Host: Amir Basirat
Title: Distributed Associative Memory Approaches for large-scale Data Processing in Cloud Computing Environments and Wireless Sensor Networks

Abstract:
Unlike the early computations that used several bytes of data, existing computing infrastructure has been able to generate and store more than peta-bytes of data for day-to-day operations. This poses a question of whether our capability to recognise and process these data, matches our ability to generate them? In this short talk, this question will be addressed, by looking at the capability of existing recognition schemes to scale up with this outgrowth of data. Applications such as pattern recognition are essential in providing front-end mechanism for data processing. However, a different perspective of pattern recognition will be considered. Rather than looking at conventional approaches, such as statistical computations and deterministic learning schemes, this research will be focusing on distributed processing approach for scalable pattern recognition. My research work aims to explore new methods of partitioning and distributing data that is, resource vitalisation in the cloud and WSNs by fundamentally re-thinking the way in which future data management models will need to be developed on the Internet. Loosely-coupled associative computing techniques, which have so far not been considered, can provide the break through needed for a distributed data management scheme.