Businesses have recognized that customers provide value to the firm beyond transactions, and leveraging this value through relationships in social media is a new area of interest for both academics and practitioners. Recent research has investigated how businesses can best manage their online presence on platforms not fully under their control, such as Facebook, YouTube, Instagram, TripAdvisor, and Yelp, among others. This dissertation extends the literature of customer engagement in social media through four contributions. First, we propose a framework that foregrounds the textual artifacts involved in online communication. Second, we develop a novel method for discovering the elements of successful Business to Customer (B2C) messages in online communities. Third, we propose a method, validated through experimentation, for finding critical product feedback in Customer to Customer (C2C) communications. Finally, we demonstrate that a set of novel numerical features can enhance the discovery of product defect mentions in C2C communications. We conclude by proposing a research agenda suggested by the framework that will further enhance our understanding of the complex customer interactions that characterize business in the era of social media. / Ph. D. / Businesses have recognized that customers provide value to the firm beyond transactions, and leveraging this value through relationships in social media is a new area of interest for both academics and practitioners. Recent research has investigated how businesses can best manage their online presence on platforms not fully under their control, such as Facebook, YouTube, Instagram, TripAdvisor, and Yelp, among others. This dissertation extends the literature of customer engagement in social media through four contributions. First, we propose a framework that foregrounds the textual artifacts involved in online communication. Second, we develop a novel method for discovering the elements of successful Business to Customer (B2C) messages in online communities. Third, we propose a method, validated through experimentation, for finding critical product feedback in Customer to Customer (C2C) communications. Finally, we demonstrate that a set of novel numerical features can enhance the discovery of product defect mentions in C2C communications. We conclude by proposing a research agenda suggested by the framework that will further enhance our understanding of the complex customer interactions that characterize business in the era of social media.

With the dramatic increase and continued growth of digital information, developing Visual Analytic systems that support human cognition and insight generation are more necessary than ever before, but there is currently no content-agnostic method for measuring or com- paring how well a system facilitates analysis. Researchers in industry and academia are developing advanced tools that offer automated data analysis combined with support for human sense-making; tools for a wide variety of sense-making tasks are freely available. Now, the pressing question is: which tool works best, and for what? We show that using Shannon's entropy and self-information measures will provide a measure of the complexity reduction that results from an analyst's actions while sorting the information. Further, we demonstrate that reduced complexity can be linked to the knowledge gained. This is important, because a metric for objectively evaluating the success of current systems in generating insights would establish a standard that future tools could build on. This work could help guide researchers and developers in making the next generation of analytic tools, and in the age of big data the effect of such tools could potentially impact everyone. / Master of Science / With the dramatic increase and continued growth of digital information, developing systems that enables humans to make sense of all the data are more necessary than ever before, but there is currently no one-size-fits-all method for measuring or comparing how well a system helps people gain such insight. Rather than trying to pin down a definition of what insight is, we instead look at complexity reduction—with the intuition that, before we can make sense of complex data, we must somehow simplify it in a meaningful way. We show that using Shannon’s entropy and self-information values will provide a measure of the complexity reduction that results from an analyst’s actions while sorting information, and further demonstrate that reduced complexity can be linked to the knowledge gained. This work is important, because a metric for objectively evaluating the success of current systems in generating insights would establish a standard that future tools could build on. This work could help guide researchers and developers in making the next generation of analytic tools, and in the age of big data the effect of such tools could potentially impact everyone.

When exploring large, high-dimensional datasets, analysts often utilize two techniques for reducing the data to make exploration more tractable. The first technique, dimension reduction, reduces the high-dimensional dataset into a low-dimensional space while preserving high-dimensional structures. The second, clustering, groups similar observations while simultaneously separating dissimilar observations. Existing work presents a number of systems and approaches that utilize these techniques; however, these techniques can cooperate or conflict in unexpected ways. The core contribution of this work is the systematic examination of the design space at the intersection of dimension reduction and clustering when building intelligent, interactive tools in visual analytics. I survey existing techniques for dimension reduction and clustering algorithms in visual analytics tools, and I explore the design space for creating projections and interactions that include dimension reduction and clustering algorithms in the same visual interface. Further, I implement and evaluate three prototype tools that implement specific points within this design space. Finally, I run a cognitive study to understand how analysts perform dimension reduction (spatialization) and clustering (grouping) operations. Contributions of this work include surveys of existing techniques, three interactive tools and usage cases demonstrating their utility, design decisions for implementing future tools, and a presentation of complex human organizational behaviors. / Doctor of Philosophy / When an analyst is exploring a dataset, they seek to gain insight from the data. With data sets growing larger, analysts require techniques to help them reduce the size of the data while still maintaining its meaning. Two commonly-utilized techniques are dimension reduction and clustering. Dimension reduction seeks to eliminate unnecessary features from the data, reducing the number of columns to a smaller number. Clustering seeks to group similar objects together, reducing the number of rows to a smaller number. The contribution of this work is to explore how dimension reduction and clustering are currently being used in interactive visual analytics systems, as well as to explore how they could be used to address challenges faced by analysts in the future. To do so, I survey existing techniques and explore the design space for creating visualizations that incorporate both types of computations. I look at methods by which an analyst could interact with those projections in other to communicate their interests to the system, thereby producing visualizations that better match the needs of the analyst. I develop and evaluate three tools that incorporate both dimension reduction and clustering in separate computational pipelines. Finally, I conduct a cognitive study to better understand how users think about these operations, in order to create guidelines for better systems in the future.

No / Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders.

No / With a cornucopia of literature on Big Data and Data Analytics it has become a recent buzzword. The literature is full of hymns of praise for big data, and its potential applications. However, some of the latest published material exposes the challenges involved in implementing Big Data (BD) approach, where the uncertainty surrounding its applications is rendering it ineffective. The paper looks at the mind-sets and perspective of executives and their plans for using Big Data for decision making. Our data collection involved interviewing senior executives from a number of world class organisations in order to determine their understanding of big data, its limitations and applications. By using the information gathered by this is used to analyse how well executives understand big data and how well organisations are ready to use it effectively for decision making. The aim is to provide a realistic outlook on the usefulness of this technology and help organisations to make suitable and realistic decisions on its investment. Professionals and academics are becoming increasingly interested in the field of big data (BD) and data analytics. Companies invest heavily into acquiring data, and analysing it. More recently the focus has switched towards data available through the internet which appears to have brought about new data collection opportunities. As the smartphone market developed further, data sources extended to include those from mobile and sensor networks. Consequently, organisations started using the data and analysing it. Thus, the field of business intelligence emerged, which deals with gathering data, and analysing it to gain insights and use them to make decisions (Chen, et al., 2012). BD is seem to have a huge immense potential to provide powerful information businesses. Accenture claims (2015) that organisations are extremely satisfied with their BD projects concerned with enhancing their customer reach. Davenport (2006) has presented applications in which companies are using the power of data analytics to consistently predict behaviours and develop applications that enable them to unearth important yet difficult to see customer preferences, and evolve rapidly to generate revenues.

Dieser Tagungsband beinhaltet die auf dem E-Learning Symposium 2012 an der Universität Potsdam vorgestellten Beiträge zu aktuellen Anwendungen, innovativen Prozesse und neuesten Ergebnissen im Themenbereich E-Learning. Lehrende, E-Learning-Praktiker und -Entscheider tauschten ihr Wissen über etablierte und geplante Konzepte im Zusammenhang mit dem Student-Life-Cycle aus. Der Schwerpunkt lag hierbei auf der unmittelbaren Unterstützung von Lehr- und Lernprozessen, auf Präsentation, Aktivierung und Kooperation durch Verwendung von neuen und etablierten Technologien.

Com a evolução tecnológica pela qual o mundo vem passando nos últimos anos, o tema Marketing Analytics vem ganhando relevância gerencial e acadêmica. No entanto, pouco se avançou no entendimento do que determina a inclinação de uma empresa a adotar a prática de Analytics para tomada de decisão nas atividades de Marketing, ou seja, sua Orientação para Marketing Analytics (OMA). Além disso, pouco se sabe sobre os impactos dessa orientação nos resultados da empresa. Para suprir essas lacunas, essa pesquisa buscou entender quais os construtos antecedentes que poderiam influenciar a Orientação para Marketing Analytics, bem como se essa orientação levaria a um melhor desempenho do negócio. Para tanto, por meio de uma vasta revisão da literatura, delimitou-se o escopo de Orientação para Marketing Analytics, apresentando uma definição para o construto, um modelo teórico e proposições de pesquisa. Na etapa seguinte, foram feitas entrevistas em profundidade com especialistas em Analytics e executivos de marketing para aprofundar o conceito de Orientação para Marketing Analytics e para entender quais os determinantes para uma empresa ser orientada para Marketing Analytics. A terceira e última parte desse trabalho se focou em desenvolver um modelo de mensuração para Orientação para Marketing Analytics e testar o modelo estrutural proposto junto a uma amostra de 127 profissionais de marketing e especialistas em Analytics. As respostas foram analisadas por meio de Modelagem de Equações Estruturais (PLS-SEM) e os resultados indicaram que o suporte da alta administração desempenha um papel relevante na valorização de habilidades analíticas das pessoas, nos investimentos em infraestrutura tecnológica e na orientação para processos da empresa. Além disso, observou-se que todos esses antecedentes apresentam influência significativa na Orientação para Marketing Analytics da empresa que, por sua vez, tem impacto positivo no desempenho percebido do negócio. / With the technological evolution that the world has been going through in recent years, the topic Marketing Analytics has gained managerial and academic relevance. However, little progress has been made in understanding what determines a company\'s willingness to adopt the practice of Analytics for decision-making in Marketing activities, ie its Marketing Analytics Orientation (MAO). In addition, little is known about the impacts of this orientation on company results. To fill these gaps, this research sought to understand which antecedent could influence the Marketing Analytics Orientation, as well as whether such orientation would lead to better business performance. To do so, through a vast literature review, the scope of Marketing Analytics Orientation was delimited, presenting a definition for the construct, a theoretical model and research propositions. In the next step, in-depth interviews were conducted with analytics experts and marketing executives to deepen the concept of Marketing Analytics Orientation and to understand the determinants for a company to be Marketing Analytics-oriented. The third and final part of this work focused on developing a measurement model for Marketing Analytics Orientation and testing the proposed structural model with a sample of 127 marketing professionals and Analytics experts. The responses were analyzed through Structural Equation Modeling (PLS-SEM) and the results indicated that the top management support plays a relevant role in the appreciation of people\'s analytical skills, on investments in technological infrastructure and in the company\'s processes orientation. The result of this work showed that all of these antecedents had significant influence on the company\'s Marketing Analytics Orientation, which in turn had a positive impact on the perceived performance of the business.

Syftet med denna kandidatuppsats är att, via kvalitativa intervjuer, identifiera och beskriva ett landstings process för hur patient- och sjukvårdsdata, genom olika typer av dataanalyser, kan generera rättidiga beslut.   I det valda kvalitativa tillvägagångssättet skapades en semi-strukturerad intervjuguide, baserad på en analysmodell. Fem intervjuer med anställda inom fallstudieorganisationen genomfördes. För att underlätta för respondenterna och ge en tydlig helhetssyn på intervjuns innehåll, fick respondenterna innan intervjun se analysmodellen som skapats.   Deskriptiv analys är den vanligaste dataanalysmodellen som används av respondenterna, i form av verksamhets- och produktionsuppföljning. Det framgår även i undersökningen att det är viktigt med ett brett dataunderlag för beslutsfattning och att Landstinget i Värmland överlag är en mycket faktabaserad organisation. Förutsättningarna för att kunna fatta rättidiga beslut, genererade från dataanalyser, anses vara att veta vilka frågeställningar som ska besvaras, att data snabbt finns tillgänglig och att analyser utförs på denna tillgängliga data. Men även om data snabbt finns tillgängligt för beslutsfattarna och analyser gjorts, medför inte det rättidiga beslut. Data ger inte alltid en korrekt eller sann bild, utan behöver först tolkas innan besluten kan fattas. Tolkningar av data kan skilja sig åt vilket medför att ytterligare en person behöver titta på materialet, vilket också medför att besluten skjuts fram. Det anses även saknas ett enhetligt arbetssätt för hur de anställda arbetar i vårdsystemen.   Rättidiga beslut finns på olika nivåer inom landstinget. När det gäller den övergripande nivån är det inte brådskande med beslut, men ju mer operativt personalen arbetar desto viktigare är det med snabba beslut. Detta visar att ”rättidiga beslut” har olika betydelser, beroende på var i verksamheten de anställda befinner sig. Vården är tidspressad och det är inte alltid det finns möjlighet att analysera de data som finns tillräckligt mycket.

As estrategias de passes têm sido sempre de interesse para a pesquisa de futebol. Desde os inícios do futebol, os técnicos tem usado olheiros, gravações de vídeo, exercícios de treinamento e feeds de dados para coletar informações sobre as táticas e desempenho dos jogadores. No entanto, a natureza dinâmica das estratégias de passes são bastante complexas para refletir o que está acontecendo dentro do campo e torna difícil o entendimento do jogo. Além disso, existe uma demanda crecente pela deteção de padrões e analise de estrategias de passes popularizado pelo tiki-taka utilizado pelo FC. Barcelona. Neste trabalho, propomos uma abordagem para abstrair as sequências de pases e agrupálas baseadas na geometria da trajetória da bola. Para analizar as estratégias de passes, apresentamos um esquema de visualização interátiva para explorar a frequência de uso, a localização espacial e ocorrência temporal das sequências. A visualização Frequency Stripes fornece uma visão geral da frequencia dos grupos achados em tres regiões do campo: defesa, meio e ataque. O heatmap de trajetórias coordenado com a timeline de passes permite a exploração das formas mais recorrentes no espaço e tempo. Os resultados demostram oito trajetórias comunes da bola para sequências de três pases as quais dependem da posição dos jogadores e os ângulos de passe. Demonstramos o potencial da nossa abordagem com utilizando dados de várias partidas do Campeonato Brasileiro sob diferentes casos de estudo, e reportamos os comentários de especialistas em futebol. / Passing strategies analysis has always been of interest for soccer research. Since the beginning of soccer, managers have used scouting, video footage, training drills and data feeds to collect information about tactics and player performance. However, the dynamic nature of passing strategies is complex enough to reflect what is happening in the game and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing sequence analysis popularized by FC Barcelona’s tiki-taka. We propose an approach to abstract passing strategies and group them based on the geometry of the ball trajectory. To analyse passing sequences, we introduce a interactive visualization scheme to explore the frequency of usage, spatial location and time occurrence of the sequences. The frequency stripes visualization provide, an overview of passing groups frequency on three pitch regions: defense, middle, attack. A trajectory heatmap coordinated with a passing timeline allow, for the exploration of most recurrent passing shapes in temporal and spatial domains. Results show eight common ball trajectories for three-long passing sequences which depend on players positioning and on the angle of the pass. We demonstrate the potential of our approach with data from the Brazilian league under several case studies, and report feedback from a soccer expert.

With todays flood of data coming from many types of sources, Machine Learning becomes increasingly important. Though, many times the use of Machine Learning is not enough to make sense of all this data. This makes visualization a very useful tool for Machine Learning practitioners and data analysts alike. Interactive visualization techniques can be very helpful by giving insight on the meaning of the output from classification tasks. In this work, the aim is to explore, implement and evaluate different visualization techniques with the explicit goal of directly relating these visualization to the Machine Learning process. The proposed approach is the development of visualization techniques for a posteriori analysis that combines data exploration and classification evaluation. Results include a modified version of the Radial Visualization technique, called Dual RadViz, and also the use of interactive multiclass Partial Dependence Plots as means of finding counterfactual explanations about Machine Learning classification. An account of some of the many ways Machine Learning and visualization are used together is also given. / Hoje em dia, com o enorme fluxo de dados provenientes de muitos tipos de fontes, Aprendizado de Máquina se torna cada vez mais importante. No entanto, muitas vezes o uso de Aprendizado de Máquina não é o suficiente para que seja possível enxergar o valor e o significado de todos estes dados. Isso faz com que visualização seja uma valiosa ferramenta tanto para analistas de dados quanto para aqueles que praticam tarefas relacionadas à Aprendizado de Máquina. Técnicas de visualização interativa podem ser de grande utilidade por possibilitarem insights sobre o significado do resultado de tarefas de classificação. Neste trabalho, o objetivo é explorar, implementar e avaliar diferentes técnicas de visualização, explicitamente focando em suas relações com o processo de Aprendizado de Máquina. A abordagem proposta se trata do desenvolvimento de técnicas de visualização para análise a posteriori dos resultados de tarefas de classificação, combinando avaliação da classificação e exploração visual de dados. Os resultados incluem uma versão modificada da técnica de Visualização Radial, chamada Dual RadViz, e também o uso de Gráficos de Dependência Parcial multiclasse interativos como meio de se chegar à explicações contrafatuais sobre resultados de classificação. É dado também um relato de algumas das muitas maneiras onde Aprendizado de Máquina e visualização são usados conjuntamente.