Protecting One\u27s Own Privacy in a Big Data Economy

Big Data is the vast quantities of information amenable to large-scale collection, storage, and analysis. Using such data, companies and researchers can deploy complex algorithms and artificial intelligence technologies to reveal otherwise unascertained patterns, links, behaviors, trends, identities, and practical knowledge. The information that comprises Big Data arises from government and business practices, consumer transactions, and the digital applications sometimes referred to as the “Internet of Things.” Individuals invisibly contribute to Big Data whenever they live digital lifestyles or otherwise participate in the digital economy, such as when they shop with a credit card, get treated at a hospital, apply for a job online, research a topic on Google, or post on Facebook.Privacy advocates and civil libertarians say Big Data amounts to digital surveillance that potentially results in unwanted personal disclosures, identity theft, and discrimination in contexts such as employment, housing, and financial services. These advocates and activists say typical consumers and internet users do not understand the extent to which their activities generate data that is being collected, analyzed, and put to use for varied governmental and business purposes.I have argued elsewhere that individuals have a moral obligation to respect not only other people’s privacy but also their own. Here, I wish to comment first on whether the notion that individuals have a moral obligation to protect their own information privacy is rendered utterly implausible by current and likely future Big Data practices; and on whether a conception of an ethical duty to self-help in the Big Data context may be more pragmatically framed as a duty to be part of collective actions encouraging business and government to adopt more robust privacy protections and data security measures

Big Data Dreams and Reality in Shenzhen: An Investigation of Smart City Implementation in China

Chinese cities are increasingly using digital technologies to address urban problems and govern society. However, little is known about how this digital transition has been implemented. This study explores the introduction of digital governance in Shenzhen, one of China's most advanced smart cities. We show that, at the local level, the successful implementation of digital systems faces numerous hurdles in long-standing data management and bureaucratic practices that are at least as challenging as the technical problems. Furthermore, the study finds that the digital systems in Shenzhen entail a creeping centralisation of data that potentially turns lower administrative government units into mere users of the city-level smart platforms rather than being in control of their own data resources. Smart city development and big data ambitions thereby imply shifting stakeholder relations at the local level and also pull non-governmental stakeholders, such as information technology companies and research institutions, closer to new data flows and smart governance systems. The findings add to the discussion of big data-driven smart systems and their implications for governance processes in an authoritarian context

Big Data Dreams and Reality in Shenzhen: An Investigation of Smart City Implementation in China

Chinese cities are increasingly using digital technologies to address urban problems and govern society. However, little is known about how this digital transition has been implemented. This study explores the introduction of digital governance in Shenzhen, one of China's most advanced smart cities. We show that, at the local level, the successful implementation of digital systems faces numerous hurdles in long-standing data management and bureaucratic practices that are at least as challenging as the technical problems. Furthermore, the study finds that the digital systems in Shenzhen entail a creeping centralisation of data that potentially turns lower administrative government units into mere users of the city-level smart platforms rather than being in control of their own data resources. Smart city development and big data ambitions thereby imply shifting stakeholder relations at the local level and also pull non-governmental stakeholders, such as information technology companies and research institutions, closer to new data flows and smart governance systems. The findings add to the discussion of big data-driven smart systems and their implications for governance processes in an authoritarian context

Digital Transformation Planning Based on Big Data Technology (Case Study: XYZ Bank)

Bank XYZ is one of the regional government-owned banks which as of August 16, 2021 has a total percentage of customers who have dormant accounts reached 50.43%. This happens because currently the development of the marketing strategy used by Bank XYZ is still based on the products and services owned by Bank XYZ, not referring to customer needs. For this reason, the purpose of this research is to make a digital transformation plan that can be used by Bank XYZ in order to become a customer-centric bank using Big Data technology. The method used to carry out this research is to use the Big Data Framework for Agile Business (BDFAB). The results of this study includes mapping data requirements and Big Data technology for Bank XYZ, the impact to business processes, proposals for changes in information system architecture, planning for quality control mechanisms, to changes in organizational structure so that the benefits obtained by Bank XYZ are a more personalized marketing approach to customers, increased accuracy in offering bank products and services based on customer profiles, needs, behavior, and interests, as well as increased customer engagement and customer satisfaction

An Approach to Publish Scientific Data of Open-Access Journals Using Linked Data Technologies

Semantic Web encourages digital libraries, including open access journals, to collect, link and share their data across the Web in order to ease its processing by machines and humans to get better queries and results. Linked Data technologies enable connecting related data across the Web using the principles and recommendations set out by Tim Berners-Lee in 2006. Several universities develop knowledge through scholarship and research with open access policies for the generated knowledge, using several ways to disseminate information. Open access journals collect, preserve and publish scientific information in digital form related to a particular academic discipline in a peer review process having a big potential for exchanging and spreading their data linked to external resources using Linked Data technologies. Linked Data can increase those benefits with better queries about the resources and their relationships. This paper reports a process for publishing scientific data on the Web using Linked Data technologies. Furthermore, methodological guidelines are presented with related activities. The proposed process was applied extracting data from a university Open Journal System and publishing in a SPARQL endpoint using the open source edition of OpenLink Virtuoso. In this process, the use of open standards facilitates the creation, development and exploitation of knowledge.This research has been partially supported by the Prometeo project by SENESCYT, Ecuadorian Government and by CEDIA (Consorcio Ecuatoriano para el Desarrollo de Internet Avanzado) supporting the project: “Platform for publishing library bibliographic resources using Linked Data technologies”

Development of a supervisory internet of things (IoT) system for factories of the future

Big data is of great importance to stakeholders, including manufacturers, business partners, consumers, government. It leads to many benefits, including improving productivity and reducing the cost of products by using digitalised automation equipment and manufacturing information systems. Some other benefits include using social media to build the agile cooperation between suppliers and retailers, product designers and production engineers, timely tracking customers’ feedbacks, reducing environmental impacts by using Internet of Things (IoT) sensors to monitor energy consumption and noise level. However, manufacturing big data integration has been neglected. Many open-source big data software provides complicated capabilities to manage big data software for various data-driven applications for manufacturing. In this research, a manufacturing big data integration system, named as Data Control Module (DCM) has been designed and developed. The system can securely integrate data silos from various manufacturing systems and control the data for different manufacturing applications. Firstly, the architecture of manufacturing big data system has been proposed, including three parts: manufacturing data source, manufacturing big data ecosystem and manufacturing applications. Secondly, nine essential components have been identified in the big data ecosystem to build various manufacturing big data solutions. Thirdly, a conceptual framework is proposed based on the big data ecosystem for the aim of DCM. Moreover, the DCM has been designed and developed with the selected big data software to integrate all the three varieties of manufacturing data, including non-structured, semi-structured and structured. The DCM has been validated on three general manufacturing domains, including product design and development, production and business. The DCM cannot only be used for the legacy manufacturing software but may also be used in emerging areas such as digital twin and digital thread. The limitations of DCM have been analysed, and further research directions have also been discussed

Big data, big challenges: risk management of financial market in the digital economy

Purpose– The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It’s a big challenge for the government to carry out financial market risk management in the big data era. Design/methodology/approach– In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value. Findings– Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike Information Criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored. Originality/value– Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy

PELUANG DAN TANTANGAN “BIG DATA” DALAM MEMBANGUN “SMART CITY” UNTUK SISTEM TRANSPORTASI

This article aims to provide an overview of the opportunities and challenges of "Big Data" in building "smart city" for transportation systems. The method of research in this paper is descriptive qualitative by using literature review as primary data and synthesis from several journals both international and national. In this digital era, big data can be used by business people in determining business strategy. As for academics can be used as innovation in doing research, although there are still obstacles. For the government can be used as the basis for references to issue policies in building smart city especially transportation system that pay more attention to environmental factors and quality of life of the community so as to support sustainable development progra

Data as a design material: An analysis on the challenges of working with “big data” related technologies in an industrial context

In recent years, the ability to collect, store and analyse large datasets by private companies and government agencies has increased to the point where the term “big data” has been coined to describe the phenomena. Alongside “big data”, several data processing technologies are becoming more widespread due to their effectiveness and success in everyday products and services; these are artificial intelligence, with its subsets machine learning and deep learning, and data analytics amongst others. This study investigated the challenges designers face when working with new information and communication technologies in an industrial context. More specifically, it deals with “big data” and new data processing technologies and how designers engage with them as a design material when envisioning new products and services. The research questions were (1) what challenges are designers facing when working with “big data” in a data-rich industrial context? (2) how is working with “big data” and new data collecting and processing technologies different from other design materials? (3) how can designers overcome some of the challenges of working with data? This thesis adopted a research through design approach and data was collected between June 2015 and January 2016. Furthermore, a review of the material-centred design literature was used as a theoretical framework. To answer the research questions, this thesis investigated a six-month design project done for the energy company Vattenfall. Vattenfall was at the time going through a digitalisation phase and was interested in evaluating the possibility of combining their internal data with other data sources to explore new products and services. During the six-month period, I worked in Vattenfall’s Helsinki offices, designing different concepts under the supervision of the product development team and their programme manager as my direct supervisor. Data was gathered using different qualitative methods and focusing on three areas: the design practice, the design outcomes, and the interactions with the team and stakeholders. The key findings demonstrate how the practice of design in this new technological landscape faces multiple challenges. The main challenges being (a) the high level of complexity of these technologies, (b) the lack of education/experience of the designer to work in this context, (c) the lack of competence in the organization and (d) the missing frameworks and tools for collaboration between data experts and designers. Furthermore, it was also found and validated against the literature that these new technologies present different properties not comparable with previously well-studied ones like haptics, Bluetooth and RFID. Making existing frameworks and traditional approaches to exploring new digital materials hard to replicate. The results further suggest the need for developing novel concepts and frameworks to support new ways of understanding, describing and working with “big data” and its related technologies