Big Data and Urban Planning in Pakistan: A Case Study of The Urban Unit

This Major Paper presents research on the use of big data for urban planning and smart cities in the global South. It does so through a case study of the use of data at the Urban Unit in the province of Punjab, Pakistan. Based on a series of interviews and extended literature review, I trace the evolution of a science of cities and the growth of urban informatics and smart cities. I then define big data and discuss its related opportunities and limitations. The bulk of this Paper consists of a case study of the Urban Unit and research findings regarding the use of data in planning in Pakistan. A number of challenges to the use of data are identified, classified into challenges regarding data access and reliability, data literacy, and institutional challenges. A major finding is that the practice of urban planning in Pakistan is quite limited in a number of ways. The final chapter shares recommendations from interviewees and reflections on research findings, focusing on the politics of data. The paper ends by discussing future research directions

The role of big data in smart city

The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the existing communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model that can manage big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data

Big Data: The Engine to Future Cities—A Reflective Case Study in Urban Transport

In an era of smart cities, artificial intelligence and machine learning, data is purported to be the ‘new oil’, fuelling increasingly complex analytics and assisting us to craft and invent future cities. This paper outlines the role of what we know today as big data in understanding the city and includes a summary of its evolution. Through a critical reflective case study approach, the research examines the application of urban transport big data for informing planning of the city of Sydney. Specifically, transport smart card data, with its diverse constraints, was used to understand mobility patterns through the lens of the 30 min city concept. The paper concludes by offering reflections on the opportunities and challenges of big data and the promise it holds in supporting data-driven approaches to planning future cities

The Origin of Arabic Lexicography: Its Emergence and Evolution

This study aims to identify and describe 1) the history of the emergence and evolution of Arabic lexicography, 2) the implications of Arabic lexicography for learning Arabic. This research is a historical study using a qualitative approach with library research methods and documentation techniques. The data analysis used in this research is descriptive qualitative analysis. The results of the analysis show that 1) the emergence of al fikr al mu'jamiy was based on the difficulty of Arabs in understanding the meanings contained in the Qur'an which was named as gha>ri>bu al Quran, during this evolution period of Arabic lexicography gave an impact against the emergence of variations of the Arabic lexicon and entry systems with its characteristics one each other. This variety is the pure thought and innovation by lexicologists at that time in order to find the most appropriate method and system to codify Arabic vocabulary to make the Arabic lexicon provide an umbrella that can protect the purity and sustainability of the Arabic language from the challenges, 2) Arabic lexicography has a big impact in learning Arabic in the fields of morphology, syntax, and semantics which are language subsystems.

Data Safe Havens and Trust: Toward a Common Understanding of Trusted Research Platforms for Governing Secure and Ethical Health Research

In parallel with the advances in big data-driven clinical research, the data safe haven concept has evolved over the last decade. It has led to the development of a framework to support the secure handling of health care information used for clinical research that balances compliance with legal and regulatory controls and ethical requirements while engaging with the public as a partner in its governance. We describe the evolution of 4 separately developed clinical research platforms into services throughout the United Kingdom-wide Farr Institute and their common deployment features in practice. The Farr Institute is a case study from which we propose a common definition of data safe havens as trusted platforms for clinical academic research. We use this common definition to discuss the challenges and dilemmas faced by the clinical academic research community, to help promote a consistent understanding of them and how they might best be handled in practice. We conclude by questioning whether the common definition represents a safe and trustworthy model for conducting clinical research that can stand the test of time and ongoing technical advances while paying heed to evolving public and professional concerns

Big Data and Changes in Audit Technology: Contemplating a Research Agenda

This study explores the most recent episode in the evolution of audit technology, namely the incorporation of Big Data and Data Analytics (BDA) into audit firm approaches. Drawing on 22 interviews with individuals with significant experience in developing, implementing or assessing the impact of BDA in auditing, together with publicly available documents on BDA published within the audit field, the paper provides a holistic overview of BDA-related changes in audit practice. In particular, the paper focuses on three key aspects, namely the impact of BDA on the nature of the relationship between auditors and their clients; the consequences of the technology for the conduct of audit engagements and the common challenges associated with embedding BDA in the audit context. The study’s empirical findings are then used to establish an agenda of areas suitable for further research on the topic. The study is one of the first empirical accounts providing a perspective on the rise of BDA in auditing

Sustainable supply chain management trends in world regions: A data-driven analysis

This study proposes a data-driven analysis that describes the overall situation and reveals the factors hindering improvement in the sustainable supply chain management field. The literature has presented a summary of the evolution of sustainable supply chain management across attributes. Prior studies have evaluated different parts of the supply chain as independent entities. An integrated systematic assessment is absent in the extant literature and makes it necessary to identify potential opportunities for research direction. A hybrid of data-driven analysis, the fuzzy Delphi method, the entropy weight method and fuzzy decision-making trial and evaluation laboratory is adopted to address uncertainty and complexity. This study contributes to locating the boundary of fundamental knowledge to advance future research and support practical execution. Valuable direction is provided by reviewing the existing literature to identify the critical indicators that need further examination. The results show that big data, closed-loop supply chains, industry 4.0, policy, remanufacturing, and supply chain network design are the most important indicators of future trends and disputes. The challenges and gaps among different geographical regions is offered that provides both a local viewpoint and a state-of-the-art advanced sustainable supply chain management assessment

The interaction of artificial intelligence and design thinking In the development of HR and decision-making trends

This paper is a qualitative exploratory study, and a suggestive theory that aims to explore contemporary trends in HR policies in relation to technology. More precisely, the paper is a content-analysis research, aimed to explore the relationship between decision-making and data-driven business environment, and the extent to which AI and DT augment decision-making, if at all. Artificial intelligence is a physical concept which is used to describe and examine the impact that technology has on HRM practices. Design thinking is an abstract concept used to describe and examine the evolution of best leadership practices in terms of HRM processes. Before I started conducting this research, my focus was on AI, as a concept bound to change the face of traditional decision-making. Copious amount of data that is produced, extracted and stored daily, requires respective analysis. As such, I approached my respondents with the knowledge I gathered through personal research and during the creation of theoretical framework. As the research were advancing, I began to realise the extent to which these concepts provide insights into the relationship between the culture of design (thinking) and notion of artificial (intelligence) in decision-making. These two concepts were used to test the extent to which decision-making can be augmented with their use, and how they influence organisational hierarchy. From the side of the technology, AI is looking into the nature of Big Data and how it is used to exploit information for competitive HRM. DT is used to exploit the extent to which Big Data is used to broaden decision-making solutions. Together, this paper is examining the potential of these relationships, and if it in fact renders greater decision-making advantage, by accelerating the process with AI and disrupting traditional decision-making with DT. The paper used the Big Data Maturity Model (BDMM) to filter the findings and study this relationship accordingly. The model is comprised of five interconnected stages which test big data maturity of companies, as well as of their employees. Stages were divided according to goals of the paper and the two concepts. Moreover, the codes that were used to filter the findings served as additional differentiating points in the stages. The research provides insights into the synthesis of AI and DT and how they are perceived by decision-makers. The conclusions give an overview of advantages and challenges faced by HR managers when implementing AI and DT in decision-making and the subsequent room for research of this relationship

Big data and analytics in tourism and hospitality: a perspective article

Purpose This study aims to discuss the evolution of Big Data (BD) and Analytics in the tourism and hospitality field. It analyses the important role that BD has played so far in tourism and hospitality research and delineates how it might evolve in the future. Design/methodology/approach In line with the Platinum Jubilee Special Issue of Tourism Review, this work consists of a critical and conceptual analysis including a mini literature review of recent work in the area at the intersection of BD and tourism and hospitality research. Findings Findings suggest that tourism and hospitality scholars are increasingly aware of and adopting BD approaches to retrieve, collect, analyse, report and visualise their data. However, a number of avenues for improvement in the use and interpretation of BD and BD analytics as both sets of methods and technology need to be developed. Moreover, BD analytics promise to enhance a number of digital technologies in tourism and hospitality such as AI and IoT that heavily rely on data. As such, the authors envision that a new digital entrepreneurship field might be shaped within the tourism and hospitality literature. Research pathways for future inquiry at the intersection of BD and tourism and hospitality are outlined. Originality/value While thinking retrospectively about research revolving around BD and its role in the tourism and hospitality research field so far, this study also addresses the challenges pertaining to how BD research will be conducted in the next seven decades within tourism and hospitality