Optimized data management for e-learning in the clouds towards Cloodle

Cloud computing provides access to "infinite" storage and computing resources, offering promising perspectives for many applications, particularly e-learning. However, this new paradigm requires rethinking of database management principles in order to allow deployment on scalable, easy to access infrastructures, applying a pay-as-you-go model in which failures are not exceptions but rather the norm. The GOD project aims to provide an optimized data management system for e-learning in the cloud by rethinking traditional database management techniques, extending them to consider the specificities of this paradigm.

[1]  Donald Kossmann,et al.  The Skyline operator , 2001, Proceedings 17th International Conference on Data Engineering.

[2]  Nicolas Spyratos,et al.  Personalizing Queries over Large Data Tables , 2011, ADBIS.

[3]  Jan Chomicki,et al.  Preference formulas in relational queries , 2003, TODS.

[4]  Alon Y. Halevy,et al.  Answering queries using views: A survey , 2001, The VLDB Journal.

[5]  Jingren Zhou,et al.  SCOPE: easy and efficient parallel processing of massive data sets , 2008, Proc. VLDB Endow..

[6]  Zheng Shao,et al.  Hive - a petabyte scale data warehouse using Hadoop , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).

[7]  Ravi Kumar,et al.  Pig latin: a not-so-foreign language for data processing , 2008, SIGMOD Conference.

[8]  Arthur M. Keller,et al.  A predicate-based caching scheme for client-server database architectures , 1994, Proceedings of 3rd International Conference on Parallel and Distributed Information Systems.

[9]  Nicolas Spyratos The partition model: a deductive database model , 1987, TODS.

[10]  Verena Kantere,et al.  Predicting cost amortization for query services , 2011, SIGMOD '11.

[11]  Laurent d'Orazio,et al.  Cooperative Database Caching within Cloud Environments , 2012, AIMS.

[12]  Divesh Srivastava,et al.  Semantic Data Caching and Replacement , 1996, VLDB.

[13]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[14]  Alexis Tsoukiàs,et al.  From decision theory to decision aiding methodology , 2008, Eur. J. Oper. Res..

[15]  Dan Suciu,et al.  How to Price Shared Optimizations in the Cloud , 2012, Proc. VLDB Endow..

[16]  Verena Kantere,et al.  An Economic Model for Self-Tuned Cloud Caching , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[17]  Laurent d'Orazio,et al.  Semantic caching for pervasive grids , 2009, IDEAS '09.

[18]  Werner Kießling,et al.  Situated Preferences and Preference Repositories for Personalized Database Applications , 2004, ER.

[19]  Pierre-Yves Schobbens,et al.  Operators and Laws for Combining Preference Relations , 2002, J. Log. Comput..

[20]  Laurent d'Orazio,et al.  Cost models for view materialization in the cloud , 2012, EDBT-ICDT '12.

[21]  Andrey Balmin,et al.  Jaql , 2011, Proc. VLDB Endow..

[22]  Nicolas Spyratos,et al.  Rewriting aggregate queries using functional dependencies , 2011, MEDES.

[23]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[24]  Evaggelia Pitoura,et al.  Cooperative XPath caching , 2008, SIGMOD Conference.

[25]  Boris Chidlovskii,et al.  Semantic caching of Web queries , 2000, The VLDB Journal.

[26]  Nicolas Spyratos,et al.  Efficient Rewriting Algorithms for Preference Queries , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[27]  Elke A. Rundensteiner,et al.  XCache: a semantic caching system for XML queries , 2002, SIGMOD '02.

[28]  Laurent d'Orazio,et al.  Adaptable cache service and application to grid caching , 2010, Concurr. Comput. Pract. Exp..