DSAA 2018 - 2018 IEEE International Conference on Data Science and Advanced Analytics (DSAA'2018)

The Program Committee of the 2018 IEEE International Conference on Data Science and Advanced Analytics (DSAA-2018) invites the submission of technical papers for two main tracks: Research and Applications, and a series of Special Sessions of the conference which will be held in Turin, Italy October 1 - 4, 2018. DSAA solicits both theoretical and applied works on data science and advanced analytics. All submissions will be reviewed by the Program Committee on the basis of technical quality, relevance to conference topics of interest, originality, significance, and clarity. All accepted papers, including in main tracks and special sessions, will be published by IEEE and will be submitted for inclusion in the IEEE Xplore Digital Library. The conference proceedings will be submitted for EI indexing through INSPEC by IEEE. Top quality papers accepted and presented at the conference will be selected for extension and invited to the special issues of International Journal of Data Science and Analytics (JDSA, Springer).
What is IEEE DSAA
The IEEE International Conference on Data Science and Advanced Analytics (DSAA) aims to be the flagship annual meeting spanning the interdisciplinary field of Data Science. DSAA focuses on the science of data science, as well as the implications of the science for
DSAA-2018 in Turin, Italy, is the 5th annual installment of the conference. This year brings the full collaboration of the American Statistical Association, to complement the IEEE Computational Intelligence Society and the ACM SIGKDD. This year DSAA also adds the support of the ISI Foundation, which has worked for 35 years to break down traditional silos in the sciences of complexity and data.
DSAA-2018 aims to provide a premier forum that brings together researchers, industry and government practitioners, as well as developers and users of big data business for exchange of ideas on the latest theoretical developments in Data Science as well as on the best practice for a wide range of applications.
Research Track
The Research Track is aimed at collecting the latest, original (not previously published nor under consideration at any other venue) and significant contributions related to foundations and theoretical developments of Data Science and Analytics.
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.
Applications Track
The Applications Track is aimed at collecting high-quality, original papers describing applications of Data Science and Advanced Analytics across various disciplines including business, government, physical sciences, and social sciences. We are soliciting submissions on:
Case Studies describing work on a real-world problem using Data Science and Advanced Analytics. The focus of these papers can be on different aspects of the work such as highlighting:
Discoveries that are important and novel for the application domain.
Results of experiments that were designed and conducted as part of the analysis.
Lessons learned while deploying real-world systems containing Data Science and Advanced Analytics.
Challenges encountered while developing and deploying applications of Data Science and Advanced Analytics problem(s) in a domain.
Ethical issues on the use of Data Science and Advanced Analytics and how to handle fairness, equity, and bias concerns when working on real applications.
Infrastructure, Platforms, and Tools that were built to operationalize Data Science and Advanced Analytics. The focus on these papers can be on:
Reusable Libraries and software implementations of Data Science and Advanced Analytics methods.
Describing infrastructure developed to perform Data Science and Advanced Analytics at scale, such as experimentation infrastructure.
Data Science and Advanced Analytics Tools that are in production and are being used by end-users either in a stand-alone capacity or as part of a business process.
Special topics: We are open to papers that do not fit the above topics but would be of interest to practitioners of Data Science and Advanced Analytics, or would highlight new challenges for researchers driven by application areas. These topics include:
Data Science and Advanced Analytics Training for industry, governments, and students.
Posing research grand challenges that are abstracted out of one or more application domains.
Submissions for all categories above should very clearly specify the problem being solved, what methodologies were used to solve the problem, what data was used, how the results were evaluated, and how the solution is being used (ideally in production). Applying new data science methods to public data or data downloaded from competition sites (such as kaggle), without a real problem (and problem owner) will not be accepted in this track.
We are looking for submissions that span problem domains from industry, government, non-profits, and researchers in different disciplines including, but not limited to:
Healthcare, Medicine, Public Health, Life Sciences, etc.
Financial Services.
Telecommunications.
Search.
E-Commerce.
Marketing and Advertising.
Education.
Criminal Justice and Public Safety.
Urban Infrastructure.
Sustainability and Environment.