Graphlet enumeration is a basic task in graph analysis with many applications. Thus it is important to be able to perform this task within a reasonable amount of time. However, this objective is challenging when the input graph is very large, with millions of nodes and edges. Known solutions are limited in terms of scalability. Distributed computing is often proposed as a solution to improve scalability. How- ever, it has to be done carefully to reduce the overhead cost and to really benefit from the distributed solution. We study the enumeration of four-node graphlets in undirected graphs using a distributed platform. We propose an efficient distributed solution which significantly surpasses the existing solutions. With this method we are able to process larger graphs that have never been processed before and enumerate quadrillions of graphlets using a modest cluster of machines. We convincingly show the scalability of our solution through experimental results. / Graduate

Never had consumers been empowered by information technologies such as social media-enabled portals that permit them to access and conduct all aspects of life and work activities through a mobile phone at any time from anywhere. WeChat, with over 963 million active monthly users, represents such a revolutionary platform. In healthcare, patients can use WeChat to make doctor appointments, access health and lab results, consult with doctors, and check on the queuing status and parking conditions in the health clinics and hospitals. Such social-media-enabled systems have transformed the relationships between consumers and businesses into a new paradigm in which the supply-side is driven by the demand-side. As a result, the new technology is fundamentally changing; not only the context in which business is conducted but also the business itself. The extant literature on technology acceptance, however, has mostly focused on technical functionalities and user characteristics without adequately considering the specific context in which the technology is used. Although these affordance concepts have advanced our knowledge about the interactions between technology and users, the specific contexts in which such interactions occur have been largely ignored. There is a critical literature gap that hinders our ability to understand and provide guidelines to help organizations deal with the complex challenges they face in managing social mediaenabled technologies in today’s changing environment. Our research attempts to bridge this critical literature gap by conceptualizing the concept of contextual affordance, and by examining its determinants and consequences in healthcare services. We use a combination of qualitative method and quantitative method. Research sites are in China across multiple healthcare facilities. The anticipated findings include validated dimensions of contextual affordance and relationships between contextual affordance and its determinants and impacts on clinical process changes and health service outcomes. Theoretically, this study extends the current understanding of affordance by considering contextual dimensions of affordance, and by examining the relationships between contextual affordance and its determinants and consequences. Practically, this study sheds new lights on how organizations should go beyond the out-of-context interactions between technologies and users by considering users’ perceived affordance of technology within the specific contexts of use.

Automated event extraction from free text remains an open problem, particularly when the goal is to identify all relevant events. Manual extraction is currently the only alternative for comprehensive and reliable extraction. Therefore, it is required to have a system that can comprehensively extract events reported in news articles (high recall) and is also scalable enough to handle a large number of articles. In this dissertation, we explore various methods to develop an event extraction system that can mitigate these challenges. We primarily investigate three major problems related to event extraction as follows. (i) What are the strengths and weaknesses of the automated event extractors? A thorough understanding of what can be automated with high success and what leads to common pitfalls is crucial before we could develop a superior event extraction system. (ii) How can we build a hybrid event extraction system that can bridge the gap between manual and automated event extraction? Hybrid extraction is a semi-automated approach that uses an ecosystem of machine learning models along with a carefully designed user interface for extracting events. Since this method is semi-automated it also requires a meticulous understanding of user behavior in order to identify tasks that humans can perform with ease while diverting the more tedious task to the machine learning methods (iii) Finally, we explore methods for displaying extracted events that could simplify the analytical and inference generation processes for an analyst. We particularly aim to develop visualizations that would allow analysts can perform macro and micro level analysis of significant societal events. / Ph. D. / News articles provide information about who did what to whom, when, where, and why. Extracting this structured information from news articles can allow scientific evaluation of widely believed information. However, curating these databases of structured information is not a trivial task. Currently there are two main approaches: manual and automated. Manually curation is not scalable due to labor costs: adding more humans to perform analysis is prohibitively expensive and time consuming. The alternative approach is ‘Automated Extraction’, wherein, machine learning algorithms extract events on their own without any human assistance. Even though this approach can easily scale to work with a large number of articles, it frequently misclassifies events. In this dissertation, we present EMBERS AutoGSR, a framework for comprehensively extracting ‘protest’ events reported in news articles using Hybrid Event Extraction. In the hybrid approach, we use an ecosystem of Filtering, Ranking, and Recommendation models to determine if an article is reporting a protest and, if so, proceed to identify and encode specific characteristics of the event, such as who protested when, where and why? These extracted events are then displayed on an interactive web-based interface that allows manual validation. This manual validation, in turn, helps the automated event extractors learn and evolve from user feedback and error correction. The interface is carefully designed with an aim to minimize the manual effort required for user validation, thereby making it feasible and viable to work with a large number of articles. EMBERS AutoGSR operated 24x7 for a year from October 2015 through September 2016, during which it extracted protest events from news articles that were collected from 19 countries across 8 languages. These extracted events were validated by 12 subject matter experts. The system was evaluated by an independent third party, MITRE corporation. They compared EMBERS AutoGSR events with events that were manually extracted by their team of political scientists. AutoGSR achieved a recall of 0.82 out of 1, and reduced the manual effort required for event extraction by 72%, thereby making the system extremely reliable and scalable.

Graph libraries containing already-implemented algorithms are highly desired since users can conveniently use the algorithms off-the-shelf to achieve fast analyt- ics and prototyping, rather than implementing the algorithms with lower-level APIs. Besides the ease of use, the ability to efficiently process extra large graphs is also required by users. The popular existing graph libraries include the igraph R library and the NetworkX Python library. Although these libraries provide many off-the-shelf algorithms for users, the in-memory graph representation limits their scalability for computing on large graphs. Therefore, in this work, we develop Graph-XLL: a graph library implemented using the WebGraph framework in a vertex-centric manner, with much less memory requirement compared to igraph and NetworkX. Scalable analytics for extra large graphs (up to tens of millions of vertices and billions of edges) can be achieved on a single consumer grade machine within a reasonable amount of time. Such computation would cause out-of-memory error if using igraph or NetworkX. / Graduate

O mercado consumidor passou por diversas transformações ao longo do tempo devido principalmente à evolução tecnológica. A evolução tecnológica proporcionou ao consumidor a possibilidade de escolher por produtos e marcas, e permite a oportunidade de colaborar e influenciar a opinião de outros consumidores através do compartilhamento de experiências, principalmente através da utilização de plataformas digitais. O CRM (gerenciamento do relacionamento com o consumidor) é a forma utilizada pelas empresas para conhecerem o consumidor e criar um relacionamento satisfatório entre empresa e consumidor. Esse relacionamento tem o intuito de satisfazer e fidelizar o consumidor, evitando que ele deixe de consumir a marca e evitando que ele influencie negativamente outros consumidores. O e-CRM é o gerenciamento eletrônico do relacionamento com o consumidor, que possui todas as tradicionais características do CRM, porém com o incremento do ambiente digital. O ambiente digital diminuiu a distância entre pessoas e empresas e se tornou um meio colaborativo de baixo custo de interação com o consumidor. Por outro lado, este é um meio onde o consumidor deixa de ser passivo e se torna ativo, o que o torna capaz de influenciar não só um pequeno grupo de amigos, mas toda uma rede de consumidores. A digital analytics é a medição, coleta, análise e elaboração de relatórios de dados digitais para os propósitos de entendimento e otimização da performance em negócios. A utilização de dados digitais auxilia no desenvolvimento do e-CRM através da compreensão do comportamento do consumidor em um ambiente onde o consumidor é ativo. O ambiente digital permite um conhecimento mais detalhado dos consumidores, baseado não somente nos hábitos de compra, mas também nos interesses e interações. Este estudo tem como objetivo principal compreender como as empresas aplicam os conceitos do e-CRM em suas estratégias de negócios, compreendendo de que forma a digital analytics contribui para o desenvolvimento do e-CRM, e compreendendo como os fatores críticos de sucesso (humano, tecnológico e estratégico) impactam na implantação e desenvolvimento do e-CRM. Quatro empresas de diferentes segmentos foram estudadas através da aplicação de estudo de caso. As empresas buscam cada vez mais explorar as estratégias de e-CRM no ambiente digital, porém existem limitações identificadas devido à captação, armazenamento e análise de informações multicanais, principalmente considerando os canais digitais. Outros fatores como o apoio da alta direção e a compreensão de funcionários para lidar com estratégias focadas no consumidor único também foram identificados neste estudo. O estudo foi capaz de identificar as informações mais relevantes para a geração de estratégias de gerenciamento eletrônico do relacionamento com o consumidor e identificou os aspectos mais relevantes dos fatores críticos de sucesso. / The consumer market has undergone several transformations over time mainly due to technological developments. Technological progress has given the consumer a choice of products and brands, allowing the opportunity to collaborate and influence the opinion of other consumers through the sharing of experiences, specially by the use of digital platforms. The CRM (customer relationship management) is the form used by companies to know the consumer and establish a satisfactory relationship between both. This relationship aims to satisfy and retain consumers, preventing it ceases to consume the brand and preventing it negatively influence on others. The e-CRM is the electronic management of the relationship with the consumer, which has all the traditional CRM features, which increase the digital environment. The digital environment reduced the distance between consumer and companies becoming a collaborative low-cost way of interaction with the consumer. On the other hand, this is a medium where the consumer is no longer passive and becomes active, which makes it able to influence not only a small group of friends, but a whole network of consumers. The digital analytics is the measurement, collection, analysis and preparation of digital data reports for the purposes of understanding and optimizing business performance. The use of digital data helps in the development of e-CRM through understanding consumer behavior in an environment where the consumer is active. The digital environment allows a more detailed knowledge of consumers, based not only on buying habits, but also on the interests and interactions. This study aims to understand how companies apply the concepts of e-CRM in their business strategies, including how the digital analytics contributes to the development of e-CRM, and understanding how the critical success factors (human, technological and strategic) impact in the implementation and development of e-CRM. Four companies from different segments were studied through study case application. Nowadays, Companies are increasingly looking to explore the e-CRM strategies in the digital environment, but there are limitations identified due to capture, storage and analysis of multi-channel information, especially considering digital channels. Other factors were also identified in this study, such as the support of senior management and the understanding of employees to deal with strategies focused on single consumer. The study was able to identify the most relevant information for the generation of electronic management strategies relationship with the consumer and identified the most relevant aspects of the critical success factors.

This report introduces ProGENitor; a system to empower individuals with career advice based on vast amounts of data. Specifically, it develops a machine learning algorithm that shows users how to efficiently reached specific career goals based upon the histories of other users. A reference implementation of this algorithm is presented, along with experimental results that show that it provides quality actionable intelligence to users. / text

Social media is one of the most disruptive developments of the past decade. The impact of this information revolution has been fundamental on our society. Information dissemination has never been cheaper and users are increasingly connected with each other. The line between content producers and consumers is blurred, leaving us with abundance of data produced in real-time by users around the world on multitude of topics. In this thesis we study techniques to aid an analyst in uncovering insights from this new media form which is modeled as a high volume social text stream. The aim is to develop practical algorithms with focus on the ability to scale, amenability to reliable operation, usability, and ease of implementation. Our work lies at the intersection of building large scale real world systems and developing theoretical foundation to support the same. We identify three key predicates to enable online methods for analysis of social data, namely : - Persistent Chatter Discovery to explore topics discussed over a period of time, - Cross-referencing Media Sources to initiate analysis using a document as the query, and - Contributor Understanding to create aggregate expertise and topic summaries of authors contributing online. The thesis defines each of the predicates in detail and covers proposed techniques, their practical applicability, and detailed experimental results to establish accuracy and scalability for each of the three predicates. We present BlogScope, the core data aggregation and management platform, developed as part of the thesis to enable implementation of the key predicates in real world setting. The system provides a web based user interface for searching social media conversations and analyzing the results in multitude of ways. BlogScope, and its modified versions, index tens to hundreds of billions of text documents while providing interactive query times. Specifically, BlogScope has been crawling 50 million active blogs with 3.25 billion blog posts. Same techniques have also been successfully tested on a Twitter stream of data, adding thousands of new Tweets every second and archiving over 30 billion documents. The social graph part of our database consists of 26 million Twitter user nodes with 17 billion follower edges. The BlogScope system has been used by over 10,000 unique visitors a day, and the commercial version of the system is used by thousands of enterprise clients globally. As social media continues to evolve at an exponential pace, there is a lot that still needs to be studied. The thesis concludes by outlining some of possible future research directions.

Social media is one of the most disruptive developments of the past decade. The impact of this information revolution has been fundamental on our society. Information dissemination has never been cheaper and users are increasingly connected with each other. The line between content producers and consumers is blurred, leaving us with abundance of data produced in real-time by users around the world on multitude of topics. In this thesis we study techniques to aid an analyst in uncovering insights from this new media form which is modeled as a high volume social text stream. The aim is to develop practical algorithms with focus on the ability to scale, amenability to reliable operation, usability, and ease of implementation. Our work lies at the intersection of building large scale real world systems and developing theoretical foundation to support the same. We identify three key predicates to enable online methods for analysis of social data, namely : - Persistent Chatter Discovery to explore topics discussed over a period of time, - Cross-referencing Media Sources to initiate analysis using a document as the query, and - Contributor Understanding to create aggregate expertise and topic summaries of authors contributing online. The thesis defines each of the predicates in detail and covers proposed techniques, their practical applicability, and detailed experimental results to establish accuracy and scalability for each of the three predicates. We present BlogScope, the core data aggregation and management platform, developed as part of the thesis to enable implementation of the key predicates in real world setting. The system provides a web based user interface for searching social media conversations and analyzing the results in multitude of ways. BlogScope, and its modified versions, index tens to hundreds of billions of text documents while providing interactive query times. Specifically, BlogScope has been crawling 50 million active blogs with 3.25 billion blog posts. Same techniques have also been successfully tested on a Twitter stream of data, adding thousands of new Tweets every second and archiving over 30 billion documents. The social graph part of our database consists of 26 million Twitter user nodes with 17 billion follower edges. The BlogScope system has been used by over 10,000 unique visitors a day, and the commercial version of the system is used by thousands of enterprise clients globally. As social media continues to evolve at an exponential pace, there is a lot that still needs to be studied. The thesis concludes by outlining some of possible future research directions.

This dissertation develops sophisticated data analytic methods to analyze structural loads on, and power generation of, wind turbines. Wind turbines, which convert the kinetic energy in wind into electrical power, are operated within stochastic environments. To account for the influence of environmental factors, we employ a conditional approach by modeling the expectation or distribution of response of interest, be it the structural load or power output, conditional on a set of environmental factors. Because of the different nature associated with the two types of responses, our methods also come in different forms, conducted through two studies. The first study presents a Bayesian parametric model for the purpose of estimating the extreme load on a wind turbine. The extreme load is the highest stress level that the turbine structure would experience during its service lifetime. A wind turbine should be designed to resist such a high load to avoid catastrophic structural failures. To assess the extreme load, turbine structural responses are evaluated by conducting field measurement campaigns or performing aeroelastic simulation studies. In general, data obtained in either case are not sufficient to represent various loading responses under all possible weather conditions. An appropriate extrapolation is necessary to characterize the structural loads in a turbine’s service life. This study devises a Bayesian spline method for this extrapolation purpose and applies the method to three sets of load response data to estimate the corresponding extreme loads at the roots of the turbine blades. In the second study, we propose an additive multivariate kernel method as a new power curve model, which is able to incorporate a variety of environmental factors in addition to merely the wind speed. In the wind industry, a power curve refers to the functional relationship between the power output generated by a wind turbine and the wind speed at the time of power generation. Power curves are used in practice for a number of important tasks including predicting wind power production and assessing a turbine’s energy production efficiency. Nevertheless, actual wind power data indicate that the power output is affected by more than just wind speed. Several other environmental factors, such as wind direction, air density, humidity, turbulence intensity, and wind shears, have potential impact. Yet, in industry practice, as well as in the literature, current power curve models primarily consider wind speed and, with comparatively less frequency, wind speed and direction. Our model provides, conditional on a given environmental condition, both the point estimation and density estimation of the power output. It is able to capture the nonlinear relationships between environmental factors and wind power output, as well as the high-order inter- action effects among some of the environmental factors. To illustrate the application of the new power curve model, we conduct case studies that demonstrate how the new method can help with quantifying the benefit of vortex generator installation, advising pitch control adjustment, and facilitating the diagnosis of faults.

Organisationer samlar oerhört mycket information som kan användas som stöd vid strategiskt beslutfattande. Det finns många olika verktyg som kan användas för att analysera informationen och skapa det beslutunderlaget som krävs för ett informationsbaserad strategiskt beslut. Verktygen förutsätter en rationell beslutsprocess där olika alternativ vägs och analyseras med resultatet att bästa möjliga beslut fattas. Business Analytics (BA) är ett paraply begrepp som innehåller olika verktyg och processer för att analysera och bearbeta information som bygger på den rationella beslutsprocessen. Företag kan delas in i två olika kategorier: de som fokuserar/säljer sina produkter till slutkonsumenter (B2C) och de som fokuserar/säljer sina produkter till företag (B2B). Litteraturen kring hur BA används som beslutstödssystem fokuserar på B2C organisationer som Amazon.com, Netflix eller Google, medan det finns väldigt lite skrivit kring hur B2B organisationer utnyttjar BA. B2B organisationer kännetecknas bland annat av att de har mycket färre kunder och producerar varor gärna ihop med sina kunder, till skillnad till B2C organisationer som kan ha enorma kundstockar. Det finns därför ett behov för att öka förståelsen för hur B2B organisationer utnyttjar BA som stöd i den strategiska beslutsprocessen. I detta arbete har ett B2B företag analyseras för att få en bild av hur den strategiska beslutsprocessen ser ut och hur BA används som ett verktyg vid beslutsfattande. I praktiken visar sig beslutsprocessen inte vara helt rationell utan mer som en blandning av otydliga och diffusa idéer och att beslutsunderlag mognar fram och utvecklas till en mer rationell process där BA kan användas för olika beräkningar och för att skapa beslutsunderlag. Strategiska beslutsprocessen initieras genom muntlig kommunikation ifrån samarbetspartners, kunder eller genom exempelvis möten och mässor. Det är inte förrän sent i processen som BA utnyttjas för att analysera information och då helst i form av lönsamhetskalkyler. En viktig faktor för att BA inte används tidigare i den strategiska beslutsprocessen är att organisationen normalt inte har tillgång till den information som ska analyseras förrän i ett senare skede i processen vilket gör att man inte kan ställa och utveckla frågor och idéer som kommer fram inom organisationen.