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    The Application of Artificial Intelligence and Big Data in the Food Industry
    (MDPI (Basel, Switzerland), 2023-12-18) Ding H; Tian J; Yu W; Wilson DI; Young BR; Cui X; Xin X; Wang Z; Li W; Yılmaz MT
    Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.
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    A better start national science challenge: supporting the future wellbeing of our tamariki E tipu, e rea, mō ngā rā o tō ao: grow tender shoot for the days destined for you
    (Taylor and Francis Group, 2023-02-22) Maessen SE; Taylor BJ; Gillon G; Moewaka Barnes H; Firestone R; Taylor RW; Milne B; Hetrick S; Cargo T; McNeill B; Cutfield W; Moton TM; King PT; Dalziel S; Merry S; Robertson S; Day A
    The majority of children and young people in Aotearoa New Zealand (NZ) experience good health and wellbeing, but there are key areas where they compare unfavourably to those in other rich countries. However, current measures of wellbeing are critically limited in their suitability to reflect the dynamic, culture-bound, and subjective nature of the concept of ‘wellbeing’. In particular, there is a lack of measurement in primary school-aged children and in ways that incorporate Māori perspectives on wellbeing. A Better Start National Science Challenge work in the areas of Big Data, Healthy Weight, Resilient Teens, and Successful learning demonstrates how research is increasing our understanding of, and our ability to enhance, wellbeing for NZ children. As we look ahead to the future, opportunities to support the wellbeing of NZ young people will be shaped by how we embrace and mitigate against potential harms of new technologies, and our ability to respond to new challenges that arise due to climate change. In order to avoid increasing inequity in who experiences wellbeing in NZ, wellbeing must be monitored in ways that are culturally acceptable, universal, and recognise what makes children flourish.
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    An empirical comparison between MapReduce and Spark : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Information Sciences at Massey University, Auckland, New Zealand
    (Massey University, 2019) Liu, YuJia
    Nowadays, big data has become a hot topic around the world. Thus, how to store, process and analysis this big volume of data has become a challenge to different companies. The advent of distributive computing frameworks provides one efficient solution for the problem. Among the frameworks, Hadoop and Spark are the two that widely used and accepted by the big data community. Based on that, we conduct a research to compare the performance between Hadoop and Spark and how parameters tuning can affect the results. The main objective of our research is to understand the difference between Spark and MapReduce as well as find the ideal parameters that can improve the efficiency. In this paper, we extend a novel package called HiBench suite which provides multiple workloads to test the performance of the clusters from many aspects. Hence, we select three workloads from the package that can represent the most common application in our daily life: Wordcount (aggregation job),TeraSort (shuffle/sort job) and K-means (iterative job). Through a large number of experiments, we find that Spark is superior to Hadoop for aggreation and iterative jobs while Hadoop shows its advantages when processing the shuffle/sort jobs. Besides, we also provide many suggestions for the three workloads to improve the efficiency by parameter tuning. In the future, we are going to further our research to find out whether there are some other factors that may affect the efficiency of the jobs.
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    Big Data Analytic Framework for Organizational Leverage
    (MDPI (Basel, Switzerland), 6/03/2021) Mathrani S; Lai X
    Web data have grown exponentially to reach zettabyte scales. Mountains of data come from several online applications, such as e-commerce, social media, web and sensor-based devices, business web sites, and other information types posted by users. Big data analytics (BDA) can help to derive new insights from this huge and fast-growing data source. The core advantage of BDA technology is in its ability to mine these data and provide information on underlying trends. BDA, however, faces innate difficulty in optimizing the process and capabilities that require merging of diverse data assets to generate viable information. This paper explores the BDA process and capabilities in leveraging data via three case studies who are prime users of BDA tools. Findings emphasize four key components of the BDA process framework: system coordination, data sourcing, big data application service, and end users. Further building blocks are data security, privacy, and management that represent services for providing functionality to the four components of the BDA process across information and technology value chains.