DSAA 2018

Event Rating

median	worst

List of all ratings can be found at DSAA 2018/rating

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
Location:	IT/21/Turin, IT/21, IT
Loading map... {"minzoom":false,"maxzoom":false,"mappingservice":"leaflet","width":"280px","height":"280px","centre":false,"title":"","label":"","icon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"zoom":false,"defzoom":14,"layers":["OpenStreetMap"],"image layers":[],"overlays":[],"resizable":false,"fullscreen":false,"scrollwheelzoom":true,"cluster":false,"clustermaxzoom":20,"clusterzoomonclick":true,"clustermaxradius":80,"clusterspiderfy":true,"geojson":"","clicktarget":"","imageLayers":[],"locations":[{"text":"","title":"","link":"","lat":45.0677551,"lon":7.6824892,"icon":""}],"imageoverlays":null}
Committees
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.