Big Data Mining (KDD BigMine-19) th International Workshop on Big Data on Streams 0 1 2 3 4 5 Workshop Date: Aug 0 1 2 3 4 5 Anchorage 0 1 2 3 4 5 Alaska - USA https://bigmine.github.io/bigmine 0 1 2 3 4 5 Workshop Chairs 0 1 2 3 4 5 Publicity Chair 0 1 2 3 4 5 Steering Committee 0 1 2 3 4 5 Wei Fan 0 1 2 3 4 5 Tencent 0 1 2 3 4 5 Albert Bifet, Telecom ParisTech; Carlos Abreu Ferreira, University of Porto , Porto , Portugal Eugene Ryan , Eaton, Dublin , Ireland Jesse Read, cole Polytechnique , Paris , France Jo~ao Gama, University of Porto , Porto , Portugal Michele Berlingerio , Eaton, Dublin , Ireland Qiang Yang, Hong Kong University of Science and Technology -

The goal of the workshop is to provide a forum to discuss important research questions and practical challenges in big data mining and related areas. Novel ideas, controversial issues, open problems and comparisons of competing approaches are strongly encouraged. Representation of alternative viewpoints and discussions are also strongly encouraged.

We invite submission of papers describing innovative research on all aspects of big data mining. Work-in-progress papers, demos, and visionary papers are also welcome.

Papers emphasizing theoretical foundations, algorithms, systems, applications, language issues, data storage and access, architecture are particularly encouraged.

Topics

 Examples of topics of interest include:

Scalable, Distributed and Parallel Algorithms; Designing light-weighted data mining algorithm; New Programming Model for Large Data beyond Hadoop/MapReduce, STORM, streaming languages; Federated training using data from multiple devices; Mining Algorithms of Data in non-traditional formats (unstructured, semistructured);

 Streaming Data Processing;

Heterogeneous Sources and Format Mining;

 Privacy issues of on-device user data;

Systems Issues related to large datasets: clouds, streaming system, architecture, and issues beyond cloud and streams; System and platform for on-device learning; Interfaces to database systems and analytics;

 Evaluation Technologies; Integrating the design of human computer interaction with machine learning algorithms; 2 Visualization for Big Data; Applications: Large scale recommendation systems, social media systems, social network systems, scienti c data mining, environmental, urban and other large data mining applications.

We would like to thank the authors, and the program committee members:

 Georges Hebrail, EDF Lab Saclay; Geo rey Holmes, University of Waikato; Ana Rita Nogueira, INESC TEC; Shazia Tabassum, University of Porto; Herna Viktor, University of Ottawa; Thiago Andrade, INESC TEC;

Gianmarco De Francisci Morales, ISI Foundation;

 Jesse Read, Ecole Polytechnique; Mario Cordeiro, ProDEI; Frederic Stahl, University of Reading;

Chaitanya Manapragada, Monash University;

 Ricardo Sousa, INESC TEC; Fabiola Pereira, University of So Paulo; Xian Wu, Shanghai Jiao Tong University;

Le Gruenwald, The University of Oklahoma;

 Bruno Veloso, INESC TEC;

Elaine Faria, Federal University of Uberlandia;

 Maria Pedroto, University of Porto;

So a Fernandes, Laboratory of Arti cial Intelligence and Decision Support; Demetrios Zeinalipour-Yazti, University of Cyprus; Hanghang Tong, Arizona State University;

 Hasan Timucin Ozdemir, IAC; 3 Michele Berlingerio;

Eugene Ryan; Xingquan Zhu, Florida Atlantic University; Richard Hugh Moulton.