Unfortunately, cyber-attacks, which are the consequence of our increasing dependence on digital technology, is a phenomenon that we have to live with today. As technology becomes more advanced and complex, so have the types of malware that are used in these cyber-attacks. Currently, targeted cyber-attacks directed at CIIs such as financial institutions and telecom companies are on the rise. A particular group of malware known as APTs, which are used for targeted attacks, are very difficult to detect and prevent due to their sophisticated and stealthy nature. These malwares are able to attack and wreak havoc (in the targeted system) within a matter of seconds; this is very worrying because traditional cyber security defence systems cannot handle these attacks. The solution, as proposed by some in the industry, is the use of BDA systems. However, whilst it appears that BDA has achieved greater success at large companies, little is known about success at smaller companies. Also, there is scarcity of research addressing how BDA is deployed for the purpose of detecting and preventing cyber-attacks on CII. This research examines and discusses the effectiveness of the use of BDA for detecting cyber-attacks and also describes how such a system is deployed. To establish the effectiveness of using a BDA, a survey by questionnaire was conducted. The target audience of the survey were large corporations that were likely to use such systems for cyber security. The research concludes that a BDA system is indeed a powerful and effective tool, and currently the best method for protecting CIIs against the range of stealthy cyber-attacks. Also, a description of how such a system is deployed is abstracted into a model of meaningful practice.

<p>This paper explores the effect of credit rating agency’s (CRA) reputation on the discretionary disclosures of corporate bond issuers. Academics, practitioners, and regulators disagree on the informational role played by major CRAs and the usefulness of credit ratings in influencing investors’ perception of the credit risk of bond issuers. Using management earnings forecasts as a measure of discretionary disclosure, I find that investors demand more (less) disclosure from bond issuers when the ratings become less (more) credible. In addition, using content analytics, I find that bond issuers disclose more qualitative information during periods of low CRA reputation to aid investors better assess credit risk. That the corporate managers alter their voluntary disclosure in response to CRA reputation shocks is consistent with credit ratings providing incremental information to investors and reducing adverse selection in lending markets. Overall, my findings suggest that managers rely on voluntary disclosure as a credible mechanism to reduce information asymmetry in bond markets.</p> / Dissertation

The aim of this work is the analysis of current practices and techniques of Internet marketing. The work is divided into two parts. The theoretical part presents the current internet marketing tools, the optimal utilization is reflected in the practical part, which is carried out a comprehensive plan for internet marketing strategy with emphasis on SEO for start-up company in the field of accommodation and hospitable service. This is a company Penzion U Lišky, which offers accommodation in Southern Bohemia. The work will serve as a guide for small and medium organizations in the development of marketing projects. The practical part of the evidence considered in developing websites and methods to optimize site for search engines. The next section provides recommendations for use of PPC campaigns and presentation of the company's social networking site Facebook. The analysis methods for increasing traffic through social networking, PR articles, blogging and other tools.

The goal of the thesis is to analyze the behaviour of Faculty of Management website users and to formulate on its grounds the recommendations leading to increase of website usability and conversion statistics. The tool used in the thesis for this purpose are the heatmaps, in particular these two variants - mouse-tracking heatmaps and predictive heatmaps. The theoretical part describes the different types of heatmaps, their position within the framework of the web analytics and the principles of working with them. The content of the practical part is formed by the analysis of seven specific web pages and a recommendation for their optimization.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23315号 / 情博第751号 / 新制||情||128(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 緒方 広明, 教授 黒田 知宏, 教授 楠見 孝 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

The purpose of this project is to work as an interdisciplinary team whose primary role is to mentor a team of WPI undergraduate students completing their Major Qualifying Project (MQP) in collaboration with Vestigo Ventures, LLC. (“Vestigo Ventures�) and Cogo Labs. We worked closely with the project sponsors at Vestigo Ventures and Cogo Labs to understand each sponsor’s goals and desires, and then translated those thoughts into actionable items and concrete deliverables to be completed by the undergraduate student team. As a graduate student team with a diverse set of educational backgrounds and a range of academic and professional experiences, we provided two primary functions throughout the duration of this project. The first function was to develop a roadmap for each individual project, with concrete steps, justification, goals and deliverables. The second function was to provide the undergraduate team with clarification and assistance throughout the implementation and completion of each project, as well as provide our opinions and thoughts on any proposed changes. The two teams worked together in lock-step in order to provide the project sponsors with a complete set of deliverables, with the undergraduate team primarily responsible for implementation and final delivery of each completed project.

The purpose of this project is to work as an interdisciplinary team whose primary role is to mentor a team of WPI undergraduate students completing their Major Qualifying Project (MQP) in collaboration with Vestigo Ventures, LLC. (“Vestigo Ventures�) and Cogo Labs. We worked closely with the project sponsors at Vestigo Ventures and Cogo Labs to understand each sponsor’s goals and desires, and then translated those thoughts into actionable items and concrete deliverables to be completed by the undergraduate student team. As a graduate student team with a diverse set of educational backgrounds and a range of academic and professional experiences, we provided two primary functions throughout the duration of this project. The first function was to develop a roadmap for each individual project, with concrete steps, justification, goals and deliverables. The second function was to provide the undergraduate team with clarification and assistance throughout the implementation and completion of each project, as well as provide our opinions and thoughts on any proposed changes. The two teams worked together in lock-step in order to provide the project sponsors with a complete set of deliverables, with the undergraduate team primarily responsible for implementation and final delivery of each completed project.

The idea of a Future Internet platform is to deliver reusable and common functionalities to facilitate making wide ranges of software products and services.  The Future Internet platform, introduced by the Future Internet Public Private Partnership (FI-PPP) project, makes the common functionalities available through so-called Enablers to be instantly integrated into software products and services with less cost and complexity rather than a development from scratch. Quality assessment of software products and services and gaining insights into whether the quality fulfills users’ expectations within the platform are challenging. The challenges are due to the propagation of quality in the heterogeneous composite software that uses Enablers and infrastructure developed by third parties. The practical problem is how to assess the quality of such composite software as well as the impacts of the quality on users’ Quality of Experience (QoE). The research objective is to study an analytics-driven Quality-Impact approach identifying how software quality analytics together with their impact on QoE of users can be used for the assessment of software products and services in a Future Internet platform. The research was conducted with one systematic mapping study, two solution proposals, and one empirical study. The systematic mapping study is contributed to produce a map overviewing important analytics for managing a software ecosystem. The thesis also proposes a solution to introduce a holistic software-human analytics approach in a Future Internet platform. As the core of the solution, it proposes a Quality-Impact inquiry approach exemplified with a real practice. In the early validation of the proposals, a mixed qualitative-quantitative empirical research is conducted with the aim of designing a tool for the inquiry of user feedback. This research studies the effect of the instrumented feedback tool on QoE of a software product. The findings of the licentiate thesis show that satisfaction, performance, and freedom from risks analytics are important groups of analytics for assessing software products and services.  The proposed holistic solution takes up the results by describing how to measure the analytics and how to assess them practically using a composition model during the lifecycle of products and services in a Future Internet platform. As the core of the holistic approach, the Quality-Impact assessment approach could elicit relationships between software quality and impacts of the quality on stakeholders. Moreover, the early validation of the Quality-Impact approach parameterized suitable characteristics of a feedback tool. We found that disturbing feedback tools have negligible impacts on the perceived QoE of software products. The Quality-Impact approach is helpful to acquire insight into the success of software products and services contributing to the health and sustainability of the platform. This approach was adopted as a part of the validation of FI-PPP project. Future works will address the validation of the Quality-Impact approach in the FI-PPP or other real practices.

Visual analytics tools play an important role in the scenario of big data solutions, combining data analysis and interactive visualization techniques in effective ways to support the incremental exploration of large data collections from a wide range of domains. One particular challenge for visual analytics is the analysis of multidimensional datasets, which consist of many observations, each being described by a large number of dimensions, or attributes. Finding and understanding data-related patterns present in such spaces, such as trends, correlations, groups of related observations, and outliers, is hard. Dimensionality reduction methods, or projections, can be used to construct low (two or three) dimensional representations of high-dimensional datasets. The resulting representation can then be used as a proxy for the visual interpretation of the high-dimensional space to efficiently and effectively support the above-mentioned data analysis tasks. Projections have important advantages over other visualization techniques for multidimensional data, such as visual scalability, high degree of robustness to noise and low computational complexity. However, a major obstacle to the effective practical usage of projections relates to their difficult interpretation. Two main types of interpretation challenges for projections are studied in this thesis. First, while projection techniques aim to preserve the so-called structure of the original dataset in the final produced layout, and effectively achieve the proxy effect mentioned earlier, they may introduce a certain amount of errors that influence the interpretation of their results. However, it is hard to convey to users where such errors occur in the projection, how large they are, and which specific data-interpretation aspects they affect. Secondly, interpreting the visual patterns that appear in the projection space is far from trivial, beyond the projections ability to show groups of similar observations. In particular, it is hard to explain these patterns in terms of the meaning of the original data dimensions. In this thesis we focus on the design and development of novel visual explanatory techniques to address the two interpretation challenges of multidimensional projections outlined above. We propose several methods to quantify, classify, and visually represent several types of projection errors, and how their explicit depiction helps interpreting data patterns. Next we show how projections can be visually explained in terms of the highdimensional data attributes, both in a global and a local way. Our proposals are designed to be easily added, and used with, any projection technique, and in any application context using such techniques. Their added value is demonstrated by presenting several exploration scenarios involving various types of multidimensional datasets, ranging from measurements, scientific simulations, software quality metrics, software system structure, and networks. / Ferramentas de análise visual desempenham um papel importante no cenário de soluções para grandes volumes de dados (big data), combinando análise de dados e técnicas interativas de visualização de forma eficaz para apoiar a exploração incremental de coleções de dados em diversos domínios. Um desafio importante em análise visual é a exploração de conjuntos de dados multidimensionais, que consistem em muitas observações, sendo cada uma descrita por um grande número de dimensões, ou atributos. Encontrar e compreender os padrões presentes em tais espaços, tais como tendências, correlações, grupos de observações relacionadas e valores extremos, é difícil. Técnicas de redução de dimensionalidade ou projeções são utilizadas para construir, a partir de conjuntos de dados multidimensionais, representações de duas ou três dimensões que podem então ser utilizadas com substitutas do espaço original para sua interpretação visual, apoiando de forma eficiente as tarefas de análise de dados acima mencionadas. Projeções apresentam vantagens importantes sobre outras técnicas de visualização para dados multidimensionais, tais como escalabilidade visual, resistência a ruídos e baixa complexidade computacional. No entanto, um grande obstáculo para o uso prático de projeções vem da sua difícil interpretação. Dois principais tipos de desafios de interpretação de projeções são estudados nesta tese. Em primeiro lugar, mesmo que as técnicas de projeção tenham como objetivo preservar, na representação final, a estrutura do conjunto de dados original, elas podem introduzir uma certa quantidade de erros que influenciam a interpretação dos seus resultados. No entanto, é difícil transmitir aos usuários onde tais erros ocorrem na projeção, quão severos eles são e que aspectos específicos da interpretação dos dados eles afetam. Em segundo lugar, interpretar os padrões visuais que aparecem em uma projeção, além da percepção de grupos de observações semelhantes, está longe de ser trivial. Em particular, é difícil explicar tais padrões em termos do significado das dimensões dos dados originais. O trabalho desenvolvido nesta tese concentra-se no projeto e desenvolvimento de novas técnicas visuais explicativas para lidar com os dois desafios de interpretação de projeções multidimensionais descritos acima. São propostos alguns métodos para quantificar, classificar e representar visualmente diversos tipos de erros de projeção, e é descrito como essas representações explícitas ajudam na interpretação dos padrões dos dados. Além disso, também são propostas técnicas visuais para explicar projeções em termos dos atributos dos dados multidimensionais, tanto de forma global quanto local. As propostas apresentadas foram concebidas para serem facilmente incorporadas e usadas com qualquer técnica de projeção e em qualquer contexto de aplicação. As contribuições são demonstradas pela apresentação de vários cenários de exploração, envolvendo vários tipos de conjuntos de dados multidimensionais, desde medições e simulações científicas até métricas de qualidade de software, estruturas de sistema de software e redes.

De nombreux documents concernant l'émergence, la propagation ou le suivi de maladies humaines et animales sont quotidiennement publiés sur le Web. Afin de prévenir l'expansion des maladies, les épidémiologistes doivent constamment rechercher ces documents et les étudier afin de détecter les foyers de propagation le plus tôt possible. Dans cette thèse, nous nous intéressons aux deux activités liées à ce travail de veille afin de proposer des outils visuels permettant de faciliter/accélérer l'accès aux informations pertinentes. Nous nous focalisons sur les maladies animales, qui ont été moins étudiées et qui pourtant peuvent avoir de lourdes conséquences sur les activités humaines (maladies transmises d'animaux à humains, épidémies dans les élevages, ...).La première activité du veilleur consiste à collecter les documents issus du Web. Pour cela, nous proposons EpidVis, un outil visuel permettant aux épidémiologistes de regrouper et structurer les mots-clés nécessaires à leurs recherches, construire visuellement des requêtes complexes, les lancer sur différents moteurs de recherche et visualiser les résultats retournés. La seconde activité du veilleur consiste à explorer un grand nombre de documents concernant les maladies. Ces documents contiennent non seulement des informations telles que les noms des maladies, les symptômes associés, les espèces infectées, mais aussi des informations de type spatio-temporelles. Nous proposons EpidNews, un outil de visualisation analytique permettant d'explorer ces données en vue d'en extraire des informations. Les deux outils ont été réalisés dans le cadre d'une étroite collaboration avec des experts en épidémiologie. Ces derniers ont réalisé des études de cas pour montrer que les fonctionnalités des propositions étaient complètement adaptées et permettaient de pouvoir facilement extraire de la connaissance. / Many documents concerning emergence, spread or follow-up of human and animal diseases are published daily on the Web. In order to prevent the spread of disease, epidemiologists must frequently search for these documents and analyze them to detect outbreaks as early as possible. In this thesis, we are interested in the two activities related to this monitoring work in order to produce visual tools facilitating the access to relevant information. We focus on animal diseases, which have been less studied but can have serious consequences for human activities (diseases transmitted from animals to humans, epidemics in livestock ...).The first activity is to collect documents from the Web. For this, we propose EpidVis, a visual tool that allows epidemiologists to group and organize the keywords used for their research, visually build complex queries, launch them on different search engines and view the results returned. The second activity is to explore a large number of documents concerning diseases. These documents contain not only information such as disease names, associated symptoms, infected species, but also spatio-temporal information. We propose EpidNews, a visual analytics tool to explore this data for information extraction. Both tools were developed in close collaboration with experts in epidemiology. The latter carried out case studies to show that the functionalities of the proposals were completely adapted and made it possible to easily extract knowledge.