A latency hiding framework for enhanced ubiquitous access to big data in a constrained digital ecosystem

Dessalegn Mequanint, Lionel Brunie, Mulugeta Libsie, David Coquil
2012 Proceedings of the International Conference on Management of Emergent Digital EcoSystems - MEDES '12  
This paper presents our latency hiding framework for access to big data in a constrained digital ecosystem with application to digital medical archives. Aiming to enhance ubiquitous access of big data such as patient-oriented access of medical archives, we apply complex/multi-context prefetching to reduce latency thereby improving response time. We propose a formal model for prefetch requests rate and network workload or stress bound that takes into account a diverse set of constraints a
more » ... ecosystem could be in. In addition to that, components of our latency hiding framework such as a generic multi-context functional architecture, use case model, medical database model with emphasis on API (abstracted patient information) and a high-level system architecture have been designed. The development of a complex or multi-context prefetch algorithm that uses a patient's chief complaints, slackness sensitivity, popular content tag, user specified contexts and constraints is underway. A prototype system will also be developed to validate the proposed solutions. Moreover, input and output metrics will be developed to gauge the efficiency and effectiveness of the prefetch algorithm under development.
doi:10.1145/2457276.2457292 dblp:conf/medes/MequanintBLC12 fatcat:au2fcfoui5c67gjlz3oj4irspy