LDMTA 2012 - The 4th Workshop on Large Scale Data Mining: Theory and Applications

With advances in data collection and storage technologies, large data sources have become ubiquitous. Today, organizations routinely collect terabytes of data on a daily basis with the intent of gleaning non-trivial insights on their business processes. To benefit from these advances, it is imperative that data mining and machine learning techniques scale to such proportions. Such scaling can be achieved through the design of new and faster algorithms and/or through the employment of parallelism. Furthermore, it is important to note that emerging and future processor architectures (like multi-cores) will rely on user-specified parallelism to provide any performance gains. Unfortunately, achieving such scaling is non-trivial and only a handful of research efforts in the data mining and machine learning communities have attempted to address these scales.
At the other end of the spectrum, the past few years have witnessed the emergence of several platforms for the implementation and deployment of large-scale analytics. Examples of such platforms include Hadoop (Apache), Hyracks (UCI) and Dryad (Microsoft). These platforms have been developed by the large-scale distributed processing community and can not only simplify implementation but also support execution on the cloud making large-scale machine learning and data mining both affordable and available to all. Today, there is a large gap between the data mining/machine learning and the large scale distributed processing communities. To make advances in large-scale analytics it is imperative that both these communities work hand-in-hand. The intent of this workshop is to further research efforts on large-scale data mining and to encourage researchers and practitioners to share their studies and experiences on the implementation and deployment of scalable data mining and machine learning algorithms.
http://arnetminer.org/LDMTA2012
Call for papers
Topics of interest
Systems and frameworks for large-scale data mining
Methodologies for large-scale data mining
Scalable data mining algorithms and systems over multiple (heterogeneous) data sources
Scalable data preprocessing and cleaning techniques
Scalable mining systems in finance, sciences, retail, e-commerce
Exploiting modern and specialized hardware such as multi-core processors, GPUs, STI Cell processor, FPGAs, etc
Emerging applications of large-scale data mining, such as climate modeling, medical informatics
Scalable learning and mining for large graph data sets
Empirical study of data mining algorithms and applications
Parallel data mining methods and applications
Web mining and social search applications
Streaming data algorithms for machine learning and data mining
Important dates and guidelines
Submission deadline: May 9th, 2012
Notification of acceptance: June 1st, 2012
Final papers due: June 8th, 2012
All submitted papers should have a maximum length of 8 pages and must be prepared as per instructions provided at http://sigkdd.org/kdd2012/author_instructions.shtm.... Authors are required to submit their papers electronically in PDF format. All submissions should clearly present the names of authors, their affiliations, and their emails.
Submission site is located at https://www.easychair.org/conferences/?conf=ldmta2...