Call for Papers

Modern automatic systems are able to collect huge volumes of data, often with a complex structure (e.g. multi-table data, semi-structured data, web data, time series and sequences, graphs and trees).
The massive and complex data pose new challenges for current research in Knowledge Discovery and Data Mining. They require new methods for storing, managing and analyzing them by taking into account various complexity aspects:
Complex structures (e.g. multi-relational, time series and sequences, networks, and trees) as input/output of the data mining process; Massive amounts of high dimensional data collections flooding as high-speed streams and requiring (near) real time processing and model adaptation to concept drifts; New application scenarios involving security issues, interaction with other entities and real-time response to events triggered by sensors.
The purpose of the workshop is to bring together researchers and practitioners of data mining and machine learning interested in analysis of complex data.
We welcome submissions focusing on recent advances and latest developments in the analysis of complex and massive data sources such as blogs, event or log data, medical data, spatio-temporal data, social networks, mobility data, sensor data and streams. Submissions discussing and introducing new algorithmic foundations and representation formalisms in complex pattern discovery are also welcome. We encourage submissions from the areas of statistics, learning and big data analytics, which present techniques that take advantage of the informative richness of complex massive data for efficiently and effectively identifying new patterns.
Finally, submissions describing preliminary, promising studies and recently accepted papers are also welcome.

The workshop will take place on
*** September 16, 2019 (W¨zburg, Germany) ***

Topics of Interest

A non-exclusive list of topics for the complex pattern mining research is reported in the following:
Methodologically, the solutions can be rooted in different research fields, such as, pattern mining, neural networks and deep learning, probabilistic inference, decision trees, rule learning, reinforcement learning etc.