DSAA

DSAA

DSAA
IEEE International Conference on Data Science and Advanced Analytics
Categories: Data science
https://dsaa.co/?page id=1064
Avg. acceptance rate:	0
Avg. acceptance rate (last 5 years):	0
Table of Contents

IEEE International Conference on Data Science and Advanced Analytics (DSAA) has an average acceptance rate of 0% (last 5 years 0%).

Events

There are 3 events of the series DSAA known to this wiki: DSAA 2018, DSAA 2019, DSAA 2020

	Ordinal	Year	From	To	City	Country	presence	Homepage	TibKatId	GND	dblp	WikiCFP	Wikidata	General chair	PC chair
DSAA 2020		2020	Oct 6	Oct 9	Sidney	Australia		http://dsaa2020.dsaa.co/						Geoff Webb Richard De Veaux Usama Fayyad	Mark Zhang Vincent S. Tseng
DSAA 2019		2019	Oct 5	Oct 8	Washington D.C.	USA		http://dsaa2019.dsaa.co/						Philip S. Yu Richard De Veaux	Jeffrey Xu Yu George Karypis Francesco Bonchi Jennifer Hill Roger Hoerl
DSAA 2018	5	2018	Oct 1	Oct 3	Torino	Italy		https://dsaa2018.isi.it/home						Francesco Bonchi Foster Provost	Rayid Ghani Tina Eliassi-Rad Ciro Cattuto

Locations

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DSAA Topics

DSAA encourages research, education/training, development and applications on big data, data science, and advanced analytics, related to topics include, but are not limited to:

• Foundations for Big Data, Data Science and Advanced Analytics
• New mathematical, probabilistic and statistical models and theories
• New learning theories, models and systems
• Deep analytics and learning
• Distributed and parallel computing (cloud, map-reduce, etc.)
• Non-iidness (heterogeneity & coupling) learning
• Invisible structure, relation and distribution learning
• Intent and sight learning
• Scalable analysis and learning
• Information infrastructure, management and processing
• Data pre-processing, sampling and reduction
• Feature selection and feature transformation
• High performance/parallel distributed computing
• Analytics architectures and infrastructure
• Heterogeneous data/information integration
• Crowdsourcing
• Human-machine interaction and interfaces
• Retrieval, query and search
• Web/social web/distributed search
• Indexing and query processing
• Information and knowledge retrieval
• Personalized search and recommendation
• Query languages and user interfaces
• Analytics, discovery and learning
• Mixed-type data analytics
• Mixed-structure data analytics
• Big data modeling and analytics
• Multimedia/stream/text/visual analytics
• Coupling, link and graph mining
• Personalization analytics and learning
• Web/online/network mining and learning
• Structure/group/community/network mining
• Big data visualization analytics
• Large scale optimization
• Privacy and security
• Security, trust and risk in big data
• Data integrity, matching and sharing
• Privacy and protection standards and policies
• Privacy preserving big data access/analytics
• Social impact
• Evaluation, applications and tools
• Data economy and data-driven lousiness model
• Domain-specific applications
• Quality assessment and interestingness metrics
• Complexity, efficiency and scalability
• Anomaly/fraud/exception/change/event/crisis analysis
• Large-scale recommender and search systems
• Big data representation and visualization
• Post-processing and post-mining
• Large scale application case studies
• Online/business/government data analysis
• Mobile analytics for handheld devices
• Living analytics