Difference between revisions of "DSAA"

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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
 +
*

Revision as of 16:28, 25 May 2020

DSAA Download DSAAUpload
DSAA
IEEE International Conference on Data Science and Advanced Analytics
Categories: Data science
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

 OrdinalYearFromToCityCountrypresenceHomepageTibKatIdGNDdblpWikiCFPWikidataGeneral chairPC chair
DSAA 20202020Oct 6Oct 9SidneyAustraliahttp://dsaa2020.dsaa.co/Geoff Webb
Richard De Veaux
Usama Fayyad		Mark Zhang
Vincent S. Tseng
DSAA 20192019Oct 5Oct 8Washington D.C.USAhttp://dsaa2019.dsaa.co/Philip S. Yu
Richard De Veaux		Jeffrey Xu Yu
George Karypis
Francesco Bonchi
Jennifer Hill
Roger Hoerl
DSAA 201852018Oct 1Oct 3TorinoItalyhttps://dsaa2018.isi.it/homeFrancesco Bonchi
Foster Provost		Rayid Ghani
Tina Eliassi-Rad
Ciro Cattuto


Submission/Acceptance

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
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