Description

     Data properties and hardware characteristics are two key aspects for efficient data management.  A clear trend in the first aspect, data properties, is the increasing demand to manage and process Big Data in both enterprise and consumer applications, characterized by the fast evolution of “Big Data Systems”. Examples of big data systems include NoSQL storage systems, MapReduce/Hadoop, data analytics platforms, search and indexing platforms, messaging infrastructures, event log processing systems, as well as novel extensions to relational database systems. These systems address needs for processing structured, semi-structured, and unstructured data across a wide spectrum of domains such as web, social networks, enterprise, mobile computing, sensor networks, multimedia/streaming, cyber-physical and high performance systems, and for a great many application areas such as e-commerce, finance, healthcare, transportation, telecommunication, and scientific computing. At the same time, the second aspect, hardware characteristics, is undergoing rapid changes, imposing new challenges for the efficient utilization of hardware resources. Recent trends include massive multi-core processing systems, high performance co-processors, very large main memory systems, storage-class memory, fast networking components, big computing clusters, and large data centers that consume massive amounts of energy.

    Utilizing new hardware technologies for efficient Big Data management is of urgent importance.  However, many essential issues in this area have yet to be explored, including system architecture, data storage, indexes, query processing, energy efficiency and proportionality. The aim of this half-day workshop is to bring together researchers, practitioners, system administrators, and others interested in this area to share their perspectives on the efficient management of big data over new hardware platforms, and to discuss and identify future directions and challenges in this area.

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  Topics

 Topics of interest include but not limited to:

• New systems architecture
• New storage devices and indexes
• Query processing
• Transaction processing
• Energy-efficient and energy-proportional data processing
• Benchmarking
• Fault management and reliability
• Heterogeneous hardware
• Main memory data management
• Sustainable power management
• Scalable and reconfigurable challenges

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  Submission  Guidelines

  Important Dates

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  Program

9:00-10:30am Session I: Keynote

• Recent Advances in Flash Solutions. Sangyeun Cho (Samsung)

10:30-11:00am coffee break

11:00-12:30pm Session II: Paper Presentation

• Scalable and Efficient Spatial Data Management on Multi-Core CPU and GPU Clusters: A Preliminary Implementation based on Impala. Simin You (City University of New York), Jianting Zhang (City College of New York), Le Gruenwald (University of Oklahoma)
  
  
• Optimizing CPU Cache Performance for Pregel-Like Graph Computation. Songjie Niu, Shimin Chen (Chinese Academy Sciences)
  
  
• Query Processing on Low-Energy Many-Core Processors. Annett Ungethüm, Dirk Habich, Tomas Karnagel, Wolfgang Lehner, Nils Asmussen, Marcus Völp, Benedikt Nöthen, Gerhard Fettweis (Technische Universität Dresden)
  
  
• Flash-aware Index Scan in PostgreSQL. Da-som Hwang, Woon-hak Kang, Sang-won Lee (Sungkyunkwan University)

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  Organizers

• Shimin Chen, Chinese Academy of Sciences, chensm@ict.ac.cn

• Qiong Luo, Hong Kong University of Science and Technology, luo@cse.ust.hk

• Xiaofeng Meng, Renmin University of China (RUC), xfmeng@ruc.edu.cn

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  PC Members

• Philippe Bonnet, IT University of Copenhagen, Denmark
• Bin Cui, Peking University, China
• Binsheng He, Nanyang Technological University, Singapore
• Stratos Idreos, Harvard University, USA
• Peiquan Jin, University of Science and Technology of China (USTC), China
• Sang-Wook Kim, Hanyang University, Korea
• Ioannis Koltsidas, IBM Zurich, Switzerland
• Suman Nath, Microsoft Research, USA
• Ippokratis Pandis, Cloudera, USA
• Jianliang Xu, Hong Kong Baptist University, China

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