Workshop on Parallel and Distributed Computing for Knowledge Discovery in Data Bases (PDCKDD 2015)

as part of the

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
(ECML/PKDD 2015)

Call for Papers
Important dates
Workshop invited speakers:

Albert Bifet (The University of Waikato, New Zealand)
Real-Time Big Data Stream Analytics

João Gama (University of Porto)
Distributed data stream mining

Inês Dutra (University of Porto)
Accelerating Machine Learning Tasks using Multiprocessors and GPUs

The number of very large data repositories (big data) is increasing in a rapid pace. Analysis of such repositories requires, using the "traditional" sequential implementations of ML and statistical algorithms, expensive computational resources and long running times. Parallel or distributed computing is one possible approaches that can make analysis of very large repositories feasible. Taking advantage of a parallel or a distributed execution a ML/statistical system may: i) increase its speed; ii) search a larger space and reach a better solution or; iii) increase the range of applications where it can be used (because it can process more data, for example). Parallel and distributed computing is therefore of high importance for Knowledge Discovery in Databases (KDD) practitioners. The workshop will be concerned with the exchange of experience among researchers that use parallel or distributed computing within KDD. Researchers will present recently developed algorithms/systems, on going work and applications taking advantage of such parallel or distributed environments.

This page is maintained by Rui Camacho (