The market of IOT devices continues to grow at a rapid speed as well as constrained wireless sensor networks. Today, the main network paradigm is host centric where a users have to specify which host they want to receive their data from. Information-centric networking is a new paradigm for the future internet, which is based on named data instead of named hosts. With ICN, a user needs to send a request for a perticular data in order to retrieve it. When sent, any participant in the network, router or server, containing the data will respond to the request. In order to achieve low latency between data creation and its consumption, as well as being able to follow data which is sequentially produced at a fixed rate, an algortihm was developed. This algortihm calculates and determines when to send the next interest message towards the sensor. It uses a ‘one time subscription’ approach to send its interest message in advance of the creation of the data, thereby enabling a low latency from data creation to consumption. The result of this algorithm shows that a consumer can retrieve the data with minimum latency from its creation by the sensor over an extended period of time, without using a publish/subscribe system such as MQTT or similar which pushes their data towards their consumers. The performance evaluation carried out which analysed the Content Centric Network application on the sensor shows that the application has little impact on the overall round trip time in the network. Based on the results, this thesis concluded that the ICN paradigm, together with a ’one-time subscription’ model, can be a suitable option for communication within the IoT domain where consumers ask for sequentially produced data.

This dissertation joins an ongoing discussion in the business management and information technology literature surrounding the measurement of an organization’s business analytic capability, the benefits derived from maturing the capability and the improvements being made toward maturity. The dissertation specifically focuses on the healthcare industry in the United States and more specifically independent physician practices specializing in orthopaedics. After an extensive literature review along with expertise from industry leaders and experienced academic faculty, a survey instrument was developed to measure organizational capabilities, technology capabilities and people capabilities which together measured an organizations overall business analytic capability maturity. The survey instrument was delivered to 89 C-suite executives in the target population. A response rate of 36% was achieved resulting in a total of 32 completed responses. The research study provides evidence that improving an organization’s business analytic capability leads to an improvement in the use of analytics to drive business performance. The research study also explored whether or not the use of analytics would improve business outcomes. The results were inconclusive. This could be due to the lag time between the use of analytics and business performance. In addition, the study did not have access to actual outcome data but rather asked the CEO’s whether or not performance in several areas had improved, remained stable or had declined. This measure may not have been precise enough to provide the predictive value needed. As such, this is an area that should be explored further. Finally, the research shows that over the past two years, physician practices have been focused on and successful in improving their business analytic capabilities. Despite these improvements, opportunities exist for physician practices to further their maturity, particularly in the areas of technology capabilities and people capabilities.

The world’s data sets are growing exponentially every day due to the large number of devices generating data residue across the multitude of global data centers. What to do with the massive data stores, how to manage them and defining who are performing these tasks has not been adequately defined and agreed upon by academics and practitioners. Data science is a cross disciplinary, amalgam of skills, techniques and tools which allow business organizations to identify trends and build assumptions which lead to key decisions. It is in an evolutionary state as new technologies with capabilities are still being developed and deployed. The data science tasks and the data scientist skills needed in order to be successful with the analytics across the data stores are defined in this document. The research conducted across twenty-two academic articles, one book, eleven interviews and seventy-eight surveys are combined to articulate the convergence on the terms data science. In addition, the research identified that there are five key skill categories (themes) which have fifty-five competencies that are used globally by data scientists to successfully perform the art and science activities of data science. Unspecified portions of statistics, technology programming, development of models and calculations are combined to determine outcomes which lead global organizations to make strategic decisions every day. This research is intended to provide a constructive summary about the topics data science and data scientist in order to spark the dialogue for us to formally finalize the definitions and ultimately change the world by establishing set guidelines on how data science is performed and measured.

The transport sector is responsible for a significant and growing proportion of greenhouse gas emissions.  The urgent actions are required to take in the transport sector facing the challenge of growing global change. The major trends, including global urbanization, widespread application of digital technologies, and broad demand for sustainable development, have provided new opportunities for data-driven smart mobility in the future. This research aims to explore potentials of data-driven smart mobility in achieving Sustainable Development Goal 11.2, “provide access to safe, affordable, accessible and sustainable transport systems for all,” and Sustainable Development Goal 13.2, “take urgent action to combat climate change and its impacts” and “integrate climate change measures into national policies, strategies and planning” reducing greenhouse gas emissions every year. In order to meet this aim, this research explores the understandings and innovations of data-driven smart mobility in achieving decarbonization in urban, as well as barriers during the current practices. Hangzhou, as the capital city in Zhejiang Province in China, has been selected for the case study to examine data-driven smart mobility approaches. The research results show that the potentials of the data to tackle climate issues lie in the efficient transport operation and travel behaviors change. Data technologies have been widely applied to improve the integration of travel modes and the efficiency of transport management to reduce greenhouse gas emissions in road traffic. However, there are few drivers to mine data resources for travel behavior change. Moreover, data-driven smart mobility initiatives applied in urban areas involve multiple stakeholders but with limited access to data sharing and opening. Considering disruptive effects and potential promises brought by the big data technologies, the implementation of smart mobility requires for public data strategy with a holistic view of the complex urban challenges and global climate change.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22687号 / 医博第4631号 / 新制||医||1045(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 山下 潤, 教授 木村 剛, 教授 浅野 雅秀 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Profiling users online and directed online advertising has become a major business with companiessuch as Google and Facebook as frontier companies. Through incidents such as the CambridgeAnalytica scandal, the public has started to take notice of both the positive and the negative sides of thebusiness. The data given to companies with a data driven business model can make the user experiencemore personalized and therefore better. On the other hand the data collected could be seen as privacyreducing and exploitation of users. This study aims to foresee opportunities and new ways to develop adata driven business model which has the user's interests in mind and still remains profitable. Withempirical data through interviews and theories the study will show that data driven business modelshave big potential to be profitable and simultaneously make the user more aware or even make datadelivery in the user’s best interest. The main methods to do this is by implementing privacy dashboards,transparency and moving the pieces in the business model to make the user central in the businessmodel.

This masters thesis deals with analysis of tree structure data. The aim of this thesis is to design and implement a tool for automated detection of relation among samples of read data considering their three structure and node values. Output of the tool is a prescription for automated synthesis of data for testing purposes. The tool is a part of Testos platform developed at FIT BUT.

Simultaneously as the societal trends of urbanization, digitalization and electrification of society are moving at a high speed, the Swedish power grid is undergoing a necessary transition to a renewable energy system. Even though there are difficulties on all grid levels, the lack of capacity in some local grids is among the most present problems and originates from the long lead time of grid expansion as well as the challenges within long-term planning of grids. This thesis aims to improve the understanding of future trends’ impact on grid capacity needs. More specifically, a scenario-based and data driven method, with an accompanying model, is derived to target local capacity challenges. The trends identified to pose impact on the future grid capacity were electrification of different sectors, energy efficiency actions, decentralized energy generation, energy storage solutions, flexibility, smart grids, urbanization and climate. The thesis concludes that the impact of a trend on national level is not simply equal to the impact on a local level. Similarly, a long-term increase of the national electricity consumption does not necessarily worsen local capacity challenges. Furthermore, the developed model in this project shows potential to provide more detailed and accurate information about consumption than currently used methods based on standardized power estimations, which could favor more transparent decision making when dimensioning local grids.

The aim of this paper is to present the possibilities of the usage of advanced analytical tools to optimize decision-making in personnel practice. The literature review part of the thesis deals with the so-called HR analytics, its development, possibilities of its usage, and the methodological framework on which it is based. The next part of the paper deals with the specific application of HR analytics in the field of employee retention according to the methodological framework of CRISP-DM. The last chapter describes in detail the phenomenon of employee turnover, its consequences, and possible explanatory variables. The empirical part of the paper is framed as a quantitative, applied research and deals with voluntary turnover of employees in a particular company-a large Czech bank. Firstly, the statistical-inference part of the research identifies several statistically significant predictors of employee turnover through binary logistic regression-unemployment rate, number of changed teams, time spent in the company, salary and total income, salary growth rate, team size, extraordinary bonus, and gender. Secondly, in the data-science part, several prediction models are compiled, one using binary logistic regression as well and another based on several machine learning techniques. The models are...

Background: Digital technologies are increasingly transforming traditional businesses, and their pervasive impact is leading to a radical restructuring of entire industries. While the significance of generating competitive advantages for businesses utilizing big data analytics is recognized, there is still a lack of consensus of big data analytics influencing strategic decision-making in organisations. As big data and big data analytics become increasingly common, understanding the factors influencing decision-making quality becomes of paramount importance for businesses. Purpose: This thesis investigates how big data and big data analytics affect the operational strategic decision-making processes in enterprises through the theoretical lens of the strategy-as-practice framework. Method: The study follows an abductive research approach by testing a theory (i.e., strategy-aspractice) through the use of a qualitative research design. A single case study of IKEA was conducted to generate the primary data for this thesis. Sampling is carried out internally at IKEA by first identifying the heads of the different departments within the data analysis and from there applying the snowball sampling technique, to increase the number of interviewees and to ensure the collection of enough data for coding. Findings: The findings show that big data analytics has a decisive influence on practitioners. At IKEA, data analysts have become an integral part of the operational strategic decision-making processes and discussions are driven by data and rigor rather than by gut and intuition. In terms of practices, it became apparent that big data analytics has led to a more performance-oriented use of strategic tools and enabling IKEA to make strategic decisions in real-time, which not only increases agility but also mitigates the risk of wrong decisions.