[Dbworld] CFP: VLDB Workshop on Accelerating Analytics and Data Management Systems (ADMS'17)

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Call For Participation
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8th International Workshop on Accelerating Analytics and Data
Management Systems Using Modern Processor and Storage Architectures (ADMS 2017)
                               http://www.adms-conf.org/

In conjunction with VLDB 2017 Friday, September 1 2017, Munich, Germany

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Workshop Overview
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The objective of this one-day workshop is to investigate opportunities
in accelerating analytics and data management workloads (which include
traditional OLTP, data warehousing/OLAP, ETL, Streaming/Real-time,
Business Analytics, and XML/RDF Processing) using processors (e.g.,
commodity and specialized Multi-core, GPUs, FPGAs, and ASICs), storage
systems (e.g., Storage-class Memories like SSDs and Phase-change
Memory), programming models like MapReduce, Spark, CUDA, OpenCL,
and OpenACC, and operating environments such as cloud.

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 the CPU
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 6 cores per chip and enterprise-class
systems are using processors with 8 cores per chip, where each core
can execute upto 4 simultaneous threads. Specialized multi-core
processors such as the 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 CPU 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. 


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Topics of Interest
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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
* Large-scale I/O-intensive (Big Data) Applications
* Parallelizing/Accelerating Analytical (e.g., Data Mining) Workloads
* 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
* Exploitation of specialized ASICs
* Novel Applications of Low-Power Processors and FPGAs
* Exploitation of Transactional Memory for Database Workloads
* Exploitation of Active Technologies (e.g., Active Memory, Active Storage, and Networking)
* New Benchmarking Methodologies for Storage-class Memories
* Applications of HPC Techniques for Data Management Workloads
* Acceleration in the Cloud Environments

Every year, we choose a theme around which the keynote or panel sessions are organized. 
This year, the workshop theme is "Accelerating Machine Learning Applications".


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Workshop Co-Chairs
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* Rajesh Bordawekar, IBM T.J. Watson Research Center
* Tirthankar Lahiri, Oracle 

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Program Committee
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* Nipun Agarwal, Oracle Labs
* Christoph Dubach, University of Edinburgh
* Nadathur Satish, Intel
* David Schwalb, HPI
* Viktor Rosenfeld, TU Berlin
* Shirish Tatikonda, Target
* Christian Lang, Acelot
* Vincent Kulandaisamy, IBM Analytics
* Oded Shmueli, Technion
* Spyros Blanas, Ohio State University
* Stefan Manegold, CWI
* Shirish Tatikonda, Target
* Rene Mueller, IBM Research
* Holger Pirk, MIT
* Man Lung Yiu, Hongkong Polytechnic University
* Mohammad Sadoghi, Purdue University

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Important Dates
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 * Paper Submission: Friday, June 7, 2017, 11.59 pm PST.
 * Notification of Acceptance: Friday, June 30, 2017
 * Camera-ready Submission: Friday, July 14, 2017
 * Workshop Date: Friday, September 1, 2017

For submission instructions, please visit www.adms-conf.org
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