Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method

The application of industry 4.0 in banking presents many challenges, with several operational risks related to downtime and timeout services due to system failures. One of the operational risk management steps is to estimate the value of the maximum potential losses. The purpose of this study is to estimate the maximum potential losses for digital banking transaction risks. The method used for estimating risks is the EVaR method. There are several steps in this study. The first step is to resample the data using MEBoot. This process is a simulation of the operational risk loss data of digital banking. Next, the threshold value is determined to obtain the extreme data value. Then, a Kolmogorov–Smirnov test is conducted to fit the data with the GPD. Afterward, the GPD parameter is estimated. Then, EVaR is calculated using a portfolio approach to obtain a combination of risk values as maximum potential losses. The analysis results show that the maximum potential loss is IDR144,357,528,750.94. The research results imply that the banks need to pay attention to the maximum potential losses of digital financial transactions as a reference for risk management. Therefore, banks can anticipate the adequacy of reserve funds for these potential risks.

Construction of Power Transmission and Transformation Project Cost Information Platform Based on Big Data Analysis

With the reform of China’s power system, power transmission and transformation project (hereinafter referred to as PTATP) are gradually developing in the direction of integration, informatization, large-scale and systematization. Therefore, the traditional project cost can no longer meet the needs of the society, which requires the project cost based on BD (hereinafter referred to as BD) technology. Through the information platform (hereinafter referred to as IPF), we can collect a lot of information, including policies and regulations database, talent and machine price information database, project cost index database, industry information database, etc., which will provide important support for project cost. Project cost informatization will solve the problems of low information sharing rate, low information value and high information cost, which will more scientifically complete the cost of PTATP. Based on BD technology, we can collect, sort out and analyze the cost information data of PTATP, which will fully explore the data value. Firstly, this paper analyzes the main algorithms needed for project cost. Finally, this paper constructs a PTATP cost IPF based on BD analysis, which will provide accurate countermeasures.

Toward a Conceptualization of Big Data Value Chain

The generation of digital devices such as web 2.0, smartphones, social media and sensors has led to a growing rate of data creation. The volume of data available today for organizations is big. Data are produced extensively every day in many forms and from many different sources. Accordingly, firms in several industries are increasingly interested in how to leverage on these “big data” to draw valuable insights from the various kinds of data and to create business value. The aim of this chapter is to provide an integrated view of big data management. A conceptualization of big data value chain is proposed as a research model to help firms understand how to cope with challenges, risks and benefits of big data. The suggested big data value chain recognizes the interdependence between processes, from business problem identification and data capture to generation of valuable insights and decision making. This framework could provide some guidance to business executives and IT practitioners who are going to conduct big data projects in the near future.

PRIMARY SCHOOL STUDENTS’ READING LEVELS OF LINE GRAPHS

The aim of this research was to describe the errors and reading levels that 6th and 7th grade Chilean primary school children reach when working with line graphs. To achieve this objective, we gave a questionnaire, previously validated by experts with two open-ended tasks, to a sample of 745 students from different Chilean cities. In the first task, we asked the children to read the title of the graph, describe the variables represented and perform a direct and inverse reading of a data value. In the second task, where we address the visual effect of a scale change in a representation, the students had to select the line graph more convenient to a candidate. Although both tasks were considered easy for the grade levels targeted, only some of the students achieved the highest reading level and many made occasional errors in the reading of the graphs. Abstract: Spanish El objetivo de esta investigación es describir los errores y niveles de lectura que alcanzan estudiantes chilenos de 6º y 7º grado de Educación Primaria al trabajar con gráficos de líneas. Para lograr este objetivo, se aplicó un cuestionario, previamente validado por expertos, con dos tareas abiertas a una muestra de 745 estudiantes de diferentes ciudades chilenas. En la primera tarea, se pidió que leyeran el título del gráfico, indicaran las variables representadas y realizaran una lectura directa y otra inversa de un valor de datos. En la segunda tarea, los estudiantes deben seleccionar y justificar el gráfico de líneas más conveniente para respaldar a un candidato, donde se aborda el efecto visual de cambio de escala en una representación. Aunque ambas tareas fueron fáciles, solo una parte de los estudiantes logró el máximo nivel de lectura y aparecieron errores ocasionales en la lectura de los gráficos.

Developing Information Model of Central Line-Associated Bloodstream Infection (CLABSI) Prevention

CLABSIs are one of the most lethal and costly types of healthcare associated infections (HAIs). Regulatory organizations have mandated hospitals to submit monthly surveillance reports. However, there is an inaccuracy of presenting this report because of the lack of data standardization. This descriptive qualitative study aimed to develop a CLABSI prevention Information Model (IM) so the CLABSI prevention guidelines can be incorporated into structured nursing documentations. The flowsheet metadata stored in the Clinical Decision Repository was analyzed using an advanced analytics tool. The CLABSI prevention flowsheet data were mapped to 25 concepts, 45 data attributes and over 200 data value sets after organizing hierarchical structures. Seven domains of CLABSI prevention were identified in a CLABSI prevention IM. It would provide tangible benefits to create a practice reminder of the high risk for CLABSIs based on the nursing flowsheet data sets and multidisciplinary Electronic Health Record (EHR).

Market Structure of Scooter Segment of Two Wheeler Industry in India

Transportation system of a country has a noteworthy role to play in the development of an economy and its sectors. Automobile sector occupies a prominent place in the fabric of Indian economy. Presently, India has already touched the threshold of a major take off in the next decade and beyond to becoming one of the largest automotive (vehicle and component makers) manufacturers in the world. The objective of study was to measure the market structure of scooter segment of two wheeler industry in India for eight financial years, i.e. from the year 2011-12 to the year 2018-19. Descriptive analysis was conducted to present a profile of the industry which included analysis of average, standard deviation, compound annual growth rate, frequency, percentage of data value for each of the variables. The results of the study revealed that overall, there is existence of oligopoly form of market structure in the scooter segment of two wheeler industry in India.

Project Data Categorization, Adoption Factors, and Non-Functional Requirements for Blockchain Based Digital Twins in the Construction Industry 4.0

As key technologies of the fourth industrial revolution, blockchain and digital twins have great potential to enhance collaboration, data sharing, efficiency, and sustainability in the construction industry. Blockchain can improve data integrity and enhance trust in the data value chain throughout the entire lifecycle of projects. This paper aims to develop a novel theoretical framework for the adoption of environmentally sustainable blockchain-based digital twins (BCDT) for Construction Industry (CI) 4.0. The paper identifies which key data from construction projects lifecycle should be anchored in BCDTs to benefit CI 4.0 and the environment. The paper also identifies key factors and non-functional requirements necessary for the adoption of BCDTs in a decentralized and sustainable CI 4.0. At first, a content analysis of the literature allowed the identification of which data from projects lifecycle would benefit from blockchain technology (BCT) adoption and what the key factors and non-functional requirements necessary for the adoption of BCDT in the CI4.0 are. Furthermore, the analysis of structured interviews and online survey permitted to firstly validate the hypotheses raised from the literature and to offer a novel framework for BCDT of CI 4.0 in the context of the circular economy (CE). The findings are that (1) the key project lifecycle data relevant for BCDTs relate to the BIM dimensions (3D, 4D, 5D, 6D, 7D, and 8D) and a new dimension called the contractual dimension (cD) is also proposed. (2) Ecosystems of BCDTs should embrace a novel form of collaboration that is decentralized and presented as Level 4 maturity for BCDTs. This new level of maturity leverages distributed blockchain networks to enhance collaboration, processes automation with smart contracts, and data sharing within a decentralized data value chain. Finally (3), the main non-functional requirements for BCDTs are security, privacy, interoperability, data ownership, data integrity, and the decentralization and scalability of data storage. With the proposed framework including the BCDT dimensions, the Maturity Level 4, and the key non-functional requirements, this paper provides the building blocks for industry practitioners to adopt BCDTs. This is promising for CI 4.0 to embrace a paradigm shift towards decentralized ecosystems of united BCDTs where trust, collaboration, data sharing, information security, efficiency, and sustainability are improved throughout the lifecycle of projects and within a decentralized CE (DCE).

Data Visualization to Explore the Countries Dataset for Pattern Creation

Data visualization is graph representation of data. It produces interactive graphs that explain the relationships among the data to viewers of the graph. The aim of data visualization is to communicate data value clearly and effectively through graphs [1]. Here we take the advantage of data visualization to explore the countries dataset to provide a holistic and interpretive view about the world. In addition to examine some hypotheses about gross domestic product (GDP) and Literacy and more of the countries effects on different factors showing on the dataset such as the literacy and the migration.

Research on Service Innovation of Financial Data based on Cloud Computing in Big Data Era

With the increasing penetration of the Internet into people's lives, the financial market in the era of big data directly affects the asymmetric information mode between the supply and demand sides, and on this basis, it plays a positive role in guiding the subsequent optimization of financial market services, ensuring social fairness and reducing the exclusion of financial products. Big data finance under the Internet has narrowed the distance between supply and demand, subverted the problem of information asymmetry between the two sides, and injected new innovative means to alleviate financial exclusion, promote social equity and optimize financial services. In order to support the development of the real economy and enhance the core competitiveness of the industry, the process of data value chain has been continuously extended horizontally and vertically, but the data value chain of financial enterprises has been extended. In this paper, starting from the characteristics of the big data era, taking the carrier of value extension as the research goal, the big data value chain model of financial enterprises is constructed based on cloud computing technology, and the service innovation of China's financial data in the big data era is discussed.

A Literature Review of Statistical Teaching Research in Big Data Environment

The arrival of the big data era brings not only opportunities but also challenges to statistics teaching. It is of great significance to explore the statistics teaching mode under the big data environment. Closely following the frontier of Statistics teaching research in the big data environment, this paper combs the literature on statistics teaching research in the big data environment. Through analysis, it is found that the statistics teaching should give consideration to "systematicness" and "pertinence" closely with the pace of the era, constantly cultivate big data thinking, and make good use of statistics, a "sharp tool" for mining data value, so as to better serve social development in the big data environment.