ISSN: 2308-4464
ISBN: 978-1-61208-816-7

• Registered: with the Library of Congress of the United States of America (ISSN)
• Hosted: by Technische Informationsbibliothek (TIB) - German National Library of Science and Technology (Open Access)
• Free Access: in ThinkMind Digital Library

DATA ANALYTICS 2020 is colocated with the following events as part of NexTech 2020 Congress:

• UBICOMM 2020, The Fourteenth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies
• ADVCOMP 2020, The Fourteenth International Conference on Advanced Engineering Computing and Applications in Sciences
• SEMAPRO 2020, The Fourteenth International Conference on Advances in Semantic Processing
• AMBIENT 2020, The Tenth International Conference on Ambient Computing, Applications, Services and Technologies
• EMERGING 2020, The Twelfth International Conference on Emerging Networks and Systems Intelligence
• DATA ANALYTICS 2020, The Ninth International Conference on Data Analytics
• GLOBAL HEALTH 2020, The Ninth International Conference on Global Health Challenges
• CYBER 2020, The Fifth International Conference on Cyber-Technologies and Cyber-Systems

DATA ANALYTICS 2020 Steering Committee

		Wolfram Wöß Institute for Application Oriented Knowledge Processing | Johannes Kepler University, Linz Austria			Kerstin Lemke-Rust Hochschule Bonn-Rhein-Sieg Germany
		George Tambouratzis Institute for Language and Speech Processing, Athena R.C. Greece			Les Sztandera Thomas Jefferson University USA
		Ivana Semanjski Ghent University Belgium			Sandjai Bhulai Vrije Universiteit Amsterdam The Netherlands

DATA ANALYTICS 2020 conference tracks:

Fundamentals for data analytics

Tools, frameworks and mechanisms for data analytics; Open API for data analytics; In-database analytics; Pre-built analytics (pattern, time-series, clustering, graph, statistical analysis, etc.); Analytics visualization; Multi-modal support for data analytics; Google/FaceBook/Twitter/etc. analytics; High-performance data analytics

Advanced topics in Deep/Machine learning

Distributed and parallel learning algorithms; Image and video coding; Deep learning and Internet of Things; Deep learning and Big data; Data preparation, feature selection, and feature extraction; Error resilient transmission of multimedia data; 3D video coding and analysis; Depth map applications; Machine learning programming models and abstractions; Programming languages for machine learning; Visualization of data, models, and predictions; Human behavioral predictions; Hardware-efficient machine learning methods; Model training, inference, and serving; Trust and security for machine learning applications; Testing, debugging, and monitoring of machine learning  applications; Machine learning for systems.

Specific Machine Learning approaches and Data Processing

Machine learning models (supervised, unsupervised, reinforcement, constrained, etc.); Generative modeling (Gaussian, HMM, GAN, Bayesian networks, autoencoders, etc.); Explainable AI (feature importance, LIME, SHAP, FACT, etc.); Bayesian learning models; Prediction uncertainty (approximation learning, similarity); Training of models (hyperparameter optimization, regularization, optimizers); Active learning (partially labels datasets, faulty labels, semi-supervised); Applications of machine learning (recommender systems, NLP, computer vision, etc.); Data in machine learning (no data, small data, big data, graph data, time series, sparse data, etc.)

Mechanisms and features

Scalable data analytics; Big data analytics; Deep data analytics; Mass data analytics; Storing, dropping and filtering data; Relevant/redundant/obsolete data analytics; Volume vs. semantics analytics; Nomad analytics; Predictive analytics; Trust in data analytics; Legal issues analytics; Failure on data analytics

Sentiment/opinion analysis

Architectures for generic sentiment analysis systems; Sentiment analysis techniques on social media; Document-level analysis; Sentence-level analysis; Aspect-based analysis; Comparative-sentiment analysis; Sentiment lexicon acquisition; Optimizing sentiment analysis algorithms; Applications of sentiment analysis.

Application-oriented analytics

Statistical applications; Simulation applications; Crawling web services; Cross-database analytics; Forecast analytics; Financial risk management; ROI analytics

Target analytics

Business analytics; Malware analytics; Cyber-threats analytics; Mining user logs; Reputation analytics; User choice analytics; Branding analytics; Utility proximity-search analytics; Survey-based online asset analytics; Online employment analytics; Geology analytics; Global climate analytics; Remote learning analytics; Homecare analytics; Population growth and migration analytics; Food-borne illness outbreaks analytics

Big Data

Foundational models for Big Data; Big Data Analytics and Metrics; Big Data processing and management; Big Data search and mining; Big Data platforms; Big Data persistence and preservation; Big Data and social networks; Big Data economics

Huge data

Knowledge Discovery from Huge Data; Computational Intelligence for Huge Data; Linked Huge Data; Security Intelligence with Huge Data

Deadlines: