Big data analytics — A review of data-mining models for small and medium enterprises in the transportation sector.

The need for small and medium enterprises (SMEs) to adopt data analytics has reached a critical point, given the surge of data implied by the advancement of technology. Despite data mining (DM) being widely used in the transportation sector, it is staggering to note that there are minimal research case studies being done on the application of DM by SMEs, specifically in the transportation sector. From the extensive review conducted, the three most common DM models used by large enterprises in the transportation sector are identified, namely “Knowledge Discovery in Database,” “Sample, Explore, Modify, Model and Assess” (SEMMA), and “CRoss Industry Standard Process for Data Mining” (CRISP-DM). The same finding was revealed in the SMEs’ context across the various industries. It was also uncovered that among the three models, CRISP-DM had been widely applied commercially. However, despite CRISP-DM being the de facto DM model in practice, a study carried out to assess the strengths and weakness of the models reveals that they have several limitations with respect to SMEs. This paper concludes that there is a critical need for a novel model to be developed in order to cater to the SMEs’ prerequisite, especially so in the transportation sector context

The Conceptual of Pavement Management System Based on IoT, Big Data, and Data Mining in Indonesia

The country’s economic development and people’s lives require reliable transportation and logistics services. Therefore, road maintenance policies and techniques must be more comprehensive to ensure transportation infrastructure operations’ efficiency. The inefficiency of road maintenance, among others, is due to the failure to identify and predict the level of damage that can cause fundamental data anomalies. Therefore, the current scientific development of road maintenance must be able to take advantage of the convenience of technology. Widespread technology implementation on the Internet of Think (IoT), big data, and data mining (DM) can be a solution to developing a better pavement management system. However, implementing these technologies is challenging due to the weak conception and availability of currently available resources. This paper offers a pavement system management implementation concept that can be developed in Indonesia to improve the existing system. The study results show that IoT, big data, and DM are believed to support a more intelligent and comprehensive pavement management system. The concept offered consists of three main things: identification of damage and 3D pavement modeling, data analysis, decision support systems, and collaboration of intelligent solutions in implementing maintenance in the field. This paper is equipped with an illustration of the concept of road maintenance based on IoT, big data, and DM, which can be implemented intelligently and simply in Indonesia

Big Data and IoT Opportunities for Small and Medium-Sized Enterprises (SMEs)

The advancement of technology and emergence of internet of things (IoT) has exponentially caused a data explosion in the 21st century era. As such, the arrival of IoT is set to revolutionize the development of the small and medium-sized enterprise (SME) organizations by shaping it into a more universal and integrated ecosystem. Despite evidential studies of the potential of advanced technologies for businesses, the SMEs are apprehensive towards new technologies adoption such as big data analytics and IoT. Therefore, the aim of this chapter is to provide a holistic study of big data and IoT opportunities, challenges, and applications within the SMEs context. The authors hope that the outcome of this study would provide foundational information on how the SMEs can partake with the new wave technological advancement and in turn, spurring more SMEs for adoption

The Implementation of Autocad® Civil 3D for Road Geometric Redesign on Educational Areas: A Case Leumah Neundet Bandung

Infrastructures, especially those directly related to the transportation system and has a crucial role in society is the road. “Jalan Leumah Neundeut” is a road transportation infrastructure in Sukawarna Village, Sukajadi District, Bandung City, West Java, Indonesia. It has the potential for trip attraction because it very close to the education area and passed by public transportation. This paper’s problem formulation arises based on the need for a well-road transportation infrastructure on “Jalan Leumah Neundeut”. It will be geometrically redesigned in its horizontal alignment to be as ideal as possible to fit the design criteria using AutoCAD® Civil 3D because it does not meet the design criteria, especially regarding the road spaces and lane configuration. The geometric redesign of the “Jalan Leumah Neundeut” horizontal alignment resulted in 450 meters of the total road length, 7 meters of carriageway width with 2/2 UD (undivided with two lanes and two ways) type, 60 km/hour of velocity design, S-C-S type of road curve, 47 meters of LS value, 130 meters of curve design radius, 2% of normal super-elevation, and 8% of maximum super-elevation. With the results obtained, Jalan Leumah Neundeut can maximize its potential

The Case of Mongolia

학위논문(석사) -- 서울대학교대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2021.8. Jorn Altmann.Small and medium enterprises (SMEs) are considered key players in any country's social and economic development. Adopting innovative technologies such as Big Data Analytics (BDA) can bring better performance and competitive advantage for SMEs, which is important for a country's economic growth. This study aims to assess the main challenges and potentials of BDA adoptions in SMEs and examine the impacts of its adoption into business performance for SMEs in developing countries aspect. To achieve the study's goal, a systematic literature review (SLR) is conducted regarding the adoption of BDA in SMEs. The most common SLR method among the researchers in information system research, which was initiated by Kitchencham et al. (Kitchenham, Budgen, & Brereton, 2015) and Okoli et al.(Okoli & Schabram, 2010), is adapted in the study. In doing so, the SLR is focused on defining SMEs within various aspects and is directed to determine the most common influencing factors in BDA adoption in SMEs. In the result of the SLR, widely discussed 34 distinct influencing factors are identified in the adoption of BDA in SMEs from the previous literature. In addition, the hypotheses are developed based on the influencing factors, which show consensus among the researchers. After that, a conceptual framework is developed for developing the country aspect and control variables, and the moderating variables’ effect is also estimated. To evaluate hypotheses and the conceptual framework, an online questionnaire is conducted among Mongolia SMEs which run businesses in various industries. The online questionnaire is distributed to decision-makers and information technology specialists in the firm. In total, 170 respondents participated in the online survey. Based on the survey result, hypotheses are tested. As a consequence, the collected data and proposed framework are analyzed by using Partial Least Squares (PLS). This is a method of Structure Equation Modeling (SEM) that allows investigating the inter-relationship between the latent and observed variables. In terms of statistical software tools, Smart PLS v3.3.3 was employed, which is one of the useriv friendly tools for data analysis. Finally, policies and recommendations are deployed based on the findings.중소기업 (SME)은 모든 국가의 사회 및 경제 개발에서 핵심적인 역할을 하고 있는 것으로 간주된다. 빅 데이터 분석 (BDA)과 같은 혁신적인 기술의 채택은 국가 경제 성장에 중요한 역할을 하는 있는 중소기업에 더 나은 경영 성과와 경쟁력을 가져올 수 있다. 본 연구는 중소기업에서 BDA 채택하는 데에 있는 주요 과제와 잠재력을 평가하고 개발 도상국 측면에서 BDA 채택은 중소기업의 경영 성과에 대한 영향을 조사하는 것을 목표로 한다. 본 연구의 목표를 이루기 위해 우선 SME에서 BDA 채택과 관련한 문헌검토(systematic literature review (SLR))를 하였다. 정보 시스템 연구자들 중에 Kitchencham et al [1]과 Okoli et al. [2]에 의해 시작된 정보 시스템 연구는 가장 일반적인 SLR 방법이라고 할 수 있다. 이 방법은 본 연구에 적용됩니다. 본 연구는 문헌 검토를 통해서 다양한 측면에서 SME를 정의하는 데 초점을 맞추고 있으며 SME에서 BDA 채택의 가장 일반적인 영향 요인을 밝혔다 . 문헌 검토한 결과를 보면, 선행 연구에서 SME의 BDA 채택에 있어서 34 개의 뚜렷한 영향 요인을 논의했다는 것을 확인되었다. 본 연구의 가설은 연구자들의 일치한 관점을 보여주는 영향 요인을 기반으로 설정하었다. 그 다음에 개발 도상국을 위한 개념의 체계를 세우고 통제 변인과 조절 변인의 영향도 추정하였다. 가설과 개념 체계를 평가하기 위해 본 연구는 몽골의 다양한 사업을 운영하고 있는 중소기업을 대상으로 온라인 설문조사를 실시하였다. 온라인 141 설문조사의 참여자는 회사의 주요 의사 결정자 및 정보 기술 전문가였다. 이를 통해 수집 된 데이터와 제안 된 체계를 PLS (Partial Least Squire)를 사용하여 분석하였다. 이 방법은 잠재 변수와 관찰 변수 간의 상호 관계를 조사 할 수있는 구조 방정식 모형 (SEM) 방법이다. 통계 소프트웨어 도구 측면에서는 접하기가 쉬운 데이터 분석 도구 중 하나인 SmartPLS v3.3.3 을 이용하였다. 마지막으로, 본 연구는 분석한 결과를 기반하여 정책 및 제안을 제시하였다.Chapter 1. Introduction 1 Chapter 2. Background on Big Data Analytics Adoption 6 2.1 Defination of Big Data 6 2.2 Defination of Small and Medium enterprises 9 2.3 Role of Big Data 10 2.4 Charateristics of developing countries 11 Chapter 3. Methodology and Model Design 13 3.1 Methdogology fused for analyzing Big Data Analytics in Small and Medium Enterprises in Developing countries 13 3.2. Model design 26 3.2.1 Factors 26 3.2.2. Theories 28 3.2.3. Classification of factors into categories 36 3.2.4. Impact on developing country 46 3.2.5. Impact on different industries 50 3.2.6. Theoritical background and hypothesis development 51 3.2.7. Technological context 54 3.2.8. Organizational context 58 3.2.9. Environmental context 61 3.2.10. Moderating variables 63 3.2.11. Control variables 65 Chapter 4. Framework for Mongolian case 67 4.1. Mongolia 67 4.2. Data collection 68 4.3. Basic understanding on moderating effect 70 4.4. Data analysis 71 4.5. Results 74 4.5.1. Reliability and validity 74 4.5.2. Structual model analysis 78 4.5.3. Moderating variables 82 Chapter 5. Conclusion 85 5.1. Discussion 85 5.1.1. Technological context 85 5.1.2. Organizational context 88 5.1.3. Environmental context 88 5.2. Contrubitions 89 5.3. Policy implication 90 5.4. Limitation and outlok 91 Appendix.1 93 Appendix.2 110 Bibliography 115 Abstract in Korean 140석

Gestão de riscos no direito fundamental à privacidade de dados pessoais no Processo Judicial Eletrônico / Diário de Justiça Eletrônico

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2020.O sistema judiciário do Brasil instituiu o Processo Judicial Eletrônico (PJe) e o Diário de justiça Eletrônico (DJe) que vêm promovendo mudanças profundas em todo o ecossistema do judiciário. Por um lado, busca-se aumentar a celeridade processual, reduzir custos e facilitar o acesso a justiça. Por outro lado, o sistema tem o potencial de, ao alcance de um clique, expor a privacidade das partes e, ao mesmo tempo, os advogados e escritórios de advocacia, por falta de treinamento e adoção de boas práticas têm, eventualmente, colocado em risco os dados pessoais dos assistidos. Estes dois fatos contribuem para a violação latente e efetiva da privacidade. O presente estudo investiga a exposição de dados pessoais tanto no Processo Judicial Eletrônico como no Diário de Justiça Eletrônico, principalmente em processos que tramitam sob sigilo de justiça. Também investiga as práticas adotadas pela advocacia na proteção da privacidade dos assistidos em meio digital. Assim a construção de big data analytics, e explorados por meio de jurimetria apoiada em algoritmos de inteligência artificial potencializam a exposição da privacidade em uma economia movida a dados que alimenta o “capitalismos da vigilância1 ”. Neste sentido, não só em virtude da Lei Geral de Proteção de Dados Pessoais – Lei 13.709/2018, mas também pautado em outros princípios constitucionais, é proposto um modelo de de-identificação por pseudonimização para resguardar a privacidade das partes no processo eletrônico, tendo por base o Health Insurance Portability and Accountability Act (HIPAA), também é proposto um ajuste no formato do Dje, para a efitivação do Direito ao Esquecimento, tendo em vista, principalmente, o grande potencial da WEB 3.0 e até mesmo a WEB 4.0, ao mesmo tempo em que organiza um conjunto de recomendações de boas práticas focado em pessoas, “processo” e tecnologia, para advogados e escritórios de advocacia, à luz da ABNT NBR ISO 31000:2018, com o objetivo minimizar riscos de exposição de dados pessoais dos assistidos.The judicial system in Brazil instituted the Electronic Judicial Process (PJe) and the Electronic Justice Journal (DJe), which have been promoting profound changes in the entire judicial ecosystem. On the one hand, the aim is to increase procedural speed, reduce costs and facilitate access to justice. On the other hand, the system has the potential, within the reach of a click, to expose the privacy of the parties and, at the same time, lawyers and law firms, due to lack of training and adoption of good practices, have eventually put into risk the personal data of the beneficiaries. These two facts contribute to the latent and effective breach of privacy. The present study investigates the exposure of personal data both in the Electronic Judicial Process and in the Electronic Justice Journal, mainly in processes that are under confidentiality. It also investigates the practices adopted by the legal profession in protecting the privacy of those assisted in digital media. Thus, the construction of big data analytics, through jurimetry supported by artificial intelligence algorithms, enhance the exposure of privacy in a data-driven economy that feeds the “surveillance capitalisms2 ”. In this sense, not only in virtue of the General Law on Protection of Personal Data - Law 13.709 / 2018, but also based on other constitutional principles, it is proposed a model of de-identification by pseudonymization to safeguard the privacy of the parties in the electronic process, based on the Health Insurance Portability and Accountability Act (HIPAA), while organizing a set of good practice recommendations focused on people and processes, for lawyers and law firms, in the light of ABNT NBR ISO 31000: 2018, with the objective minimize risks of exposure of the personal data of the beneficiaries