Big data and food science

Food industry and engineering—Quo vadis?

Big data are one of the representative phenomena of the information era of human societies. A basic study on the cognitive foundations of big data science is presented with a coherent set of general principles and analytic methodologies for big data manipulations. It leads to a set of mathematical theories that rigorously describe the general patterns of big data across pervasive domains in sciences, engineering, and societies. A significant finding towards big data science is that big data systems in nature are a recursive n-dimensional typed hyperstructure (RNTHS). The fundamental topological property of big data system enables the inherited complexities and unprecedented challenges of big data to be formally dealt with as a set of denotational mathematical operations in big data engineering. The cognitive relationship and transformability between data, information, knowledge, and intelligence are formally revealed towards big data science.

This report focuses on safety aspects of connected and automated vehicles (CAVs). The fundamental question to be answered is how can CAVs improve road users' safety? Using advanced data mining and thematic text analytics tools, the goal is to systematically synthesize studies related to Big Data for safety monitoring and improvement. Within this domain, the report systematically compares Big Data initiatives related to transportation initiatives nationally and internationally and provides insights regarding the evolution of Big Data science applications related to CAVs and new challenges. The objectives addressed are: 1-Creating a database of Big Data efforts by acquiring reports, white papers, and journal publications; 2-Applying text analytics tools to extract key concepts, and spot patterns and trends in Big Data initiatives; 3-Understanding the evolution of CAV Big Data in the context of safety by quantifying granular taxonomies and modeling entity relations among contents in CAV Big Data research initiatives, and 4-Developing a foundation for exploring new approaches to tracking and analyzing CAV Big Data and related innovations. The study synthesizes and derives high-quality information from innovative research activities undertaken by various research entities through Big Data initiatives. The results can provide a conceptual foundation for developing new approaches for guiding and tracking the safety implications of Big Data and related innovations.

In order to scrutinize or evaluate an extremely high quantity of an ever-present and diversified nature of data, new technologies are developed. With the application of these technologies, called big data technologies, to the constantly developing various internal as well as external sources of data, concealed correlations between data can be identified, and promising strategies can be developed, which is necessary for economic growth and new innovations. This chapter deals with the analysis of the real-time uses of big data to both individual persons and the society too, while concentrating on seven important areas of key usage: big data for business optimization and customer analytics, big data and healthcare, big data and science, big data and finance, big data as enablers of openness and efficiency in government, big data and the emerging energy distribution systems, and big data security.

The launch of the US BRAIN and European Human Brain Projects coincides with growing international efforts toward transparency and increased access to publicly funded research in the neurosciences. The need for data-sharing standards and neuroinformatics infrastructure is more pressing than ever. However, 'big science' efforts are not the only drivers of data-sharing needs, as neuroscientists across the full spectrum of research grapple with the overwhelming volume of data being generated daily and a scientific environment that is increasingly focused on collaboration. In this commentary, we consider the issue of sharing of the richly diverse and heterogeneous small data sets produced by individual neuroscientists, so-called long-tail data. We consider the utility of these data, the diversity of repositories and options available for sharing such data, and emerging best practices. We provide use cases in which aggregating and mining diverse long-tail data convert numerous small data sources into big data for improved knowledge about neuroscience-related disorders.

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.

Big Data and Data Science in Critical Care

Big data is a blanket term for the non-traditional strategies and technologies needed to organize, process, and gather insights from large datasets. While the problem of working with data that exceeds the computing power or storage of a single computer is not new, the pervasiveness, scale, and value of this type of computing has greatly expanded in recent years. Big Data can also play a role for small or medium-sized companies and organizations that recognize the possibilities (which can be incredibly diverse) to capitalize upon the gains. Now many organizations in this data-rich industry are focused on using big data and analytics to make life-altering changes in patient education, treatment and more. This paper provides a general survey of recent progress and advances in Big Data science, healthcare, and biomedical research. We have mainly focused on the recently proposed methods based on various issues in medical domain. Nevertheless there are many challenges in implementing big data in healthcare especially in relation to privacy, security, standards, governance, integration of data, data accommodation, data classification, incorporation of technology etc. Further it includes research applications, technical tools of big data in healthcare and the opportunities inside this quickly emerging scientific field are explored.

Big Data refers to large and disparate volumes of data generated by people, applications and machines. It is gaining increasing attention from a variety of domains, including education. What are the challenges of engaging with Big Data research in education? This paper identifies a wide range of critical issues that researchers need to consider when working with Big Data in education. The issues identified include diversity in the conception and meaning of Big Data in education, ontological, epistemological disparity, technical challenges, ethics and privacy, digital divide and digital dividend, lack of expertise and academic development opportunities to prepare educational researchers to leverage opportunities afforded by Big Data. The goal of this paper is to raise awareness on these issues and initiate a dialogue. The paper was inspired partly by insights drawn from the literature but mostly informed by experience researching into Big Data in education.

Cloud computing paradigm builds on the foundations of distributed computing, grid computing, virtualisation, service orientation, etc. Cloud storage, which is one of the most attractive cloud services, offers numerous benefits from both the technology and functionality perspectives such as increased availability, flexibility, and functionality. Therefore, Cloud-based platforms have become fundamental to collaborative and data management systems. However, the new Cloud environment creates major collaborative and data management challenges such as efficient and reliable remote storage, online data query processing, data integrity, outsourcing computation, and secure virtualisation, among others. Such issues need to be carefully studied and solved to enable the wide deployment and adoption of Cloud computing in businesses, industry, etc. The purpose of this special issue is to publish recent advances in Cloud-based collaborative systems and Cloud storage. The special issue comprises five high-quality papers, which are arranged as follows. De Maio et al in the first paper1 “Social Media Marketing through Time-Aware Collaborative Filtering,” define a time-aware collaborative filtering for estimating users' interest along the time in Twitter. Their approach uses text analysis services to semantically annotate tweets' content and to track concepts considering post frequencies along the time. A model-based approach implementing K-Nearest Neighbors is used to estimate user's similarity representing their profile by sampling user's interest with three different techniques: Vectorial Representation, Symbolic Aggregation Approximation, and Median. The authors present experimental results comparing these techniques and performing model training in different time windows. The second paper2 “SERNOTATE: An Automated Approach for Business Service Description Annotation for efficient Service Retrieval and Composition” by Chotipant et al proposes an automated approach, namely, SERNOTATE, for business service description annotation for efficient service retrieval and composition. To that end, the authors employ new semantic-based linking approaches, namely, Extended Case-based Reasoning, Vector-based and Classification-based, that automatically annotate business services to relevant service concepts. The experimental results test and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real-world dataset. In the third paper,3 “Sensor Data Management in the Cloud: Data Storage, Data Ingestion, and Data Retrieval,” the authors address an increasing need to capture, store, and analyse the dynamic semistructured data from sensors. A similar growth of semistructured data in the modern Web has led to the creation of NoSQL data stores for scalability, availability, and performance, whereas large-scale data processing frameworks for parallel analysis. The authors study how sensor data management can benefit from MongoDB with Apache Spark technologies, specifically for ingesting high-velocity sensor data and parallel retrieval of high volume data. The performance of MongoDB sharding and no-sharding databases with Apache Spark are evaluated to identify the right software environment for sensor data management. Park et al in the fourth paper,4 “Study on the SDN-IP based Solution of Well-known Bottleneck Problems in Private Sector of National R&E Network for Big Data Transfer,” deal with issues arising in Software Defined Networking (SDN). The key feature of SDN networks is the provisioning of dynamic service architecture. Therefore, it is more appropriate for the big data science computing trends, for handling today's big data science, which requires massive parallel processing and a constant demand for additional capacity and on demand connectivity. The authors bring the experience from KREONET (Korea Research Environment Open Network) and Korea National R&E Network, which adopt SDN as a platform service for big data scientific application. The big data application for KREONET SDN service comprises high-energy physics, astrophysics, biology/genetics, meteorology, artificial satellite data, etc. The last paper5 “Improved Outsourced Private Set Intersection Protocol Based on Polynomial Interpolation” by Yang et al investigates issues in private set intersection (PSI) protocols, which enable two parties to compute the intersection of their inputs without compromising anything about the datasets beyond the intersection. The authors present a variant of delegated private set intersection protocol secure in the semi-honest model under RSA assumption as well as an efficient and secure outsourcing computation algorithm for RSA crypto-system. Based on this algorithm, the authors transform a variant of delegated private set intersection protocol into an improved outsourced one, which enables the clients to perform only simple modular multiplication for computing during the execution of the protocol. Compared with the state of the art, the proposed protocol has a great advantage in efficiency. The editor of this special issue would like to thank all authors for their contributions and the reviewers for their constructive and useful feedback to authors. I would like to thank Prof Geoffrey Fox, the Editor-in-Chief of Concurrency and Computation: Practice and Experience, for the opportunity to edit this special issue and his support.

After several years of development, the first results and papers showcasing the output from New Zealand’s earth system modelling community are now available. This represents a large body of behind-the-scenes work from multiple NIWA and NeSI staff, not to mention our international collaborators in the Unified Model partnership. This is all very well, but how are we going about turning approximately 0.5PB of raw model output into science which enables New Zealanders to ‘anticipate, adapt, manage risk, and thrive in a changing climate.’ This is the mission statement of the Deep South National Science Challenge, through which this work is funded. We are simulating three greenhouse gas emissions scenarios representative of an unknown future. From the model output, we can estimate how the world will warm. However, earth system models enable us to do a lot more than this. We can also examine changes to chemical processes in the atmosphere, biogeochemical processes in the ocean, as well as changes to the terrestrial biosphere. I will discuss the theory and practice of turning this raw data into useful science in an HPC context. I will also present some early findings from our model, which differs from its parent model – the UKESM – in its ability to simulate the ocean circulation around Aotearoa New Zealand at high, ‘eddy permitting’ resolution. ABOUT THE AUTHORS Jonny Williams moved to New Zealand in 2015 after a postdoc in physical geography at Bristol University studying extreme warm paleoclimates of the Cretaceous and Jurassic periods. Before this he worked in private practice as a junior consultant at Eunomia Research and Consulting and as a climate scientist at the UK Met Office. Jonny has a PhD in molecular electronics from the University of Bath and a degree in physics from Imperial College London. Erik Behrens leads the ocean modelling project, to further improve NZESM, in the second phase of the Deep South. He has a PhD and degree in physical oceanography from the Christian-Albrechts University of Kiel, Germany. His main interest is to understand how oceans around New Zealand and around Antarctica change due to climate change. Olaf Morgenstern is leading climate modelling at NIWA and for the Deep South National Science Challenge. Prior to joining NIWA worked for Cambridge University in the UK and the Max-Planck-Institute for Meteorology in Hamburg, Germany. His main research interest is in the linkages between physical climate change and atmospheric composition. He holds a PhD in meteorology from ETH Zurich, Switzerland, and a physics degree from Freiburg University, Germany. Mike Williams has been the director of the Deep South National Science Challenge since 2016. He obtained his PhD in polar oceanography from the University of Tasmania in 1999 and was an assistant professor at the Niels Bohr Institute for Physics in Copenhagen, Denmark for three. He joined in NIWA in 2001 and has had various roles, including leading the climate observations programme and Antarctic research programmes.

In this study the evolution of Big Data (BD) and Data Science (DS) literatures and the relationship between the two are analyzed by bibliometric indicators that help establish the course taken by publications on these research areas before and after forming concepts. We observe a surge in BD publications along a gradual increase in DS publications. Interestingly, a new publications course emerges combining the BD and DS concepts. We evaluate the three literature streams using various bibliometric indicators including research areas and their origin, central journals, the countries producing and funding research and startup organizations, citation dynamics, dispersion and author commitment. We find that BD and DS have differing academic origin and different leading publications. Of the two terms, BD is more salient, possibly catalyzed by the strong acceptance of the pre-coordinated term by the research community, intensive citation activity, and also, we observe, by generous funding from Chinese sources. Overall, DS literature serves as a theory-base for BD publications.

Big Data Analytics (BDA) is revolutionizing decision-making but remains underutilized in Food, Agriculture, Natural Resources, and Human (FANH) sciences. This study assesses the perceived importance of data analytics knowledge among FANH professionals. It explores differences in perceptions based on workplace data analytics usage and work experience. A literature review highlights BDA's potential to improve efficiency, sustainability, and innovation in FANH disciplines. However, challenges such as inadequate education, limited resources, and institutional resistance hinder adoption. A survey of 88 former Nigerian agribusiness students assessed their proficiency in data analytics. Using MANOVA, findings show that active data analytics users value it more than non-users. Work experience alone does not significantly influence perceptions. The study highlights the need for interdisciplinary curriculum development and targeted training. Integrating analytics into FANH education can enhance decision-making, resource optimization, and sustainability.

There are different paradigms in educational technology. Under the background of big data era, data science, learning analysis and education have made great achievements. In the field of education under big data, all kinds of new paradigms are constantly emerging and have achieved very good results in actual education. In the era of education big data, how to fully tap the value of big data for online education practice, decision-making, evaluation and research, and how to avoid the risk of big data are important issues in the current education reform and development. This paper analyzes the application of the current scientific paradigm in education, constructs the construction paradigm of online education evaluation model, and puts forward a new education concept in order to promote the development of the new paradigm of big data online education technology research. Applying this paradigm, a series of educational evaluation models are constructed from the macro, miso and micro levels, which play a positive role in the research, decision-making, practice and evaluation of related fields.

In a world increasingly driven by data, most developed economies are leveraging big data to achieve greater feats in various sectors of their economies. From advertisement, commerce, healthcare, and energy to defense, big data has given new insights into the huge volume of data accumulated over the past few decades that is helping reshape our knowledge and understanding of these sectors. Unfortunately, the same cannot be said about the state of big data in the developing world, where investments in IT infrastructure are dangerously low, keeping huge proportions of the population offline. This chapter discussed the challenges that exist in developing countries, which affect the smooth take-off of big data and data science as well as recommendations as to how countries and companies in the developing world can overcome these challenges to harness the benefits and opportunities presented by this technology.