Emerging Data Technologies Research Articles

The false prosperity and promising future: Effects of data resources on bank efficiency

This study investigates the impact of data resources on bank efficiency. Based on data management theory, we measure bank data resources and find that higher data resources are associated with lower cost efficiency using data on Chinese banks over the period 2013 to 2019. However, data resources have no significant impact on profit efficiency. We discover that data resources reduce cost efficiency possibly due to increased overheads and decreased capital adequacy ratio. Furthermore, we conclude that emerging data technologies can reduce the cost burden of data resources on commercial banks, resulting in synergistic technical benefits. The findings are of timely policy importance and practical relevance for regulators, policy-makers, and bank managers. Our main results hold for a battery of endogeneity and robustness tests.

Effective Data Protection in Nigeria: Challenges

The importance of protecting data and privacy cannot be overemphasized in this day of technological and digital breakthroughs. Nigeria, a developing nation, is prone to suffering from the shortcomings of conventional methods of data protection as well as from the cost of insufficient legislation to secure the data or the privacy of the data users. This has brought up a number of issues, including the applicability and responsiveness of current laws and how it tends to handle or manage violations. It is imperative to address these issues and challenges faced by data and privacy protection in Nigeria, given the complexity of the emerging data technology and the privacy challenges posed by the use and retention of data. The paper identified the lack of a comprehensive data protection legislation as well as their non-enforcement as the main barrier to effective data protection in the country. In order to solve some of the issues affecting effective data privacy in Nigeria, this paper proffers cogent and practical recommendations.

Exploring data mining: facets and emerging trends

PurposeData mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data.Design/methodology/approachAn extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field.FindingsThe study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences.Practical implicationsThe paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful.Originality/valueThe paper tries to highlight the current trends and facets of data mining.

How emerging data technologies can increase trust and transparency in fisheries

Abstract The ubiquitous spread of digital networks has created techniques which can organize, store, and analyse large data volumes in an automized and self-administered manner in real time. These technologies will have profound impacts on policy, administration, economy, trade, society, and science. This article sketches how three digital data technologies, namely the blockchain, data mining, and artificial intelligence could impact commercial fisheries including producers, wholesalers, retailers, consumers, management authorities, and scientist. Each of these three technologies is currently experiencing an enormous boost in technological development and real-world implementation and is predicted to increasingly affect many aspects of fisheries and seafood trade. As any economic sector acting on global scales, fishing and seafood production are often challenged with a lack of trust along various steps of the production process and supply chain. Consumers are often not well informed on the origin and production methods of their product, management authorities can only partly control fishing and trading activities and producers can be challenged by low market prices and competition with peers. The emerging data technologies can improve the trust among agents within the fisheries sector by increasing transparency and availability of information from net to plate.

Special Section Editorial on "High Performance Computing in Simulation and Optimization of Dynamic Transportation Networks\" [Guest Editorial

The articles in this special section focus on high performance computing that is used in the simulation and optimization of dynamic transportation systems. Some of the following topics are covered: High performance computing in modeling and optimization of dynamic network traffic; Simulation- based routing and assignment of dynamic network traffic; Emerging data technologies to support dynamic network management applications; and Relevant case studies and applications.

Emerging data technology in structural health monitoring: compressive sensing technology

This paper presents a new development in structural health monitoring (SHM) data management, processing, and mining based on compressive sensing (CS) technique, which is an emerging signal processing technique. CS provides a new sampling theory to reduce data acquisition based on the idea that sparse or compressible signals can be exactly reconstructed from highly incomplete random sets of measurements. CS overcomes the restrictions of the Shannon theorem regarding the sampling frequency, allowing the use of fewer sampling resources, a higher sampling rate, and reduced hardware and software complexity to obtain the measurements. In addition to data acquisition, CS also can be used to find the sparse solutions for linear algebraic equations, which is useful for the inverse problem of SHM. In this paper, the applications of CS for SHM are presented, including CS for acceleration data, lost data recovery for wireless sensors, CS for acoustic emission data, moving loads distribution identification, and structure damage identification. The investigation results show that CS has good application potential in SHM.

Big Data and Linked Data: The Emerging Data Technology for the Future of Librarianship

Big Data and Linked Data: The Emerging Data Technology for the Future of Librarianship

Knowledge discovery through creating formal contexts

Knowledge discovery is important for systems that have computational intelligence in helping them learn and adapt to changing environments. By representing, in a formal way, the context in which an intelligent system operates, it is possible to discover knowledge through an emerging data technology called formal concept analysis (FCA). This paper describes a tool called FcaBedrock that converts data into formal contexts for FCA. This paper describes how, through a process of guided automation, data preparation techniques such as attribute exclusion and value restriction allow data to be interpreted to meet the requirements of the analysis. Examples are given of how formal contexts can be created using FcaBedrock and then analysed for knowledge discovery, using real datasets. Creating formal contexts using FcaBedrock is shown to be straightforward and versatile. Large datasets are easily converted into a standard FCA format.

New paradigms for modelling species distributions?

Summary The management of both desirable and undesirable species requires an understanding of the factors determining their distribution. Quantitative distribution models offer simple methods for formulating the species–habitat link and the means not only for predicting where species should occur, but also for understanding the factors involved. Generalized linear modelling, in particular, links the incidence of species to habitat variables, and has increasingly formed the backbone of the modelling approaches used. New ‘data technologies’, such as remote sensing and geographical information systems, have further broadened these modelling applications to almost any ecological system and any species for which there are distribution data. Many previous approaches have aimed to identify the most parsimonious model with the best suite of predictors, selected on the basis of null hypothesis testing. However, information‐theoretic approaches based on Akaike's information criterion allow the selection of a best approximating model or a subset of models from a set of candidates. Information‐theoretic approaches require a deeper understanding of the biology of the system modelled and may well become an improved paradigm for species distribution modelling. Synthesis and applications. This special profile of six papers demonstrates the development in methodology used in species distribution modelling. The papers show how information‐theoretic approaches can be coupled with emerging data technologies to address issues of conservation significance. With conservation biology and applied ecology at the forefront of many of the basic science developments so far, we expect these methods to pervade other areas of ecological research more fully in future.

Clinical information management systems: an emerging data technology for inpatient pharmacies.

Institutional pharmacy practice has undergone dramatic changes over the past few years. Hospitals are moving from using handwritten medication profiles toward implementing computerized ordering and dispensing systems. Efficiencies resulting from these technologies have afforded pharmacists more time to participate in patient care activities, including those related to performance improvement and cost savings. Coupled with the shift in pharmacists' responsibilities is an increasing amount of data, both from our expanding knowledge about new and existing medications and our ability to better capture data. Yet, data captured within many hospitals remain focused on the process of delivering care, which may be limited to billing or administrative information.[1] Data concerning patient outcomes are acquired piecemeal,[2] requiring pharmacists to seek information from disparate sources when evaluating clinical issues.