The thesis deals with the topic of social networks, particularly with the opportunities the utilisation of social network data can provide to an enterprise. The thesis is divided into two parts: The theoretical part contains definitions of the terms of data, information and knowledge, followed by descriptions of Business Intelligence and Big Data -- the two means of data analysis in an enterprise, and later by describing social networks themselves. The practical part contains an analysis of the data provided by social networks Facebook and Twitter, and at the same time defines the process of data extraction. The outcome of the analysis is a set of data that may possibly be obtained by the enterprise. This data is then used to determine the possible ways in which enterprises can leverage the data for their business. Finally data provided by Czech e shop is used to provide an example of how an entity can utilise social network data.

This diploma thesis deals with various types of relations observable in social networks. First part contains survey of existing papers focused on social media data analysis and visualisations. This survey is followed by research of existing visualisation applications. The contribution of this thesis is a new look at relations on social networks, from the side of relationship between users who write posts on any given topic. To visualize these relations was chosen network graph. On the basis of established criteria existing instruments were evaluated and since any of them did not meet all requirements, I created new application for visualising relations. Thesis also includes a description of selection the appropriate libraries for its implementation, explanation of user interface and a summary of configuration options and customization. The end of the work contains analysis and visualisations made with newly-created tool.

Increasing collaborative working between GPs and community pharmacists has recently become a high priority for the NHS. Previous research suggests that interaction is limited and problematic between the two professions, forming a barrier to service provision. This PhD aimed to explore the level, nature and process of interaction between GPs and community pharmacists, using a social network analysis approach.The study focused on four geographically different case study areas and 90 GPs and community pharmacists participated in total. A two-stage design was adopted. Firstly data were collected through a network questionnaire and analysed using social network analysis. Secondly, qualitative interviews were conducted to provide narrative to the network findings and analysed using the framework approach.The nature of contact was characterised as mostly indirect through brokers, de-personalised and non-reciprocal and seemingly at odds with collaborative behaviour. A misalignment in responses pointed to asymmetry in the relationship, representing little commonality, knowing and understanding of each other. Through social network analysis, individuals and dyads in possession of strong ties were identified. Strong ties were not the norm and were characterised by more personalised forms of reciprocal contact. Qualitative interviews provided insight into the processes of interaction between the two professional groups. An approach to the interaction, which involved pharmacists tactically managing the potential conflict in the interaction through use of deferential and sometimes subservient behaviour, was conceptualised as the ‘pharmacist-GP game’. Those pharmacists with strong ties to GPs also, at times, adopted aspects of this approach but also attempted to set themselves apart from other pharmacists in order to develop and maintain their strong ties with GPs. However, possession of strong ties did not always lead to capitalisation, and the benefits of possessing these were often viewed as efficiency and convenience gains rather than anything more wide-reaching. Often, more isolated GPs and pharmacists did not view strong ties as a necessity, with the benefits of these not considered rewarding enough for the time and effort required to achieve them. This effort-reward conflict was identified as an important constraint faced by GPs and pharmacists in relation to transforming these loose connections into more integrated networks. Other micro and macro level constraints were also identified and a series of accompanying recommendations made for future practice and research.

<p>Care of patients with chronic conditions is complicated and usually includes large number of healthcare providers. Understanding the team structure and networks of healthcare providers help to make informed decisions for health policy makers and design of wellness programs by identifying the influencers in the network. This work presents a novel approach to assess the collaboration of healthcare providers involved in the care of patients with chronic conditions and the impact on patient outcomes. </p> <p>In the first study, we assessed a patient population needs, preventive service utilization, and impact of an onsite clinic as an intervention on preventive service utilization patterns over a three-year period. Classification models were developed to identify groups of patients with similar characteristics and healthcare utilization. Logistic regression models identified patient factors that impacted their utilization of preventive health services in the onsite clinic vs. other providers. Females had higher utilizations compared to males. Type of insurance coverages, and presence of diabetes/hypertension were significant factors that impacted utilization. The first study framework helps to understand the patient population characteristics and role of specific providers (onsite clinic), however, it does not provide information about the teams of healthcare providers involved in the care process. </p> <p>Considering the high prevalence of diabetes in the patient cohort of study 1, in the second study, we followed the patient cohort with diabetes from study 1 and extracted their healthcare providers over a two-year period. A framework based on the social network analysis was presented to assess the healthcare providers’ networks and teams involved in the care of diabetes. The relations between healthcare providers were generated based on the patient sharing relations identified from the claims data. A multi-scale community detection algorithm was used to identify groups of healthcare providers more closely working together. Centrality measures of the social network identified the influencers in the overall network and each community. Mail-order and retail pharmacies were identified as central providers in the overall network and majority of communities. This study presented metrics and approach for assessment of provider collaboration. To study how these collaborative relations impact the patients, in the last study, we presented a framework to assess impacts of healthcare provider collaboration on patient outcomes. </p> <p>We focused on patients with diabetes, hypertension, and hyperlipidemia due to their similar healthcare needs and utilization. Similar to the second study, social network analysis and a multi-scale community detection algorithm were used to identify networks and communities of healthcare providers. We identified providers who were the majority source of care for patients over a three-year period. Regression models using generalized estimating equations were developed to assess the impact of majority source of care provider community-level centrality on patient outcomes. Higher connectedness (higher degree centrality) and higher access (higher closeness centrality) of the majority source of care provider were associated with reduced number of inpatient hospitalization and emergency department visits. </p> <p>This research proposed a framework based on the social network analysis that provides metrics for assessment of care team relations using large-scale health data. These metrics help implementation experts to identify influencers in the network for better design of care intervention programs. The framework is also useful for health services researchers to assess impact of care teams’ relations on patient outcomes. </p> <br> <p> </p>

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第22053号 / 工博第4634号 / 新制||工||1723(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 川﨑 雅史, 教授 CRUZ Ana Maria , 准教授 松島 格也 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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This master thesis deals with development of a social network for organizing amateurish matches in collective sports. It is implemented in JavaScript. Client side is written in AngularJS framework. Server side is built on Node.js platform, uses framework Express and NoSQL database MongoDB. Resources stored on server are accessible via REST API. The social network integrates some plugins from existing social networks. For example login dialog or share buttons. The created application supports web browsers Google Chrome, Mozilla Firefox and Internet Explorer version 10 or higher.

abstract: Many existing applications of machine learning (ML) to cybersecurity are focused on detecting malicious activity already present in an enterprise. However, recent high-profile cyberattacks proved that certain threats could have been avoided. The speed of contemporary attacks along with the high costs of remediation incentivizes avoidance over response. Yet, avoidance implies the ability to predict - a notoriously difficult task due to high rates of false positives, difficulty in finding data that is indicative of future events, and the unexplainable results from machine learning algorithms. In this dissertation, these challenges are addressed by presenting three artificial intelligence (AI) approaches to support prioritizing defense measures. The first two approaches leverage ML on cyberthreat intelligence data to predict if exploits are going to be used in the wild. The first work focuses on what data feeds are generated after vulnerability disclosures. The developed ML models outperform the current industry-standard method with F1 score more than doubled. Then, an approach to derive features about who generated the said data feeds is developed. The addition of these features increase recall by over 19% while maintaining precision. Finally, frequent itemset mining is combined with a variant of a probabilistic temporal logic framework to predict when attacks are likely to occur. In this approach, rules correlating malicious activity in the hacking community platforms with real-world cyberattacks are mined. They are then used in a deductive reasoning approach to generate predictions. The developed approach predicted unseen real-world attacks with an average increase in the value of F1 score by over 45%, compared to a baseline approach. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

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This thesis investigates how quantitative user data, extracted from server logs, and clustering algorithms can be used to model and understand user-behavior. The thesis also investigates how the results compare to the more traditional method of qualitative user-behavior analysis through interviews and observations. The results show that clustering of all user data, as opposed to interviewing only a small subset of users, increases the reliability of findings. However, the quantitative method has a risk of missing important insights that can only be discovered through observation of the user. The conclusion drawn in this thesis is that a combination of both is necessary to truly understand the user-behavior. / Denna uppsats undersöker hur kvantitativ användardata, extraherad från serverloggar, och klustringsalgoritmer kan användas för att modellera och förstå användarbeteende. Uppsatsen undersöker också hur resultatet av denna metod skiljer sig från resultatet av den mer traditionella kvalitativa metoden för användarbeteendeanalys, baserad på intervjuer och observationer. Resultatet visar att klustring av all användardata, istället för att intervjuer med endast en delmängd av användarna, ökar pålitligheten i analysen. Dock visar resultatet också att den kvantitativa metoden riskerar att missa viktiga insikter som bara kan upptäckas med hjälp av observationer. Slutsatsen är att en kombination av både den kvantitativa och den kvalitativa metoden behövs för att helt kunna förstå användarbeteendet.