|
Innovations in data analytics and machine learning are key drivers for the digitalization of virtually all aspects of our everyday life. They help us to extract knowledge from large amounts of structured and unstructured data, support (automated) decision-making, generate novel business models in industry, foreshadow personalized health technologies, and transform scientific research fields thanks to the availability of new types of data and methods. The widespread adoption of data science creates exciting challenges across all areas of computer science - including artificial intelligence, database technologies, distributed systems, computer architecture, software engineering, algorithm design, and theory – but also raises novel societal challenges like privacy, fairness, transparency and accountability.
The Data Science track of INFORMATIK 2019, the annual convention of the Gesellschaft für Informatik e.V . (GI), aims to give an overview of all aspects related to modelling, analysis, knowledge extraction, and learning from Big data, with an emphasis on recent works from the German, Austrian, and Swiss research community. More information on INFORMATIK 2019 can be found at: https://www.informatik2019.de
*Submissions*
We welcome submissions of methodological and applied works on data science from academia and industry. We accept English submissions in the following two categories:
1) Regular articles present novel insights and reliable results in one of the areas listed above. Submissions should be max. 14 pages and they must not have been submitted or published elsewhere. All accepted regular articles will be presented in a talk at the conference.
2) Extended abstracts summarize works recently published in a leading international conference or journal in data science (e.g. ACM SIGKDD, NIPS, ICML, AAAI, IEEE TKDE, ICWSM, Machine Learning, ICDM, IEEE TNNLS, EPJ Data Science, WWW, etc.). Submissions should be max. 2 pages and must clearly refer to the original publication. Accepted extended abstracts of such works will be presented during a special session “Best of Data Science made in D/A/CH”.
All contributions should be submitted using the Review System of INFORMATIK2019 and must be formatted according to the guidelines for the GI-Edition "Lecture Notes in Informatics", which are available at www.gi-ev.de/service/publikationen/lni . Each submission is reviewed by at least three members of the track program committee. Details on the program committee can be found on the conference website.
*Topics of Interest*
Soliciting data science applications in academia and industry that cross disciplinary borders, and works that advance the methodological foundation of data science, the topics include, but are not limited to:
- Applications of data analytics and machine learning in the social sciences, humanities, natural sciences, life sciences, and medicine
- Case studies of data science in industry
- Data-driven and agent-based modelling of complex systems across the sciences
- Network science, social network analysis, and graph mining
- Causal inference and reasoning in Big Data
- Fairness, transparency, and explainability in machine learning
- Quantitative studies of societal aspects of Big Data
- Data science and data literacy education in schools, academia, and industry
- Methodological foundations of artificial intelligence and statistical learning
- Anomaly detection, pattern recognition, time series analysis, spatial data analysis
- Natural language processing, text mining and sentiment analysis
- Computer vision, scene analysis, object recognition, medical image analysis
- Web science, linked data, knowledge representation, semantic query languages
- Methods to deal with uncertain, missing, and spurious data
- Scalable storage, retrieval, mining, and analysis of Big Data
- Data Visualisation and Exploration
*Proceedings and Follow-up Journal Publication*
All accepted contributions (regular articles and extended abstracts) will appear in the series Lecture Notes in Informatics (LNI). In addition, authors of selected previously unpublished regular articles will receive an invitation to submit an extended version to the journal EPJ Data Science, where they will be considered for a special issue. The selection will be made by the track co-chairs and the program committee.
*Co-Chairs*
Ingo Scholtes, Universität Zürich
Markus Strohmaier, RWTH Aachen
|
