MDAC 2010 : International Workshop on Massive Data Analytics over the Cloud
Call For Papers
International Workshop on
Massive Data Analytics over the Cloud (MDAC2010)
April 26, 2010, Raleigh, NC, USA
in conjunction with WWW 2010
***** Submission deadline: Feb 21, 2010 ************
Internet and traditional organizations continue to be faced with the challenge of making sense of the mountains of data that are everywhere,
be it the blogs on the Web, transactions performed at Web-commerce sites, data generated by telecom switches, health care systems,
bioinformatics and even in homeland security. In this new data-flooded world, the key question is: How do we analyze these enormous
amounts of data in a timely and cost-effective manner? Clearly, the traditional model of building bigger machines and more storage does not
apply here as the data is growing at a rate which is impossible to keep up with using scale-up methods. The promising solutions, such as those
based on MapReduce, use racks of commodity servers with locally attached storage and are able to scale out quickly at low cost. In general, there
is a need to assemble resources on demand ? the motivation for Cloud Computing. Broadly speaking, Cloud Computing represents the desire to migrate
from the traditional server-centric computing architecture to a totally network-centric architecture where logical computing resources can be
assembled flexibly, on demand. MapReduce is a software framework introduced by Google, in an attempt to bring the benefits of cloud computing to
tackling the problems posed by massive datasets. While the early signs in terms of support from the developer community, academia and industry are
quite encouraging, there are many open problems that still need to be addressed. Some of them are:
* Is there a class of problems/workloads for which this distributed computing over commodity machines is the best solution?
* How amenable are these algorithms to expression using MapReduce or other simple paradigms?
* How do we define and use effective and practical metrics to compare the capability of different solutions being offered?
* Do we need different solutions for different data types or algorithms? How do we build bridges between them?
* Are there viable alternatives, such as Pregel or others for important sets of problems?
* How do we integrate these new computing platforms with existing traditional data warehouse-based analytics systems?
* Challenges in managing the massive datasets - Security, versioning, archiving?
* Tackling the legal/moral challenges associated with mining those datasets?
We invite researchers working in any of the following areas to participate:
* Data intensive applications of cloud computing
* Algorithms for massive data analysis such as data mining, statistics, network, and predictive algorithms
* Distributed data management, retrieval and mining
* Novel architectures for cloud computing
* Map-reduce and its generalizations
* Large scale social media analysis
* Privacy Preservation in Cloud
* Parallel databases
* Storage as a Service
* Cloud resource management
* Fault Tolerance Assessment and Management
* Reliability of applications running over the cloud
Manuscripts due: February 21, 2010
Notification of acceptance: March 19, 2010
Final revised manuscript: March 31, 2010
Workshop: April 26, 2010
We welcome original, unpublished manuscripts of upto 6 pages (2 column format) inclusive of all references and figures. Vision papers and
descriptions of work-in-progress are welcomed as short paper submissions (4 pages). Papers must be written in English, and formatted
according to WWW 2010 proceeding format.
Submission Site: Papers are to be submitted via CMT at https://cmt.research.microsoft.com/MDAC2010/.
Proceedings: To be published as an ACM ICPS volume (ISBN: 978-1-60558-991-6) and
will be available on ACM Digital Library. We will be following ACM Copyright and plagiarism policies.
Ullas Nambiar, IBM India Research Lab, New Delhi, India
John McPherson, IBM Almaden Research Center, USA
David Konopnicki, IBM Haifa Research Lab, Israel
Rakesh Agrawal, Microsoft Search Labs, Mountain View, CA, USA
Alon Halevy, Google Inc., Mountain View, CA, USA
Amr Awadallah, Cloudera, USA
Andrew McCallum, University of Massachusetts Amherst, USA
Assaf Schuster, Technion - Israel Institute of Technology
Gautam Das, University of Texas, Arlington, USA
Jimeng Sun, IBM Watson Research Center, USA
John Shafer, Microsoft Search Labs, USA
Kun Liu, Yahoo! Labs, USA
Louiqa Raschid, University of Maryland, College Park, USA
Michal Shmueli-Scheuer, IBM Haifa Research Lab, Israel
Michael Sheng, University of Adelaide, Australia
Mong Li Lee, National University of Singapore, Singapore
Rajeev Gupta, IBM India Research Lab, India
Vanja Josifovski, Yahoo Research, USA
Yannis Sismanis, IBM Almaden Research Center, USA
Wen-syan Li, SAP, China