A Latency Hiding Framework For Enhanced Ubiquitous Access to Big Data in a Constrained Digital Ecosystem: Application to Digital Medical Archives
Résumé
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 digital 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.