(MDSP ) 2013 - The 2nd International Workshop on Massive Data Storage and Processing

Website http://cloud.hdu.edu.cn/mdsp2013/index.html | Edit Freely

Category (MDSP ) 2013

Deadline: March 21, 2013 | Date: March 21, 2013-March 22, 2013

Venue/Country: Beidaihe, Hebei Province, China, China

Updated: 2013-03-12 19:07:24 (GMT+9)

Call For Papers - CFP

We apologies if you receive multiple copies of this announcement. We sincerely invite you to submit papers to

MDSP 2013(The 2nd International workshop on Massive Data Storage and Processing) in conjunction with WAIM 2013.

Call for paper: the 2nd International Workshop on Massive Data Storage and Processing (MDSP 2013)

will be held in conjunction with WAIM 2013, Beidaihe, China, June 14-16, 2013.

Important dates:

Abstract due: March 21, 2013

Full paper due: March 25, 2013

Author notification: April 10, 2013 （updated）

Camera-Ready deadline: April 15, 2013 (updated)

Workshop date: June 14, 2013

Publication

All accepted papers will be published in LNCS Springer Verlag (http://www.springer.com/lncs）and indexed by EI Compendex.

Description

Internet companies and traditional organizations store massive data sets, such as blogs on the Web,

transactions performed at Web-commerce sites, data generated by telecom switches, health care

systems, and bioinformatics. To mine the scientific or commercial value hidden behind such big

data, large-scale data processing techniques are required. Due to the rapid growth of data size,

efficient data storage and analysis become more and more challenging. They require innovative

storage technologies, scalable processing system, and operation schemas capable of managing PB-scale data.

This MDSP 2013 workshop intends to bring together researchers and practitioners from academia and

industry to discuss cutting edge research on massive data storage and processing, such that the

interactions among the experts of these areas can be promoted and new interdisciplinary technologies

can be developed. The scope of the workshop includes both data storage and processing techniques

involving massive data, as well as techniques for more specialized problems in, e.g., scientific

computing, database, business intelligence, statistics, and health care.

The workshop welcomes various different kinds of papers that could optimize the performance aspects

of existing data storage and processing. The papers may address issues including novel architectures

for massive data storage and processing, storage networking, storage virtualization, storage for

complex computing, data pre-processing, Job scheduling for computing cluster, data scheduling.

Papers of applied research, industrial experience reports, work-in-progress and vision papers

with different criteria for each category are also solicited. The best presented paper will receive

the BEST PAPER AWARD.

Topic of Interest

The topics of interest include, but are not limited to the following:

* Distributed data management, retrieval and mining

* Novel architectures for massive storage and processing

* File systems, object-based storage, block-level storage

* Data intensive computing in science, commerce, entertainment and medicine

* Network support for data-intensive computing

* Data archives, digital libraries, and preservation

* Service oriented architectures for data-intensive computing

* Data privacy and protection in a public cloud environment

* Future research challenges of data intensive computing

* Security and protection of sensitive data in collaborative environments

* MapReduce implementation issues and improvements

* MapReduce and its Applications in Data intensive computing

* Large-scale MapReduce (Grid and Desktop Grid)

* performance measurement, analytic modeling, simulation

* Solid state drive (SSD)-based storage

* Storage virtualization and cloud storage

* MapReduce and its generalizations

* Massive data mining

* Query processing and optimization for massive data

* Storage as a Service

* Storage networking

* Parallel database

* Parallel I/O architectures

* Massive data processing: algorithms, techniques, and systems

* Privacy and security in massive data processing

* Real-time processing, performance issues

* Stream data processing

* Job Scheduling and data scheduling optimization

Submission Guidelines

Papers should not exceed 10 pages in LNCS (Lecture Notes in Computer Science) format. The submission

of papers must be in either PDF or Word format. We also welcome demonstration proposals and posters

to foster interaction, to present system perspective on real problems, and to introduce innovative

concepts. Those demo or shorter papers should not exceed 4 pages. The Authors should exclude the

usage of non-English fonts to avoid the problems from reviewers' machines.

All accepted papers will be published by Springer-Verlag in the LNCS series. This ensures wide

dissemination and high visibility (e.g. in the DBLP and ScienceDirect). Online proceedings will

additionally be hosted at the workshop web site.

Please submit your papers using the following submission site:

https://cmt.research.microsoft.com/MDSP2013

Organizing Committee

General Co-Chairs

Weisong Shi, College of Computer Science, Wayne State University, USA

Email: weisongwayne.edu

Yunjun Gao, College of Computer Science, Zhejiang University, China

Email: gaoyjzju.edu.cn

Program Co-Chairs

Weiping Wang, Institute of Computing, Chinese Academy of Sciences, China

Email: wpwangncic.ac.cn

Zujie Ren, College of Computer Science, Hangzhou Dianzi University, China

Email: renzjhdu.edu.cn

Tentative program committee members:

* Guoray Cai (Pennsylvania State University, USA）

* Yong Woo LEE (University of Seoul, Korea)

* Hung Keng Pung (National University of Singapore)

* Xiaofei Liao (Huazhong University of Science and Technology, China)

* Yijun Bei (Zhejiang University, China)

* Yi Zhuang (Zhejiang GongShang University, China)

* Tao Jiang (Jiaxing University, China)

* Weiwei Sun (Fudan University, China)

* Xiaokui Xiao (Nanyang Technological University, Singapore)

* Yimin Lin (Singapore Management University, Singapore)

* Bin Yao (Shanghai Jiaotong University, China)

* Shaojie Qiao (Southwest Jiaotong University, China)

* Congfeng Jiang (Hangzhou Dianzi University, China)

* Jilin Zhang (Hangzhou Dianzi University, China)

* Qi Qiang (Alibaba Corp, China)

* Yan Wang (Netease Corp, China)

* Yongjian Ren (Infocore Corp, China)