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The dissertation consists of a critical preface and excerpts from the novel Patrol. The preface explores how the novel Patrol utilizes characters that engage with tropes of the Romantic Genius in order to establish their subjectivity while navigating the standardizing mechanisms of twenty-first century information technologies. The preface analyzes how the rise of the organic food movement, the usage of biotech genetic engineering, and the tactics of Big Data-era marketing all inform the critical underpinnings of Patrol, situating the novel in conversation with works of fiction and nonfiction that also explore the interplay of these topics with contemporary American culture. Set primarily in Cincinnati, Ohio, the bifurcated narrative of the novel Patrol enlists the perspectives of both a science-tech father from the Boomer generation, Tim Smith, and his millennial public relations-major daughter, Sarah Smith. Both work in industries that seek to utilize the concept of the individual genius in service of quantification. Tim and Sarah’s interactions with Alexandra Smith, a family member who transitions from female to male over the course of the novel, cause both protagonists to recognize that their own identities are malleable, and this discovery goads each into reexamining their career choices and personal relationships. The plot depicts the outcome of these explorations, culminating in a series of choices for Tim and Sarah that showcase the fundamental change in each character. Unable to simply quantify themselves and those around them, Tim and Sarah instead adopt a more nuanced view of the world that seeks to find a balance between the individualistic conceit of the Romantic genius and the quantifying mandates of technology.

The aim of this dissertation is to study two problems of distributed nearest neighbor classification (DiNN) systematically. The first one compares two DiNN classifiers based on different schemes: majority voting and weighted voting. The second one is an extension of the DiNN method to the crowdsourcing application, which allows each worker data has a different size and noisy labels due to low worker quality. Both statistical guarantees and numerical comparisons are studied in depth.<br><div><br></div><div><div>The first part of the dissertation focuses on the distributed nearest neighbor classification in big data. The sheer volume and spatial/temporal disparity of big data may prohibit centrally processing and storing the data. This has imposed a considerable hurdle for nearest neighbor predictions since the entire training data must be memorized. One effective way to overcome this issue is the distributed learning framework. Through majority voting, the distributed nearest neighbor classifier achieves the same rate of convergence as its oracle version in terms of the regret, up to a multiplicative constant that depends solely on the data dimension. The multiplicative difference can be eliminated by replacing majority voting with the weighted voting scheme. In addition, we provide sharp theoretical upper bounds of the number of subsamples in order for the distributed nearest neighbor classifier to reach the optimal convergence rate. It is interesting to note that the weighted voting scheme allows a larger number of subsamples than the majority voting one.</div></div><div><br></div><div>The second part of the dissertation extends the DiNN methods to the application in crowdsourcing. The noisy labels in crowdsourcing data and different sizes of worker data will deteriorate the performance of DiNN methods. We propose an enhanced nearest neighbor classifier (ENN) to overcome this issue. Our proposed method achieves the same regret as its oracle version on the expert data with the same size. We also propose two algorithms to estimate the worker quality if it is unknown in practice. One method constructs the estimators for worker quality based on the denoised worker labels through applying kNN classifier on expert data. Unlike previous worker quality estimation methods, which have no statistical guarantee, it achieves the same regret as the ENN with observed worker quality. The other method estimates the worker quality iteratively based on ENN, and it works well without expert data required by most previous methods.<br></div>

In the sustainable tourist sector today, there is a wide margin of loss in small and medium-sized enterprise (SMEs) because of a poor control in logistical expenses. In other words, acquired goods are note being sold, a scenario which is very common in tourism SMEs. These SMEs buy a number of travel packages to big companies and because of the lack of demand of said packages, they expire and they become an expense, not the investment it was meant to be. To solve this problem, we propose a Predictive model based on sentiment analysis of a social networks that will help the sales decision making. Once the data of the social network is analyzed, we also propose a prediction model of tourist destinations, using this information as data source it will be able to predict the tourist interest. In addition, a case study was applied to a real Peruvian tourist enterprise showing their data before and after using the proposed model in order to validate the feasibility of proposed model.

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Digitalized companies are adding complexity to the theory of internationalization. In order to gain momentum in a foreign market, knowledge about specific regional aspects and customers’ behavior is crucial. In a modern business environment, data supports decisions, enhances performance, and contributes to innovative business models. Due to its unique characteristics, data is perceived as a hidden, yet valuable asset. In this thesis, I am comparing the role of data in two types of companies in a qualitative empirical study of German ventures. As a company intern data gathering practice, truly digital firms are expected to take advantage of digital platforms in the context of internationalization. Information and Communication Technology companies are supposed to collect data by enhancing their physical products with Internet of Things applications or -interfaces (Lee and Lee, 2015; Monaghan et al., 2020). I am arguing that the process of internationalization is driven by data, in both types of companies. My results are indicating that digital platforms are the primary method of gathering information about foreign markets. The importance of Internet of Things increases on a subsequent stage, during the process of internationalization. An integral perception of data and its versatile areas of application can create a nourishing ground for business opportunities.

The main aim of this diploma thesis, that names "New challenges of the Surveillance theory", is to describe the surveillance theory in today's social network society by using information theory. Accordingly, I will verify the theory of surveillance in two case studies. First case study verifies an impact of Facebook's profiles content on the hiring decisions. The second case sudy analyzes regular day of concrete person in context of surveillance. Both case studies demonstrate surveillance in different surveillance sites.

The role of marketing is evolving rapidly, and design and analysis methods used by marketing researchers are also changing. These changes are emerging from transformations in management skills, technological innovations, and continuously evolving customer behavior. But perhaps the most substantial driver of these changes is the emergence of big data and the analytical methods used to examine and understand the data. To continue being relevant, marketing research must remain as dynamic as the markets themselves and adapt accordingly to the following: Data will continue increasing exponentially; data quality will improve; analytics will be more powerful, easier to use, and more widely used; management and customer decisions will increasingly be knowledge-based; privacy issues and challenges will be both a problem and an opportunity as organizations develop their analytics skills; data analytics will become firmly established as a competitive advantage, both in the marketing research industry and in academics; and for the foreseeable future, the demand for highly trained data scientists will exceed the supply.

Since the emergence of big data, traditional business intelligence systems have been unable to meet most of the information demands in many data-driven organisations. Nowadays, big data analytics is perceived to be the solution to the challenges related to information processing of big data and decision-making of most data-driven organisations. Irrespective of the promised benefits of big data, organisations find it difficult to prove and realise the value of the investment required to develop and maintain big data analytics. The reality of big data is more complex than many organisations’ perceptions of big data. Most organisations have failed to implement big data analytics successfully, and some organisations that have implemented these systems are struggling to attain the average promised value of big data. Organisations have realised that it is impractical to migrate the entire traditional business intelligence (BI) system into big data analytics and there is a need to integrate these two types of systems. Therefore, the purpose of this study was to investigate a framework for creating a hybrid data-driven decision support system that combines components from traditional business intelligence and big data analytics systems. The study employed an interpretive qualitative research methodology to investigate research participants' understanding of the concepts related to big data, a data-driven organisation, business intelligence, and other data analytics perceptions. Semi-structured interviews were held to collect research data and thematic data analysis was used to understand the research participants’ feedback information based on their background knowledge and experiences. The application of the organisational information processing theory (OIPT) and the fit viability model (FVM) guided the interpretation of the study outcomes and the development of the proposed framework. The findings of the study suggested that data-driven organisations collect data from different data sources and process these data to transform them into information with the goal of using the information as a base of all their business decisions. Executive and senior management roles in the adoption of a data-driven decision-making culture are key to the success of the organisation. BI and big data analytics are tools and software systems that are used to assist a data-driven organisation in transforming data into information and knowledge. The suggested challenges that organisations experience when they are trying to integrate BI and big data analytics were used to guide the development of the framework that can be used to create a hybrid data-driven decision support system. The framework is divided into these elements: business motivation, information requirements, supporting mechanisms, data attributes, supporting processes and hybrid data-driven decision support system architecture. The proposed framework is created to assist data-driven organisations in assessing the components of both business intelligence and big data analytics systems and make a case-by-case decision on which components can be used to satisfy the specific data requirements of an organisation. Therefore, the study contributes to enhancing the existing literature position of the attempt to integrate business intelligence and big data analytics systems. / Dissertation (MIT (Information Systems))--University of Pretoria, 2021. / Informatics / MIT (Information Systems) / Unrestricted

This bachelor thesis aims at investigating how librarians at university libraries experience privacy and user data when using the digital library, and to relate their views to an era of Big Data. Protecting the library user’s privacy is part of the librarian profession and established in the ethical codes published by the International Federation of Library Associations and Institutions (IFLA). Privacy issues have also been of interest for the library- and information science over the years, some studies which have investigated the expectations of privacy by library users. The results show that library users expect the library to protect their privacy and feel safe about the library as an institution for doing so. Earlier studies, as well as my result, shows that this is a major challenge in an era of Big Data, when the digital library depends on third party suppliers and the exchange of data between libraries, suppliers, and library users.  The thesis takes on a qualitative approach. Interviews were held with five librarians using the semi-structured interview as a method. To analyse the result, the thoughts presented by Mai (2019) about personal information in an era of Big Data, and the models of privacy and information presented by Mai (2016, 2019) and Agre (1994), have been used. The result shows that the librarians view on privacy and user data correspond with the perspectives reflected in the Panopticon Model and the Capture Model. That is a traditional view on privacy and information, seeing personal information as a certain type of information that can be controlled. According to this view, violation of a user’s privacy is about not being able to fully protect that personal information. According to Mai (2016, 2019) this is not a satisfactory view on privacy in an era of Big Data. He therefore suggests the Datafication Model. Privacy should, according to Mai (ibid.), be less about the information and more about the situations where the information is being used. This view on privacy and information couldn’t be seen in the result of the study.