- 3rd Workshop on Parallel Programming for Analytics ApplicationsPPAA - cancelled

The PPAA 2016 workshop has been cancelled.

Motivation and Scope:

Analytics applications are scaling rapidly in terms of the size and variety of data analyzed, the complexity of models explored and tested, and the number of analytics professionals or data scientists supported concurrently. Consumer behavior modeling, IT infrastructure security and resiliency, and fraud detection and prevention are examples of application areas where the scaling is stressing the computational capabilities of current systems. At the same time hardware systems are embracing new technologies like on-chip and off-chip accelerators, vector extensions to the instruction sets, and solid state disks. New programming methodologies and run-times to support them are emerging to facilitate the development of the new analytics applications, and to leverage the emerging systems. This workshop provides a forum for the applications community, run-time and development-environment community, and systems community to exchange the outlook for progress in each of these areas, and exchange ideas on how to cross leverage the progress. Topics of interest include, but are not limited to:

• System and hardware support for big data analytics
• Exploitation of GPUs, FPGAs and on-chip vector processing units for analytics applications
• Efficient exploitation of the memory hierarchy, particularly solid state disks
• Parallel I/O to support distributed file systems
• System management issues for attaining the desired levels of reliability and performance for the above
• Parallel run-times and middleware for analytics
• Columnar databases, large data warehouses, data cubes and OLAP engines
• In memory analysis for real-time queries on large data
• No-SQL databases
• Graph databases
• Concurrency in large tabular data analytics
• Distributed file systems
• Parallel programming models and languages, and application development frameworks for analytics
• Application Frameworks for large graph applications
• Computational models and programming languages for large graph applications
• Domain specific languages for analytics
• Parallel algorithms for large graphs and other big data analytics applications
• Algorithms to exploit the hardware, run-times, middleware and programming models listed above
• Performance attainable on the hardware, run-times, middleware and programming models listed above
• Parallelism in Social Media and other big data applications
• Applications in consumer modeling and customer behavior
• Financial fraud detection and intrusion detection in IT infrastructure
• Applications in healthcare and other industries
• Analytics applications and solutions in homeland security

Call for Papers:

You are seeking submissions that cover research and/or experience aspects on topics relevant to the workshop. Each submission will be reviewed by the workshop program committee. Selected submissions will be invited to present at the workshop and be published in the workshop proceedings.

Submission Guidelines:

Submitted papers should be no longer than 8 pages using a 10 point font (single spaced). Authors are encouraged to use the ACM double column format found at http://www.sigplan.org/Resources/Author/. Papers should be submitted in PDF format and should be legible when printed on a black-and-white printer. To submit, send email to: manoj1@us.ibm.com and joefon@us.ibm.com with the paper included as an attachment. Accepted papers will be published in the ACM digital library after the workshop.