KDCLOUD 2011 - International Workshop on Knowledge Discovery Using Cloud and Distributed Computing Platforms

Website icdm2011.cs.ualberta.ca | Edit Freely

Category KDCLOUD 2011

Deadline: July 23, 2011 | Date: December 10, 2011

Venue/Country: Vancouver, Canada

Updated: 2011-05-29 20:01:00 (GMT+9)

Call For Papers - CFP

Synopsis: Processing large datasets using dedicated supercomputers alone is not an economical solution. Recent trends show that distributed computing is becoming a more practical and economical solution for many organizations. Cloud computing, which is a large-scale distributed computing, has attracted significant attention of both industry and academia in recent years. Cloud computing is fast becoming a cheaper alternative to costly centralized systems. Many recent studies have shown the utility of cloud computing in data mining, machine learning and knowledge discovery. This workshop intends to bring together researchers, developers, and practitioners from academia, government, and industry to discuss new and emerging trends in cloud computing technologies, programming models, and software services and outline the data mining and knowledge discovery approaches that can efficiently exploit this modern computing infrastructures. This workshop also seeks to identify the greatest challenges in embracing cloud computing infrastructure for scaling algorithms to petabyte sized datasets. Thus, we invite all researchers, developers, and users to participate in this event and share, contribute, and discuss the emerging challenges in developing data mining and knowledge discovery solutions and frameworks around cloud and distributed computing platforms.

Topics: The major topics of interest to the workshop include but are not limited to:

Programing models and tools needed for data mining, machine learning, and knowledge discovery

Scalability and complexity issues

Security and privacy issues relevant to KD community

Best use cases: are there a class of algorithms that best suit to cloud and distributed computing platforms

Performance studies comparing clouds, grids, and clusters

Performance studies comparing various distributed file systems for data intensive applications

Customizations and extensions of existing software infrastructures such as Hadoop for streaming, spatial, and spatiotemporal data mining

Applications: Earth science, climate, energy, business, text, web and performance logs, medical, biology, image and video.

Proceedings: Accepted papers will be included in a ICDM Workshop Proceedings volume, to be published by IEEE Computer Society Press, which will also be included in the IEEE Digital Library.