DSAA 2018

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DSAA 2018
5th IEEE International Conference on Data Science and Advanced Analytics
Ordinal 5
Event in series DSAA
Dates 2018/10/01 (iCal) - 2018/10/03
Homepage: https://dsaa2018.isi.it/home
Location: Torino, Italy
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Organizers: Laetitia Gauvin, Michele Tizzoni
General chairs: Francesco Bonchi, Foster Provost
PC chairs: Tina Eliassi-Rad, Ciro Cattuto, Rayid Ghani
Seminars Chair: Gabriella Pasi, Richard De Veaux
Table of Contents

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.