Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2016-08-31T20:54:09Z No. of bitstreams: 2 Dissertação - Vinícius Vargas Vieira dos Santos - 2016.pdf: 1721860 bytes, checksum: d9133355c0bd533b0b6fee0bbeb0f5ad (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-09-01T12:13:50Z (GMT) No. of bitstreams: 2 Dissertação - Vinícius Vargas Vieira dos Santos - 2016.pdf: 1721860 bytes, checksum: d9133355c0bd533b0b6fee0bbeb0f5ad (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-09-01T12:13:50Z (GMT). No. of bitstreams: 2 Dissertação - Vinícius Vargas Vieira dos Santos - 2016.pdf: 1721860 bytes, checksum: d9133355c0bd533b0b6fee0bbeb0f5ad (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-04-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Big data, Meio e Linguagem: Novas tecnologias e práticas linguísticas aims to assimilate possible relationships between new digital media and certain conceptual aspects of language, such as meaning and performativity. Big data is a term that refers to digital data gathering, which characterized mass communication media in the past two decades, and it is directly related to the current configuration of Web 2.0 technology services platform. Complex contemporary objects such as big data call for methodological development to meet their super diverse natures. Therefore, it was the goal of this research to expand disciplinary boundaries, searching for theoretical basis in technology studies in the purpose to understand the nature of new media supports. Devices such as computers and mobile phones, with access to the World Wide Web, are increasingly transforming the landscape of linguistic exchanges, enabling communication practices to take place through them. This is why we understand the very computational structure as the medium (media) through which language happens, from then on this structure’s design features (affordances) are stimulating semantic anchoring and linguistic performativity. The scales of excessive volume and variety of digital data and its high speed, which characterize the big data, change the social context settings, and thus causing updates on language. After all, contexts in virtual environments collapse because in assuming the characteristics of the medium they reveal themselves as super diverse, simultaneous, fragmented, unstructured, missing family markers, exceeding traditional scales of time, space and social reach. / Big data, Meio e Linguagem: Novas tecnologias e práticas linguísticas objetiva assimilar possíveis relações entre novas mídias digitais e certos aspectos conceituais da linguagem, como significado e performatividade. Big data é o termo que se refere ao acúmulo de dados digitais que caracterizou as mídias de comunicação em massa nas duas últimas décadas e está diretamente relacionado à atual configuração da plataforma de serviços de tecnologia Web 2.0. Objetos contemporâneos complexos, como big data, nos remetem à consequente necessidade de conceber metodologias que correspondam a suas naturezas superdiversas. Por conseguinte, teve-se em vista, na presente pesquisa, a necessidade de se expandir fronteiras disciplinares, buscando em estudiosos das tecnologias subsídios teóricos para compreensão da natureza dos novos suportes midiáticos. Aparelhos como computadores e celulares com acesso à World Wide Web estão aceleradamente transformando o panorama das trocas linguísticas, possibilitando que práticas comunicacionais, a cada dia mais, realizem-se através dos mesmos. É neste ponto que se compreende a própria estrutura computacional como o meio (mídia) através do qual se efetiva a linguagem, a partir de então suas características próprias de design (affordances) passam a estimular a ancoragem semântica e a performatividade linguística. As escalas de desmedido volume e variedade de dados digitais e altos índices de velocidade que caracterizam o big data modificam as paisagens de contexto social, provocando, consequentemente, atualizações nas escalas da linguagem. Afinal, contextos em ambientes virtuais entram em colapso, pois ao assumir as próprias características do meio, revelam-se superdiversos, simultâneos, fragmentados, não estruturados, ausentes de marcadores familiares, excedendo escalas tradicionais de tempo, espaço e alcance social.

Context. Big data visualization is a vital part of today's technological advancement. It is about visualizing different variables on a graph, map, or other means often in real-time. Objectives. This study aims to determine what challenges there are for big data visualization, whether significant amounts of data impact the visualization, and finding existing solutions for the problems. Methods. Databases used in this systematic literature review include Inspec, IEEE Xplore, and BTH Summon. Papers are included in the review if certain criteria are upheld. Results. 6 solutions are found to reduce large data sets and reduce latency when viewing 2D and 3D graphs. Conclusions. In conclusion, many solutions exist in various forms to improve visualizing graphs of different dimensions. Future grows of data might change this though and might require new solutions of the growing data.

Large amounts of data in various forms are generated at a fast pace in today´s society. This is commonly referred to as “Big Data”. Making use of Big Data has been increasingly important for both business and in research. The forest industry is generating big amounts of data during the different processes of forest harvesting. In Sweden, forest infor-mation is sent to SDC, the information hub for the Swedish forest industry. In 2014, SDC received reports on 75.5 million m3fub from harvester and forwarder machines. These machines use a global stand-ard called StanForD 2010 for communication and to create reports about harvested stems. The arrival of scalable cloud technologies that com-bines Big Data with machine learning makes it interesting to develop an application to analyze the large amounts of data produced by the forest industry. In this study, a proof-of-concept has been implemented to be able to analyze harvest production reports from the StanForD 2010 standard. The system consist of a back-end and front-end application and is built using cloud technologies such as Apache Spark and Ha-doop. System tests have proven that the concept is able to successfully handle storage, processing and machine learning on gigabytes of HPR files. It is capable of extracting information from raw HPR data into datasets and support a machine learning pipeline with pre-processing and K-Means clustering. The proof-of-concept has provided a code base for further development of a system that could be used to find valuable knowledge for the forest industry.

This work describes the design and implementation of space weather related phenomena within the interactive astro-visualization software OpenSpace. Data sets from the Community Coordinated Modelling Center (CCMC) at the National Aeronautics and Space Administration (NASA) were used to implement time-varying high-resolution solar imagery from space observatory spacecraft and time-varying field lines from the different models produced at the CCMC. The obtained results were used to take an audience on an interactive journey through the solar system, at the world’s first ever live planetarium show about space weather.

A/B testing combined with funnel analysis is a highly interesting technique to support data driven decision making. This thesis outlines a scalable architecture that gathers custom defined events and applies funnel analysis to gain valuable insights about user behaviour. The insights of the users are discussed from an A/B testing point of view, however, these insights are just as valuable for scenarios outside A/B testing as well. Custom defined events together with A/B testing is an interesting combination, since it provides opportunities to test different versions of an application against each other, based on relevant metrics. Where the vision is to determine which of the tested application versions that is best. The power to make smart data driven decisions lies in the hand of good data analysis of the end-users. Having pre-defined metrics such as counts, of some sort, is one way to do it. However, it reduces the flexibility to let e.g. application managers to "dig deeper" into what is actually happening. Funnel analysis is a technique to analyze sequences, and can be used to analyze user behaviour in a sequential matter. There are different techniques to provide such tools, e.g. with Google Analytics, users can pre-define funnel steps that they want Google Analytics to register when events are logged. This thesis will instead strive to not require anything being pre-defined and also make it possible, at high scale, provide dynamic low latency queries. A proof of concept architecture has been presented in this thesis, to support two problematic ends of a spectrum, that is; at scale, both to store custom events, and at the same time be able to interactively run dynamic low latency funnel analysis on the events.

Truecaller is a mobile application with over 200 million unique users worldwide. Every day truecaller stores over 1 billion rows of data that they use to analyse for improving their product. The data is stored in Hadoop, which is a framework for storing and analysing large amounts of data on a distributed file system. In order to be able to analyse these large amounts of data the analytics team needs a new solution for more lightweight, ad-hoc analysis. This thesis evaluates the performance of the query engine Presto to see if it meets the requirements to help the data analytics team at truecaller gain efficiency. By using a design-science methodology, Presto’s pros and cons are presented. Presto is recommended as a solution to be used together with the tools today for specific lightweight use cases for users that are familiar with the data sets used by the analytics team. Other solutions for future evaluation are also recommended before taking a final decision.Keywords: Hadoop, Big Data, Presto, Hive, SQL on Hadoop / <p>Validerat; 20160819 (global_studentproject_submitter)</p>

L'existence de mécanismes juridiques de protection de la vie privée en droit commun est irréfutable. Ceux dont le patient peut se prévaloir à l'occasion de la relation de soin demeurent nébuleux. La spécificité de cette relation, et des atteintes physiques et morales à la vie privée qui y sont consommées - regard, le toucher, nudité et échange d’informations privées - justifient un encadrement spécial et des protections spécifiques, existantes - mais à repenser - pour accompagner les enjeux posés par l'évolution et la modification de la relation de soin. L'émergence de nouveaux acteurs en santé, aux aspirations propres, modifie incontestablement l'objectif et les conséquences de cette relation. La donnée de santé, composante sous-estimée de la vie privée, en ce qu'elle ne transite plus uniquement du patient vers le professionnel de santé - et inversement - doit être encadrée, tant les enjeux économiques et politiques qui y sont afférents sont importants. La "valeur" de la vie privée doit être recentrée, à l’heure où la consommation, l’échange instantané d’informations et la publicité semblent avoir pris le pas sur elle. / The existence of legal mechanisms for the protection of privacy under common law is irrefutable. Those that the patient can claim during the care relationship remain unclear. The specific nature of this relationship, and the physical and moral impairments to privacy that are consumed in it - look, touch, nudity and the exchange of private information - justify a special framework and specific protections, existing but repensable, for accompany the stakes posed by the evolution and the modification of the care relationship. The emergence of new actors in health, with their own aspirations, undoubtedly modifies the objective and consequences of this relationship. Health data, an underestimated component of privacy, in that it n° longer passes only from the patient to the healthcare professional - and vice versa - must be framed, both the economic and political stakes associated with it . The "value" of privacy must be refocused, at a time when consumption, instantaneous exchange of information and “publicy” seem to have taken precedence over it.

This multidisciplinary thesis is concerned with the prediction of drug formulations for the reduction of drug toxicity. Both scientific and computational approaches are utilised to make original contributions to the field of predictive toxicology. The first part of this thesis provides a detailed scientific discussion on all aspects of drug formulation and toxicity. Discussions are focused around the principal mechanisms of drug toxicity and how drug toxicity is studied and reported in the literature. Furthermore, a review of the current technologies available for formulating drugs for toxicity reduction is provided. Examples of studies reported in the literature that have used these technologies to reduce drug toxicity are also reported. The thesis also provides an overview of the computational approaches currently employed in the field of in silico predictive toxicology. This overview focuses on the machine learning approaches used to build predictive QSAR classification models, with examples discovered from the literature provided. Two methodologies have been developed as part of the main work of this thesis. The first is focused on use of directed bipartite graphs and Venn diagrams for the visualisation and extraction of drug-vehicle relationships from large un-curated datasets which show changes in the patterns of toxicity. These relationships can be rapidly extracted and visualised using the methodology proposed in chapter 4. The second methodology proposed, involves mining large datasets for the extraction of drug-vehicle toxicity data. The methodology uses an area-under-the-curve principle to make pairwise comparisons of vehicles which are classified according to the toxicity protection they offer, from which predictive classification models based on random forests and decisions trees are built. The results of this methodology are reported in chapter 6.

Decision-making is no longer based on human preferences and expertise alone. The era of big data brings up new challenges with business analytics for organizations that want a competitive advantage. Previous research shows that a lot of studies have been made on why this era is now crucial to organizations but not how they can adapt it. In this case study there is a glimpse of how a traditional organization with an old mindset can catch up on the new technological advantages. The purpose of this study is to understand how a traditional company in Sweden is affected by analytics and if it is valuable to them.For us to be able to create our theoretical framework, we based our on peer-reviewed material but also technological and science blogs from key experts in the field. The material examines the most essential and crucial elements within the area of business analytics and data management. The theoretical framework has guided our work when formulating and refining the research question and the interview questions.The results of the study clearly show that our case is on the right track with new development and projects, but there are still a lot of milestones to achieve before these are fulfilled. Issues within the company have to be solved and there is a need to modify and change the culture in the organization to a more data-driven decisive culture. The study gives a clear insight into the challenges that organizations have to face and overcome before making radical changes.

Platform based ecosystems are becoming dominant models in the software engineering industry. ‘Big data’ has recently gained increased attention from both academia and practitioners and it is believed that big data affects every sector and industry. While an abundance of research focuses on big data and platform-based ecosystems, these two are typically approached as secluded spheres. This study aimed toward an investigation of big data’s role in the evolution of platform-based ecosystems in the software engineering industry. In the present thesis the influence of big data on the software engineering industry and more specifically, the impact of big data on the evolution of software ecosystems, is examined. A case study focused on a platform owner and pioneer in the software engineering industry has been conducted. This study identifies challenges and opportunities triggered by the advent of big data in context of platform-based ecosystems. Hence, considerable insight regarding the impact of big data on contemporary platform providers and the evolution of platform-centric ecosystems is gained. The findings illustrate that software ecosystems are affected by big data in a positive manner, but some identified challenges emerge and have to be tackled. Additionally, in this paper, it is suggested that both academia and practitioners should dig deeper into this relationship and identify how the evolution of platform-based ecosystems is impacted by the advent of big data.