Toward designing a problem solving environment for analyzing dietary data on distributed computing

Abstract

For a better quality of life, people desire wellness by preventing diseases. A huge amount of personal health data involves information which represents current health status and potential diseases. Particularly, individual dietary data, which can be obtained on a daily basis, indicate health condition and potential diseases when dietary patterns are analyzed. Therefore, it is essential to perform dietary analysis for wellness. In the field of food and nutrition, dietary analysis requires handling large and diverse kinds of input data and is based on tedious and repetitive tasks. A useful problem solving environment (PSE) is imperative to run experiments fast and conveniently. We design a problem solving environment to handle numerous data, speed up an experiment, and automate repeated tasks with food and nutrition data on distributed computing infrastructure. Our proposed problem solving environment speeds up each step and total analysis effectively as it automates steps of dietary analysis composing of sequential and parallel tasks. Our designed framework on parallel processing with over 70 MB of data can efficiently reduce execution time compared to sequential processing. © 2015 American Scientific Publishers. All rights reserved.