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Interposition Based Container Optimization For Data Intensive Applications, Rohan Tikmany
Interposition Based Container Optimization For Data Intensive Applications, Rohan Tikmany
College of Computing and Digital Media Dissertations
Reproducibility of applications is paramount in several scenarios such as collaborative work and software testing. Containers provide an easy way of addressing reproducibility by packaging the application's software and data dependencies into one executable unit, which can be executed multiple times in different environments. With the increased use of containers in industry as well as academia, current research has examined the provisioning and storage cost of containers and has shown that container deployments often include unnecessary software packages. Current methods to optimize the container size prune unnecessary data at the granularity of files and thus make binary decisions. We show …
Empirical Assessment Of Big Data Technology Adoption Factors For Organizations With Data Storage Systems, Ahmad B. Alnafoosi
Empirical Assessment Of Big Data Technology Adoption Factors For Organizations With Data Storage Systems, Ahmad B. Alnafoosi
College of Computing and Digital Media Dissertations
Many organizations have on-premises data storage systems. Data storage systems are evolving in multiple ways. One way is the adoption of Big Data. Big Data is a data storage system with the ability to analyze large volumes, velocity, and a variety of data. Per the Economist, data is now the most valuable resource (Parkins, 2017). Big Data holds the promise of unlocking a substantial value of data stored. Yet many organizations are not implementing Big Data. There is a need to identify key factors affecting adoption for such organizations. The literature review revealed multiple gaps in studied adoption factors (un-studied …
Federated Learning In Gaze Recognition (Fligr), Arun Gopal Govindaswamy
Federated Learning In Gaze Recognition (Fligr), Arun Gopal Govindaswamy
College of Computing and Digital Media Dissertations
The efficiency and generalizability of a deep learning model is based on the amount and diversity of training data. Although huge amounts of data are being collected, these data are not stored in centralized servers for further data processing. It is often infeasible to collect and share data in centralized servers due to various medical data regulations. This need for diversely distributed data and infeasible storage solutions calls for Federated Learning (FL). FL is a clever way of utilizing privately stored data in model building without the need for data sharing. The idea is to train several different models locally …