DSAA

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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

	OrdinalThis property of the datatype Number represents the ordinal number of an event within an event series. Thereby it informs about the age of an event series. This property is not needed for the DOI registration process via DataCite and is optional.	Year	FromThis property is of the datatype Date and it is being used to provide the start date of an academic event or a project.</br>This property is aligned with icaltzd:dtstart. It is a mandatory property when describing an academic event and it is needed for the DOI registration process via DataCite.	ToThis property is of the datatype Date and it is being used to provide the end date of an academic event or a project.</br>This property is aligned with icaltzd:dtend.</br>It is a mandatory property when describing an academic event and it is needed for the DOI registration process via DataCite.	CityThe property Has location city can be used to specify the city where a street, building, event, etc. is located in.</br>It is of the datatype Page and a special case of the Property:Located in. Other properties for specifying locations are: property:Has location country, property:Has location state and property:Has location address.</br>When specifying the city in which an academic event takes or took place, using this property is not needed for the DOI registration process via DataCite but strongly recommended.	CountryThe property Has location country is used to describe the country where something is located in.</br>It is of the datatype Page and a special case of the Property:Located in. Other properties for specifying locations are: property:Has location city, property:Has location state and property:Has location address.</br>When specifying the country in which an academic event takes or took place, using this property is not needed for the DOI registration process via DataCite but strongly recommended.	presence	HomepageThis property is of the datatype URL and it is being used to provide the official website of an academic event or an event series.</br>It is a recommended property when describing an academic event or event series and it is not needed for the DOI registration process via DataCite.	TibKatId	GNDThis property of the datatype External identifier is used to provide the identifier with which an entity is indexed in the Integrated_Authority_File (GND). Its external formatter URI is http://d-nb.info/gnd/$1.</br>In Open Research it is mostly used to identify an academic event or event series within the GND. </br>The use of this property is optional.</br>It is not necessary for the DOI registration process via DataCite.	dblpThis property of the datatype External identifier is used to provide the identifier with which an entity is indexed in dblp. Its external formatter URI is https://dblp2.uni-trier.de/db/conf/$1.</br>In Open Research it is mostly used to identify an academic event within dblp. </br>The use of this property is optional. It is not necessary for the DOI registration process via DataCite.	WikiCFPThis property of the datatype External identifier is used to provide the identifier with which an entity is indexed in WikiCFP. Its external formatter URI is http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=$1.</br>In Open Research it is used to identify an academic event or event series within WikiCFP. </br>The use of this property is optional. It is not necessary for the DOI registration process via DataCite.	WikidataThis property of the datatype External identifier is used to provide the identifier with which an entity is indexed in Wikidata. Its external formatter URI is https://www.wikidata.org/entity/$1.</br>In Open Research it is mostly used to identify an academic event or event series within Wikidata. </br>The use of this property is optional. It is not necessary for the DOI registration process via DataCite.
DSAA 2020		2020	Oct 6	Oct 9	US/OH/Sidney	US		http://dsaa2020.dsaa.co/
DSAA 2019		2019	Oct 5	Oct 8	US/DC/Washington, D.C.	US		http://dsaa2019.dsaa.co/
DSAA 2018	5	2018	Oct 1	Oct 3	IT/21/Turin	IT		https://dsaa2018.isi.it/home

Locations

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DASS Profile

The DSAA conference was initially established in 2014, jointly technically sponsored by IEEE and ACM. Since 2015, DSAA has been fully sponsored by IEEE, becoming an IEEE conference, and co-sponsored by ACM, in addition to sponsorship from IEEE Big Data Initiative. In 2016, the American Statistical Association (ASA) officially sponsors DSAA; DSAA becomes the only data science event that is jointly sponsored by ACM, IEEE and ASA.

IEEE DSAA’2020 will be held in Sydney, Australia (6-9 Oct 2020); IEEE DSAA’2019 was held in Washington DC, USA (5-8 Oct 2019); IEEE DSAA’2018 was held in Turin, Italy (1-4 Oct 2018); IEEE DSAA’2017 was held in Tokyo, Japan (19-21 Oct 2017); IEEE DSAA’2016 was held in Montreal, Canada (17-19 Oct 2016); IEEE DSAA’2015 was held in Paris, France (19-21 Oct 2015), with about 1/3 industrial participants of a total of over 250 and several exhibition booths organized; and DSAA’2014 was held in Oct 2014 in Shanghai, China (30 Oct-1 Nov 2014), with over 200 participants from over 40 countries.

DSAA has firmly established itself as the premier forum in the area of data science, big data, advanced analytics, statistics, and machine learning for industry, government and academic participants. This is ensured by such features as a very competitive acceptance rate (about 10%) for regular papers, high profile core function chairs, 10 pages in IEEE double-column format by double-blind review, interdisciplinary and cross-domain engagement from statistics, industry, and government...

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