Submission due:
Mar.24, 2014
Notification
Apr.10, 2014

HardBD 2014

     the Second International Workshop on Big Data Management on Emerging Hardware

in conjunction with WAIM 2014

16-18 June, 2014. Macau SAR, China

http://idke.ruc.edu.cn/HardBD2014

The workshop program is updated!
We are pleased to announce that Prof. Shimin Chen from ICT@CAS will give a keynote at HardBD'14!
Accepted paper list is updated!
 

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Description
 

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Topics
 

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Submissions
 

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Important Dates
 

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Accepted Papers
 

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Program
 

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Registration
 

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Organization

  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, characterized by the fast evolution of “Big Data Systems”, where nearly every aspect of both enterprise and consumer services is being driven by data processing and analysis. Examples of big data systems include NoSQL storage systems, Hadoop/MapReduce, data analytics platforms, search and indexing platforms, and messaging infrastructures. These systems address needs for structured and unstructured data across a wide spectrum of domains such as Web, social networks, enterprise, cloud, mobile, sensor networks, multimedia/streaming, cyber-physical and high performance systems, and for multiple application areas such as healthcare, transportation, and scientific computing.

At the same time, the second aspect, hardware characteristics, is undergoing rapid changes, imposing new challenges for an efficient utilization of hardware resources. Recent trends include storage-class memory, massive multi-core processing systems, very large main memory systems, fast networking components, big computing clusters, and large data centers that consume massive amounts of energy.  It is clear that many aspects of data management have to evolve with these trends. Utilizing new hardware technologies for efficient Big Data management is of urgent importance.

However, many essential issues in this area have to be exploited, such as new system architecture, new storage devices and indexes, query processing, energy efficiency and proportionality, and so on. The aim of this half-day workshop is to bring together researchers, practitioners, system administrators, system programmers, and others interested in sharing and presenting their perspectives on the effective management of big data over new hardware platforms, and to also discuss and identify future directions and challenges in this area. In addition, a number of invited papers will also be solicited.

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  Topics

 

We welcome papers that address fundamental research issues in this challenging area, with emphasis on big data management on emerging hardware. Submissions covering topics from the following non-exclusive list are encouraged:

Ø    New systems architecture

Ø    New storage devices

Ø    Architecture-aware computing

Ø    GPGPU-based data processing and analytics

Ø    Energy-aware data processing

Ø    Paralell and distributed data processing

Ø    Bechmarking big data systems

Ø    Co-processing of heterogeneous hardware

Ø    In-memory data management

Ø    Scalable and reconfigurable challenges

Ø    Other challenges in big data systems

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  Submissions

Authors are invited to submit electronically original, English-language research contributions not concurrently submitted elsewhere. Accepted papers will be published by Springer as proceedings in Lecture Notes in Computer Science (LNCS). All submitted papers should be Springer LNCS camera-ready format. The style files are available from Springer LNCS site.

All submissions files should be in PDF formats. The number of pages should not exceed 12 pages. Any paper more than 12 pages will be rejected. Please submit your paper(s) at: 

  Important Dates


Mar. 24, 2014: Submission deadline

Apr. 10, 2014: Acceptance notification

Apr. 17, 2014: Submission deadline of camera-ready papers.

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  Accepted Papers


[1]  Zhijie Feng, Zhiyong Feng, Xin Wang, Guozheng Rao, Yazhou Wei and Zhiyuan Li. HDStore: An SSD/HDD Hybrid Distributed Storage Scheme for Large-Scale Data
[2]  Yi Ou, Jianliang Xu and Theo Haerder. Wear-Aware Algorithms for PCM-Based Database Buffer Pools
[3]  Zheng Huanxin and Wu Junmin. Accelerate K-means Algorithm by using GPU in the Hadoop Framework
[4]  Dongmei Huang, Le Sun and Danfeng Zhao. An Efficient Hybrid Index Structure for Temporal Marine Data
[5]  Xiaolan Xie and Zhuang Xiong. On Massive Spatial Data Retrieval Based on Spark

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  Program


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  Registration


There is no seperated registration for workshops at WAIM'14. Please refer to the registration page of WAIM'14 to find the detailed instructions.

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  Organization

 

Workshop Organizers:

  •    Xiaofeng Meng, Renmin University of China (RUC), China

  •    Jianliang Xu, Hong Kong Baptist University (HKBU), China

  •    Peiquan Jin, University of Science and Technology of China (USTC), China  

PC Members:

  • Yi Ou, Technical University of Kaiserslautern, Germany
  • Binsheng He, Nanyang Technological University, Singapore
  • Luc Bouganim, INRIA, France
  • Bin Cui, Peking Universit, China
  • Bin He, IBM Almaden Research, USA
  • Sang-Wook Kim, Hanyang University, Korea
  • Ioannis Koltsidas, IBM Research - Zurich, Switzerland
  • Ziyu Lin, Xia'Men University, China
  • Vijayan Prabhakaran, Microsoft Research, USA
  • Xike Xie, Aalborg Univeristy, Denmark
  • Ke Lu, Tencent, China
  • Qi Zhang, Alibaba, China

     

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