Development Of Big Data Analytics Research Articles

The role of Big Data and Data Science in the context of information security and cybersecurity

ObjectivesIn the area of the fastest growing fields of ICT technology which determine the successive stages of progress in the field of online electronic banking, it is necessary to disseminate standards for conducting financial operations carried out in the so-called cloud as well as using the large data sets located in the so-called Big Data platforms.Material and methodsFor the purposes of this article, research has been carried out on the broad issue of Big Data Analytics applications in the context of social, normative, economic, environmental, climate aspects. A questionnaire survey was conducted. The method of analysis of literature sources concerning the subject of the article was applied.ResultsCurrently emerging trends in the development of technology based on Big Data dataset platforms usually allow to perform multidimensional calculations and reported results of analyzes in real time. The analyzes conducted on huge data sets allow for comprehensive, multi-aspect risk assessments at the level of the whole entity. During the SARS-CoV-2 (Covid-19) coronavirus pandemic, there was an increase in the importance of analytics based on Big Data Analytics. During the pandemic, there was also an increase in the digitisation and internetisation of business processes. This also resulted in an increase in the scale of cybercrime development.ConclusionsThe technological revolution currently taking place, defined as Industry 4.0, is motivated by the development of the following factors: Big Data database technologies, cloud computing, machine learning, Internet of Things, artificial intelligence, Business Intelligence and other technologies of advanced data processing Data Mining. On the basis of the development new technological solutions, in recent years there has been a dynamic development of Big Data Analytics.

Development of Big Data Analytics Technology on Sea Freight Operational Performance Using the Agile Model

The development of industry 4.0 is still difficult for a country to produce all goods according to the required needs. Big data analytics technology has the potential to revolutionize how logistics companies, including freight forwarders, can facilitate real-time monitoring in the supply chain by increasing flexibility. Management information systems produce a number of data from different sources and in different formats according to business needs. The development of big data analytics uses a dynamic system development method or model (DSDM), which is a dynamic system development model that uses a rapid application development (RAD) approach. Software development with an emphasis on collaboration, customer engagement, and continuous delivery of working software. This model is very suitable for the development of big data analytics technology that can provide solutions that often involve complex and rapidly changing requirements with large amounts of data and the business need to provide feedback from stakeholders in real-time. It is hoped that the final results of this research will provide insight into sea freight operations that are more informative and efficient for the logistics industry in Indonesia.

Advancing beyond technicism when managing big data in companies’ decision-making

PurposeIn recognising the key role of business intelligence and big data analytics in influencing companies’ decision-making processes, this paper aims to codify the main phases through which companies can approach, develop and manage big data analytics.Design/methodology/approachBy adopting a research strategy based on case studies, this paper depicts the main phases and challenges that companies “live” through in approaching big data analytics as a way to support their decision-making processes. The analysis of case studies has been chosen as the main research method because it offers the possibility for different data sources to describe a phenomenon and subsequently to develop and test theories.FindingsThis paper provides a possible depiction of the main phases and challenges through which the approach(es) to big data analytics can emerge and evolve over time with reference to companies’ decision-making processes.Research limitations/implicationsThis paper recalls the attention of researchers in defining clear patterns through which technology-based approaches should be developed. In its depiction of the main phases of the development of big data analytics in companies’ decision-making processes, this paper highlights the possible domains in which to define and renovate approaches to value. The proposed conceptual model derives from the adoption of an inductive approach. Despite its validity, it is discussed and questioned through multiple case studies. In addition, its generalisability requires further discussion and analysis in the light of alternative interpretative perspectives.Practical implicationsThe reflections herein offer practitioners interested in company management the possibility to develop performance measurement tools that can evaluate how each phase can contribute to companies’ value creation processes.Originality/valueThis paper contributes to the ongoing debate about the role of digital technologies in influencing managerial and social models. This paper provides a conceptual model that is able to support both researchers and practitioners in understanding through which phases big data analytics can be approached and managed to enhance value processes.

Ethical considerations surrounding health-related big data research.

As health-related big data research (HRBDR) has drastically increased over the last years due to the rapid development of big data analytics, a range of important ethical issues are raised. In this study, a systematic literature review was conducted. Several and interesting results emerged from this review. The term ″big data″ has not yet been clearly defined. The already existing ethical principles and concepts need to be revisited in the new HRBDR context. Traditional research ethics notions like privacy and informed consent are to be reconsidered. HRBDR creates new ethical issues such those related to trust / trustworthiness and public values such as reciprocity, transparency, inclusivity and common good. The implementation of dynamic consent rather than broad consent is currently highlighted as the more satisfying solution. Ethical review committees in their current form are ill-suited to provide exclusive ethical oversight on HRBDR projects. Expanding Ethical Review Committees' purview and members' expertise, as well as creating novel oversight bodies by promoting a co-governance system including public and all the stakeholders involved are strongly recommended. The mechanism of ″social licence″, that is, informal permissions granted to researchers by society, can serve as a guideline. High-stakes decisions are often made under uncertainty. Machine learning algorithms are highly complex and in some cases opaque, and may yield biased decisions or discrimination. Improved interdisciplinary dialogue along with considering aspects like auditing, benchmarking, confidence / trust and explainability /interpretability may address concerns about HRBDR ethics. Finally and most importantly, research ethics shifts towards a population-based model of ethics.

The path from big data analytics capabilities to value in hospitals: a scoping review

BackgroundAs the uptake of health information technologies increased, most healthcare organizations have become producers of big data. A growing number of hospitals are investing in the development of big data analytics (BDA) capabilities. If the promises associated with these capabilities are high, how hospitals create value from it remains unclear. The present study undertakes a scoping review of existing research on BDA use in hospitals to describe the path from BDA capabilities (BDAC) to value and its associated challenges.MethodsThis scoping review was conducted following Arksey and O’Malley’s 5 stages framework. A systematic search strategy was adopted to identify relevant articles in Scopus and Web of Science. Data charting and extraction were performed following an analytical framework that builds on the resource-based view of the firm to describe the path from BDA capabilities to value in hospitals.ResultsOf 1,478 articles identified, 94 were included. Most of them are experimental research (n=69) published in medical (n=66) or computer science journals (n=28). The main value targets associated with the use of BDA are improving the quality of decision-making (n=56) and driving innovation (n=52) which apply mainly to care (n=67) and administrative (n=48) activities. To reach these targets, hospitals need to adequately combine BDA capabilities and value creation mechanisms (VCM) to enable knowledge generation and drive its assimilation. Benefits are endpoints of the value creation process. They are expected in all articles but realized in a few instances only (n=19).ConclusionsThis review confirms the value creation potential of BDA solutions in hospitals. It also shows the organizational challenges that prevent hospitals from generating actual benefits from BDAC-building efforts. The configuring of strategies, technologies and organizational capabilities underlying the development of value-creating BDA solutions should become a priority area for research, with focus on the mechanisms that can drive the alignment of BDA and organizational strategies, and the development of organizational capabilities to support knowledge generation and assimilation.

The Role of Big Data Analytics in Manufacturing Agility and Performance: Moderation–Mediation Analysis of Organizational Creativity and of the Involvement of Customers as Data Analysts

AbstractThe involvement of customers as data analysts enables firms to gain valuable insights and create value from big data. We provide a theoretical explanation, drawn from the resource‐based view, for the influence of the involvement of customers as data analysts and of the development of big data analytics (BDA) capabilities in business‐to‐business contexts as routes to manufacturing agility and performance. Our study empirically tested a framework in which organizational creativity and the involvement of customers as data analysts may differentially influence the relationship between BDA capabilities and manufacturing agility. We further tested whether the relative impact of manufacturing agility depends on organizational creativity and the involvement of customers as data analysts. To test our proposed framework, we took a partial least‐squares structural modelling approach using data collected through a survey involving 179 engineering manufacturers operating across different industrial sectors in Pakistan. We provide evidence for organizational creativity and customer involvement, presenting a promising opportunity for manufacturers to gain better insights from resources, and for the deployment of BDA capabilities leading to better manufacturing agility and performance.

Mathematical Modelling and Big-Data Analytics for Student Performance

Mathematical Modelling and Big-data Analytics are playing a vital role in educational databases. The result of integrating technology to predict student performance along with Mathematical Modelling and Big-Data Analytics helps us to make better decisions about teaching and learning. Modelling involves formulating real-life situations or to convert the problems in mathematical explanations to a real or believable situation. However, Mathematical modelling are an essential enabler in Big-data and Developments in Big-data analytics require not only more computing, but also new advanced mathematical approaches. In this paper, our main aim to see how Mathematical Modelling and Big-Data analytics help in students’ learning and how they relate to each other.

An end-to-end model-based approach to support big data analytics development

Abstract We present BiDaML 2.0, an integrated suite of visual languages and supporting tool to help multidisciplinary teams with the design of big data analytics solutions. BiDaML tool support provides a platform for efficiently producing BiDaML diagrams and facilitating their design, creation, report and code generation. We evaluated BiDaML using two types of evaluations, a theoretical analysis using the “physics of notations”, and an empirical study with 1) a group of 12 target end-users and 2) five individual end-users. Participants mostly agreed that BiDaML was straightforward to understand/learn, and prefer BiDaML for supporting complex data analytics solution modeling than other modeling languages.

Dynamic Modal Accessibility Gap: Measurement and Application Using Travel Routes Data

Accessibility is a key concept in transportation research and an important indicator of people’s quality of life. With the development of big data analytics, dynamic accessibility that captures the temporal variations of accessibility becomes an important research focus. Few prior studies focus on comparative measures of dynamic accessibility to Points of Interest (POIs) by alternative travel modes. To fill this research gap, we propose a new index called dynamic modal accessibility gap (DMAG), which draws upon available data on residents’ real travel routes using different travel modes, as well as the data on POIs. We study the DMAG in the real-travel covered area, assuming POIs are only useful if it is within someone’s real-travel covered area. We then apply this DMAG methodology to Shanghai’s central city and peripheral area. In both cases, we measure the accessibility for public and private travel modes. As an example, one-week taxi GPS and metro smart card data, and POIs data are used to generate the DMAG index for 30-minute and 60-minute trip durations for weekdays and holidays. Results show that DMAG can reflect the pattern of temporal variations. The proposed DMAG analytical framework, which can be applied at both the user and the system levels, can support urban and transportation planning, and promote social equity and livability.

Customer experience management in the age of big data analytics: A strategic framework

Customer experience (CX) has emerged as a sustainable source of competitive differentiation. Recent developments in big data analytics (BDA) have exposed possibilities to unlock customer insights for customer experience management (CXM). Research at the intersection of these two fields is scarce and there is a need for conceptual work that (1) provides an overview of opportunities to use BDA for CXM and (2) guides management practice and future research. The purpose of this paper is therefore to develop a strategic framework for CXM based on CX insights resulting from BDA. Our conceptualisation is comprehensive and is particularly relevant for researchers and practitioners who are less familiar with the potential of BDA for CXM. For managers, we provide a step-by-step guide on how to kick-start or implement our strategic framework. For researchers, we propose some opportunities for future studies in this promising research area.

Flexible and durable wood-based triboelectric nanogenerators for self-powered sensing in athletic big data analytics

In the new era of internet of things, big data collection and analysis based on widely distributed intelligent sensing technology is particularly important. Here, we report a flexible and durable wood-based triboelectric nanogenerator for self-powered sensing in athletic big data analytics. Based on a simple and effective strategy, natural wood can be converted into a high-performance triboelectric material with excellent mechanical properties, such as 7.5-fold enhancement in strength, superior flexibility, wear resistance and processability. The electrical output performance is also enhanced by more than 70% compared with natural wood. A self-powered falling point distribution statistical system and an edge ball judgement system are further developed to provide training guidance and real-time competition assistance for both athletes and referees. This work can not only expand the application area of the self-powered system to smart sport monitoring and assisting, but also promote the development of big data analytics in intelligent sports industry.

Impacts of digitization on auditing: A Delphi study for Germany

The ongoing digitization of the economy presents challenges and opportunities for the auditing profession and requires both auditors and their clients to adapt. Against the background of current technological developments in big data analytics, artificial intelligence (AI), and blockchain technology, this study examines changes in auditing practices expected by German auditing professionals within the next five to ten years. It addresses the perception of auditing, the auditor–client relationship, regulations, structural and procedural changes for auditing firms, and the profile of the auditing profession. These will probably change with new technologies. We surveyed experts as part of a Delphi study in Germany conducted over two rounds. The results show that no far-reaching changes are expected within the given time horizon. The annual audit will increasingly evolve toward a continuous audit approach. Despite predominantly uncertain opinions, experts believe that new technologies will not replace the auditor, but rather will provide relief and support. Even if the job’s necessary requirements make it more difficult to remain in the profession, disruptive effects in auditors’ workplaces are not expected in the near future. Nevertheless, the consequences of using new technologies in the auditing process offer numerous future research opportunities.

Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process

This paper outlines real-world control challenges faced by modern-day biopharmaceutical facilities through the extension of a previously developed industrial-scale penicillin fermentation simulation (IndPenSim). The extensions include the addition of a simulated Raman spectroscopy device for the purpose of developing, evaluating and implementation of advanced and innovative control solutions applicable to biotechnology facilities. IndPenSim can be operated in fixed or operator controlled mode and generates all the available on-line, off-line and Raman spectra for each batch. The capabilities of IndPenSim were initially demonstrated through the implementation of a QbD methodology utilising the three stages of the PAT framework. Furthermore, IndPenSim evaluated a fault detection algorithm to detect process faults occurring on different batches recorded throughout a yearly campaign. The simulator and all data presented here are available to download at www.industrialpenicillinsimulation.com and acts as a benchmark for researchers to analyse, improve and optimise the current control strategy implemented on this facility. Additionally, a highly valuable data resource containing 100 batches with all available process and Raman spectroscopy measurements is freely available to download. This data is highly suitable for the development of big data analytics, machine learning (ML) or artificial intelligence (AI) algorithms applicable to the biopharmaceutical industry.

An Evaluation of the Right to Privacy in the Digital Age

Privacy does not always connote the act of being left alone or protection from home evasion. Privacy is in fact infringed every day. Over the years, there have been vast improvements in the availability, storage, and removal of personal information online, supported by developments in big data analytics. Google, Yahoo, Bing, Ask.com have been deemed to be search engines that users cannot ‘lie’ to. Facebook, Instagram, Twitter, LinkedIn monitor companions and places of users, Google Music, Apple Music tell the type of songs the user likes, Google Books, Amazon, Wattpad, tells the type of books enjoyed by the user, CCTV tells who was here and why, Taxify, Uber, OkadaGo, tells the location of the user, amongst others, which shows the dire need of maintaining a balance on the benefits of receiving information with the threat to the right to privacy. The idea of privacy revolves around keeping to the exclusion of the public eye what an individual considers as private. Data is a pollution problem in the digital age, bringing forth demands such as how it is accessed, how it is used, what it is used for, how it is disposed and forgotten, and the law does not really show a visible line between these rights. This essay therefore dissected the various arguments to the long lingering question, ‘Is privacy truly dead?’ drawing a conclusive remark thereafter.

The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects

The recent development of big data analytics (BDA) and the Internet of Things (IoT) technologies create a huge opportunity for both disaster management systems and disaster-related authorities (emergency responders, police, public health, and fire departments) to acquire state-of-the-art assistance and improved insights for accurate and timely decision-making. The motivation behind this research is to pave the way for effective utilization of the available opportunities that the BDA and IoT collaboratively offer to predict, understand and monitor disaster situations. Most of the conventional disaster management systems lack the support for multiple new data sources and real-time big data processing tools that can assist decision makers with quick and accurate results. This paper highlights the importance of BDA and IoT for disaster management and investigates recent studies directed towards the same. We classify a thematic taxonomy with several related attributes and inspect the prevalent solutions to propose a conceptual reference model for the deployment of BDA- and IoT-based disaster management environments. The reference model with its proposed integrated parameters can provide guidelines to harvest, transmit, manage, and analyze disaster data from various data sources to deliver updated and valuable information for disaster management. We also enumerate some important use cases from a disaster management perspective. Finally, we highlight the main research challenges that need to be addressed in such an important field of research.

A bibliometric analysis of research on Big Data analytics for business and management

PurposeThe purpose of this paper is to scrutinize and classify the literature linking Big Data analytics and management phenomena.Design/methodology/approachAn objective bibliometric analysis is conducted, supported by subjective assessments based on the studies focused on the intertwining of Big Data analytics and management fields. Specifically, deeper descriptive statistics and document co-citation analysis are provided.FindingsFrom the document co-citation analysis and its evaluation, four clusters depicting literature linking Big Data analytics and management phenomena are revealed: theoretical development of Big Data analytics; management transition to Big Data analytics; Big Data analytics and firm resources, capabilities and performance; and Big Data analytics for supply chain management.Originality/valueTo the best of the authors’ knowledge, this is one of the first attempts to comprehend the research streams which, over time, have paved the way to the intersection between Big Data analytics and management fields.

A survey towards an integration of big data analytics to big insights for value-creation

Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions.

Big Search in Cyberspace

With the rapid development of big data analytics, mobile computing, Internet of Things, cloud computing, and social networking, cyberspace has expanded to a cross-fused and ubiquitous space made up of human beings, things, and information. Internet applications have evolved from Web 1.0 to Web 2.0 and Web 3.0, and web information has seen an explosive growth, which is strongly promoting the advent of a global era of big data. In this ubiquitous cyberspace, traditional search engines can no longer fully satisfy the evolving needs of various types of users. Therefore, search engines must make completely innovative, revolutionary changes for the next generation of search, which is referred to as “big search”. This paper first studies the development needs of big search. Then, big search is defined, and the 5S properties (Sourcing, Sensing, Synthesizing, Solution, and Security) of big search, which are different from those of traditional search engines, are elaborated. Also, the paper provides a system architecture for big search, explores the key technologies that support the 5S properties, and describes potential application fields of big search technology. Finally, the research opportunities of big search are discussed.

Big Data and Climate Smart Agriculture - Review of Current Status and Implications for Agricultural Research and Innovation in India

Climate change will increase the vulnerability of agricultural production systems, unless scientists and farmers reorient their present approaches towards making them climate smart or climate resilient. The integration of recent developments in big data analytics and climate change science with agriculture can greatly accelerate agricultural research and innovation for climate smart agriculture (CSA). CSA refers to an integrated set of technologies and practices that simultaneously improve farm productivity and incomes, increase adaptive capacity to climate change effects, and reduce green house gas emissions from farming. It is a multistage, multiobjective, data-driven, and knowledge based approach to agriculture, with the farm as the most fundamental unit for both strategic and tactical decisions. This paper explores how big data analytics can accelerate research and innovation for CSA. Three levels at which big data can enhance farmer field level insights and actionable knowledge for the practice of CSA are identified: (i) developing a predictive capability to factor climate change effects to scales relevant to farming practice, (ii) speeding up plant breeding for higher productivity and climate resilience, and (iii) delivery of customized and prescriptive real-time farm knowledge for higher productivity, climate change adaptation and mitigation. The state-of-art on big data based approaches at each of the three levels is assessed. The paper also identifies the research and institutional challenges, and the way forward for leveraging big data in research and innovation aimed at climate smart agriculture in India.

Towards a Privacy Framework for India in the Age of the Internet

Over the last decade, there have been vast improvements in surveillance technology and the availability, storage, and mining of personal information online, supported by developments in big data analytics. This has created a public policy conundrum over balancing the benefits of big data with the threat to the right to privacy. In an environment of pervasive surveillance and intrusive technology, there is a need for improved protection of privacy rights through a mixture of legislation and regulation, and building public awareness and demand for safeguards. This paper makes a case for the need for privacy from both the State and the private sector; examines the jurisprudential development of the right to privacy in India, and lays down privacy principles, that will underlie any proposed privacy law. It then evaluates the Indian IT Act, and the recently legislated Aadhaar Act, against the proposed privacy principles.