Abbrevation
BigMine
City
London
Country
UK
Deadline Paper
Start Date
End Date
Abstract

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&#046; Novel ideas, controversial issues, open problems and comparisons of competing approaches are strongly encouraged&#046; Representation of alternative viewpoints and discussions are also strongly encouraged&#046;<br>We invite submission of papers describing innovative research on all aspects of big data mining&#046; Work&#8211;in&#8211;progress papers, demos, and visionary papers are also welcome&#046;<br>Papers emphasizing theoretical foundations, algorithms, systems, applications, language issues, data storage and access, architecture are particularly encouraged&#046;<br>Topics<br>Examples of topics of interest include<br>&#046; Scalable, Distributed and Parallel Algorithms<br>&#046; Designing light­weighted data mining algorithm<br>&#046; New Programming Model for Large Data beyond Hadoop/MapReduce, STORM, streaming languages<br>&#046; Federated training using data from multiple devices<br>&#046; Mining Algorithms of Data in non&#8211;traditional formats (unstructured, semi&#8211;structured)<br>&#046; Applications: social media, Internet of Things, Smart Grid, Smart Transportation Systems<br>&#046; Streaming Data Processing<br>&#046; Heterogeneous Sources and Format Mining<br>&#046; Privacy issues of on&#8211;­device user data<br>&#046; Systems Issues related to large datasets: clouds, streaming system, architecture, and issues beyond cloud and streams&#046;<br>&#046; System and platform for on­&#8211;device learning<br>&#046; Interfaces to database systems and analytics&#046;<br>&#046; Evaluation Technologies<br>&#046; Integrating the design of human ­computer interaction with machinelearning algorithms<br>&#046; Visualization for Big Data<br>&#046; Applications: Large scale recommendation systems, social media systems, social network systems, scientific data mining, environmental, urban and other large data mining applications&#046;<br>