CALL FOR PAPERS
ECDA solicits contributions to theoretical as well as application-oriented research on topics in the realm of data science, with a specific focus on data analytics. With its motto "Multidisciplinary Facets of Data Science", ECDA 2018 specifically emphasizes the important interplay of disciplines involved in data science, most notably statistics and computer science. Topics of interest include, but are not limited to the following:
- Theory and Methods: Big Data; Clustering, Classification, Discrimination and Regression; Data Science; Databases and Data Management; Data Mining, Text and Web Mining; Data Visualization; Dimension Reduction; Image Analysis and Computer Vision; Impact of Technical Revolution and Library Science; Knowledge Representation and Discovery; Machine Learning; Mathematical Foundations of Data Science; Multivariate Methods; Online Algorithms and Algorithms for Data Streams; Social Network Analysis; Statistical and Econometric Methods; Symbolic Data Analysis.
- Applications: Archeology; Biostatistics; Business and Management; Economics; Education; Engineering; Finance; Geosciences; Industrial Automation; Linguistics; Marketing; Medicine and Health Care; Musicology; Psychology; Risk Management; Social Sciences.
Besides, ECDA will feature several special sessions on interesting research themes within the scope of the conference.
- Abstract submission deadline: April 1, 2018
- Notification of abstract acceptance or rejection: April 30, 2018
- Early registration deadline: May 15, 2018
- Registration deadline: June 15, 2018
- Full paper submission: September 15, 2018
Titles and abstracts are submitted in plain text (no LaTeX, no .docx). The length of an abstract should not exceed 300 words. Formatting guidelines for full papers will be provided after the conference.
Submission of an abstract and full paper can be done here.
Accepted abstracts will be published in the book of abstracts and made available at the conference. Accepted full papers will be published in the Journal "Archives of Data Science, Series A" .