Spark solutions for discovering fuzzy association rules in Big Data

The research reported in this paper was partially supported the COPKIT project from the 8th Programme Framework (H2020) research and innovation programme (grant agreement No 786687) and from the BIGDATAMED projects with references B-TIC-145-UGR18 and P18-RT-2947.The high computational impact when mining fuzzy association rules grows significantly when managing very large data sets, triggering in many cases a memory overflow error and leading to the experiment failure without its conclusion. It is in these cases when the application of Big Data techniques can help to achieve the experiment completion. Therefore, in this paper several Spark algorithms are proposed to handle with massive fuzzy data and discover interesting association rules. For that, we based on a decomposition of interestingness measures in terms of α-cuts, and we experimentally demonstrate that it is sufficient to consider only 10equidistributed α-cuts in order to mine all significant fuzzy association rules. Additionally, all the proposals are compared and analysed in terms of efficiency and speed up, in several datasets, including a real dataset comprised of sensor measurements from an office building.COPKIT project from the 8th Programme Framework (H2020) research and innovation programme 786687BIGDATAMED projects B-TIC-145-UGR18 P18-RT-294

From automated to data-driven large-scale dietary assessment

Ph. D. Thesis.Dietary assessment surveys are an important tool for measuring and/or monitoring the nutritional profile of a population. The analysis of data that is collected in these surveys helps to develop health care guidelines and policies that minimise the risk of diet related diseases on a national scale. For years these surveys had to be conducted in a form of an interview by trained researchers with a nutritional background. The emergence of systems that automate interviewer-led protocols and transform these interviews into online surveys has addressed financial limitations and brought scalability into dietary assessment studies. In the meantime, online dietary assessment surveys mostly copy the interviewer-led procedures and inherit some of their methodological issues that lead to misreporting of dietary intake and lower the accuracy of assessment. This thesis primarily focuses on the issues related to human-memory, motivation of respondents to take part in dietary assessment studies, and the usability of survey interfaces. This work pinpoints the elements of automated dietary assessment systems, where these issues affect the accuracy of results. This analysis is then translated into three research questions of this thesis. Challenges related to human-memory are then addressed by developing and evaluating a recommender system for prompting omitted foods in online dietary assessment surveys. This work also explores short retention intervals (i.e. time between an intake and recall) as another method for recall assistance. As a way to motivate respondents to take part in dietary assessment surveys this thesis explores tailored dietary feedback provided to respondents at the end of a survey. Usability and performance of new methods are analysed in real-life dietary assessment surveys using a usability framework developed for this research. Acceptance of the methods is analysed using thematic analysis of transcribed interviews with respondents. Research activities conducted during this work provide some support to hypotheses defined in the research questions.School of Computing, Faculty of Science, Agriculture and Engineering of Newcastle Universit