General Information[edit]

Event series historical notes:

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 *

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