Assessing Brunei Darussalam Public and Private Sector Readiness Towards Big Data Application

This exploratory study aims to assess and investigate Brunei Darussalam’s readiness in developing and applying big data technologies for its public and private sectors, using Social, Technological, Environmental and Policy (STEP) framework. The results show that the population are digitally literate (Social) and utilises smart devices as well as internet network connectivity that is widely offered by the local telecommunications company (Technology). The government of Brunei Darussalam established multiple digital transformation initiatives including implementation of 5G connectivity as well as digital economy masterplan to digitally transformed in the near future (Environment). Regardless of the absence of national digital data privacy policy (Policy) in Brunei, the recent nation’s successful big data application in public sector – BruHealth Application – to contain Covid-19 community spread was achieved. Alas, the existence of such policy in the near future will create opportunities for the local private sectors to capitalise big data technologies to their business strategies.

Assessing Brunei Darussalam Public and Private Sector Readiness Towards Big Data Application

This exploratory study aims to assess and investigate Brunei Darussalam’s readiness in developing and applying big data technologies for its public and private sectors, using Social, Technological, Environmental and Policy (STEP) framework. The results show that the population are digitally literate (Social) and utilises smart devices as well as internet network connectivity that is widely offered by the local telecommunications company (Technology). The government of Brunei Darussalam established multiple digital transformation initiatives including implementation of 5G connectivity as well as digital economy masterplan to digitally transformed in the near future (Environment). Regardless of the absence of national digital data privacy policy (Policy) in Brunei, the recent nation’s successful big data application in public sector – BruHealth Application – to contain Covid-19 community spread was achieved. Alas, the existence of such policy in the near future will create opportunities for the local private sectors to capitalise big data technologies to their business strategies.

Why Personal Dreams Matter: How professionals affectively engage with the promises surrounding data-driven healthcare in Europe

Recent buzzes around big data, data science and artificial intelligence portray a data-driven future for healthcare. As a response, Europe's key players have stimulated the use of big data technologies to make healthcare more efficient and effective. Critical Data Studies and Science and Technology Studies have developed many concepts to reflect on such overly positive narratives and conduct critical policy evaluations. In this study, we argue that there is also much to be learned from studying how professionals in the healthcare field affectively engage with this strong European narrative in concrete big data projects. We followed twelve hospital-based big data pilots in eight European countries and interviewed 145 professionals (including legal, governance and ethical experts, healthcare staff and data scientists) between 2018 and 2020. In this study, we introduce the metaphor of dreams to describe how professionals link the big data promises to their own frustrations, ideas, values and experiences with healthcare. Our research answers the question: how do professionals in concrete data-driven initiatives affectively engage with European Union's data hopes in their ‘dreams’ – and with what consequences? We describe the dreams of being seen, of timeliness, of connectedness and of being in control. Each of these dreams emphasizes certain aspects of the grand narrative of big data in Europe, makes particular assumptions and has different consequences. We argue that including attention to these dreams in our work could help shine an additional critical light on the big data developments and stimulate the development of responsible data-driven healthcare.

Healthcare Big Data

Big data has unlocked a new opening in healthcare. Thanks to the considerable benefits and opportunities, it has attracted the momentous attention of all the stakeholders in the healthcare industry. This chapter aims to provide an overall but thorough understanding of healthcare big data. The chapter covers the 10 ‘V's of healthcare big data as well as different healthcare data analytics including predictive and prescriptive analytics. The obvious advantages of implementing big data technologies in healthcare are meticulously described. The application areas and a good number of practical use cases are also discussed. Handling big data always remains a big challenge. The chapter identifies all the possible challenges in realizing the benefits of healthcare big data. The chapter also presents a brief survey of the tools and platforms, architectures, and commercial infrastructures for healthcare big data.

A Brief Survey on Big Data in Healthcare

This article presents a brief introduction to big data and big data analytics and also their roles in the healthcare system. A definite range of scientific researches about big data analytics in the healthcare system have been reviewed. The definition of big data, the components of big data, medical big data sources, used big data technologies in present, and big data analytics in healthcare have been examined under the different titles. Also, the historical development process of big data analytics has been mentioned. As a known big data analytics technology, Apache Hadoop technology and its core components with tools have been explained briefly. Moreover, a glance of some of the big data analytics tools or platforms apart from Hadoop eco-system were given. The main goal is to help researchers or specialists with giving an opinion about the rising importance of used big data analytics in healthcare systems.

Big Data Technologies and Management

Developments in information technology and its prevalent growth in several areas of business, engineering, medical, and scientific studies are resulting in information as well as data explosion. Knowledge discovery and decision making from such rapidly growing voluminous data are a challenging task in terms of data organization and processing, which is an emerging trend known as big data computing. Big data has gained much attention from the academia and the IT industry. A new paradigm that combines large-scale compute, new data-intensive techniques, and mathematical models to build data analytics. Thus, this chapter discusses the background of big data. It also discusses the various application of big data in detail. The various related work and the future direction would be addressed in this chapter.

Development on advanced technologies – design and development of cloud computing model

Big Data has been created from virtually everything around us at all times. Every digital media interaction generates data, from computer browsing and online retail to iTunes shopping and Facebook likes. This data is captured from multiple sources, with terrifying speed, volume and variety. But in order to extract substantial value from them, one must possess the optimal processing power, the appropriate analysis tools and, of course, the corresponding skills. The range of data collected by businesses today is almost unreal. According to IBM, more than 2.5 times four million data bytes generated per year, while the amount of data generated increases at such an astonishing rate that 90 % of it has been generated in just the last two years. Big Data have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. This paper presents a view of the BD challenges and methods to help to understand the significance of using the Big Data Technologies. This article based on a bibliographic review, on texts published in scientific journals, on relevant research dealing with the big data that have exploded in recent years, as they are increasingly linked to technology

A Refection on Digitalized Creation of Chinese Internet Big Movies in the Context of Internet

The 21st century is the era of the Internet, where movies see new vitality in artistic creation under the concept of the Internet+. Internet Big movies, a new type of films born with the advancement of Internet technology, have seen rapid development in less than a decade, having gradually established a mature production model. This paper provides reflection and analysis on the new phenomena in Chinese film production that involve numerous applications of big data technologies.

The Metamorphosis (of RAM3S)

The real-time analysis of Big Data streams is a terrific resource for transforming data into value. For this, Big Data technologies for smart processing of massive data streams are available, but the facilities they offer are often too raw to be effectively exploited by analysts. RAM3S (Real-time Analysis of Massive MultiMedia Streams) is a framework that acts as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, so as to facilitate the implementation of the former on top of the latter. RAM3S has been proven helpful in simplifying the deployment of non-parallel techniques to streaming platforms, such as Apache Storm or Apache Flink. In this paper, we show how RAM3S has been updated to incorporate novel stream processing platforms, such as Apache Samza, and to be able to communicate with different message brokers, such as Apache Kafka. Abstracting from the message broker also provides us with the ability to pipeline several RAM3S instances that can, therefore, perform different processing tasks. This represents a richer model for stream analysis with respect to the one already available in the original RAM3S version. The generality of this new RAM3S version is demonstrated through experiments conducted on three different multimedia applications, proving that RAM3S is a formidable asset for enabling efficient and effective Data Mining and Machine Learning on multimedia data streams.