect the interdisciplinary nature of data science and analytics, as an alternative to the highly specialized disciplinary conferences.

DSAA 2018 in Turin, Italy, is the 5th annual installment of the conference. This year brings the collaboration of the American Statistical Association, to complement the IEEE Computational Intelligence Society and the ACM SIGKDD. This year DSAA also adds the support of the ISI Foundation, which has worked for 35 years to break down traditional silos in the sciences of complexity and data.

Topics of interest include but are not limited to:

Foundations

• Mathematical, probabilistic and statistical models and theories.
• Machine learning theories, models and systems.
• Knowledge discovery theories, models and systems.
• Manifold and metric learning.
• Deep learning and deep analytics.
• Scalable analysis and learning.
• Non-iid learning.
• Heterogeneous data/information integration.
• Data pre-processing, sampling and reduction.
• Dimensionality reduction.
• Feature selection, transformation and construction.
• Large scale optimization.
• High performance computing for data analytics.
• Learning for streaming data.
• Learning for structured and relational data.
• Latent semantics and insight learning.
• Mining multi-source and mixed-source information.
• Mixed-type and structure data analytics.
• Cross-media data analytics.
• Big data visualization, modeling and analytics.
• Multimedia/stream/text/visual analytics.
• Relation, coupling, link and graph mining.
• Personalization analytics and learning.
• Web/online/social/network mining and learning.
• Structure/group/community/network mining.
• Cloud computing and service data analysis.

Management, storage, retrieval and search

• Cloud architectures and cloud computing.
• Data warehouses and large-scale databases.
• Memory, disk and cloud-based storage and analytics.
• Distributed computing and parallel processing.
• High performance computing and processing.
• Information and knowledge retrieval, and semantic search.
• Web/social/databases query and search.
• Personalized search and recommendation.
• Human-machine interaction and interfaces.
• Crowdsourcing and collective intelligence.

Theoretical Foundations for Social issues

• Data science meets social science.
• Security, trust and risk in big data.
• Data integrity, matching and sharing.
• Privacy and protection standards and policies.
• Privacy preserving big data access/analytics.
• Fairness and transparency in data science.

Further information: https://dsaa2018.isi.it/home