Evolution of information retrieval in cloud computing by redesigning data management architecture from a scalable associative computing perspective

Authors:
Amir H. Basirat;Asad I. Khan
Affiliations:
Clayton School of Informationn Technology, Monash University, Victoria, Australia;Clayton School of Informationn Technology, Monash University, Victoria, Australia
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Abstract

The new surge of interest in cloud computing is accompanied with the exponential growth of data sizes generated by digital media (images/audio/video), web authoring, scientific instruments, and physical simulations. Thus the question, how to effectively process these immense data sets is becoming increasingly urgent. Also, the opportunities for parallelization and distribution of data in clouds make storage and retrieval processes very complex, especially in facing with real-time data processing. Loosely-coupled associative computing techniques, which have so far not been considered, can provide the break through needed for cloud-based data management. Thus, a novel distributed data access scheme is introduced that enables data storage and retrieval by association, and thereby circumvents the partitioning issue experienced within referential data access mechanisms. In our model, data records are treated as patterns. As a result, data storage and retrieval can be performed using a distributed pattern recognition approach that is implemented through the integration of loosely-coupled computational networks, followed by a divide-and-distribute approach that allows distribution of these networks within the cloud dynamically.