The objective of this one-day workshop is to investigate opportunities in accelerating data management systems and workloads (which include traditional OLTP, data warehousing/OLAP, ETL, Streaming/Realtime, and XML/RDF Processing) using various processor architectures  (e.g., commodity and specialized Multi-core CPUs, Many-core GPUs, and FPGAs), storage systems (e.g., Storage-class Memories like SSDs and Phase-change Memory), and multicore programming strategies like OpenCL.

More information and the full call can be found here: http://www.adms-conf.org/

From the call for papers:

The current data management scenario is characterized by the following trends: traditional OLTP and OLAP/data warehousing systems are being used for increasing complex workloads (e.g., Petabyte of data, complex queries under real-time constraints, etc.); applications are becoming far more distributed, often consisting of different data processing components; non-traditional domains such as bio-informatics, social networking, mobile computing, sensor applications, gaming are generating growing quantities of data of different types; economical and energy constraints are leading to greater consolidation and virtualization of resources; and analyzing vast quantities of complex data is becoming more important than traditional transactional processing.

At the same time, there have been tremendous improvements in processor and memory technologies. Newer processors are more capable in the CPU and memory capabilities and are optimized for multiple application domains. Commodity systems are increasingly using multi-core processors with more than 4 cores per chip and enterprise-class systems are using processors with at least 32 cores per chip. Specialized multi-core processors such as the Cell and many-core GPUs have brought the computational capabilities of supercomputers to cheaper commodity machines. On the storage front, FLASH-based solid state devices (SSDs) are becoming smaller in size, cheaper in price, and larger in capacity. Exotic technologies like Phase-change memory are on the near-term horizon and can be game-changers in the way data is stored and processed.

In spite of the trends, currently there is limited usage of these technologies in data management domain. Naive usage of multi-core processors or SSDs often leads to unbalanced system. It is therefore important to evaluate applications in a holistic manner to ensure effective utilization of processor and memory resources. This workshop aims to understand impact of modern hardware technologies on accelerating core components of data management workloads. Specifically, the workshop hopes to explore the interplay between overall system design, core algorithms, query optimization strategies, programming approaches, performance modelling and evaluation, etc., from the perspective of data management applications.

The suggested topics of interest include, but are not restricted to:

• Hardware and System Issues in Domain-specific Accelerators
• New Programming Methodologies for Data Management Problems on Modern Hardware
• Query Processing for Hybrid Architectures
• Autonomic Tuning for Data Management Workloads on Hybrid Architectures
• Algorithms for Accelerating Multi-modal Multi-tiered Systems
• Energy Efficient Software-Hardware Co-design for Data Management Workloads
• Parallelizing non-traditional (e.g., graph mining) workloads
• Algorithms and Performance Models for modern Storage Sub-systems
• Data Layout Issues for Modern Memory and Storage Hierarchies
• New Benchmarking Methodologies for Storage-class Memories