El gran volumen de datos que las técnicas de minería Web generan sobre espacios Web puede llegar a ser muy difícil de entender, provocando la necesidad de desarrollar nuevas técnicas que permitan generar conocimiento sobre esos datos con el fin de facilitar la toma de decisiones. Esta tesis explora la utilización de técnicas de InfoVis/VA para ayudar en la exploración de espacios Web. Más concretamente, presentamos el desarrollo de un prototipo muy flexible que ha sido utilizado para analizar tres tipos distintos de espacios Web con distintas metas informacionales: el análisis de la usabilidad de páginas Web, la evaluación del comportamiento de los estudiantes en entornos virtuales de aprendizaje y la exploración de la estructura de grandes conversaciones asíncronas existentes en foros online. Esta tesis pretende aceptar el reto propuesto por la comunidad de InfoVis/VA de llevar a cabo investigaciones en condiciones más reales, introduciendo los problemas relacionados con el análisis de los espacios Web ya mencionados, y explorando las ventajas de utilizar las visualizaciones proporcionadas por nuestra herramienta con usuarios reales. / The vast amount of data that Web mining techniques generate from Web spaces is difficult to understand, suggesting the need to develop new techniques to gather insight into them in order to assist in decision making processes. This dissertation explores the usage of InfoVis/VA techniques to assist in the exploration of Web spaces. More specifically, we present the development of a customisable prototype that has been used to analyse three different types of Web spaces with different information goals: the analysis of the usability of a website, the assessment of the students in virtual learning environments, and the exploration of the structure of large asynchronous conversations existing in online forums. Echoing the call of the Infovis/VA community for the need for more research into realistic circumstances, we introduce the problems of the analysis of such Web spaces, and further explore the benefits of using the visualisations provided by our system with real users. / El gran volum de dades que les tècniques de mineria Web proporcionen sobre els espais Web és generalment molt difícil dʼentendre, provocant la necessitat de desenvolupar noves tècniques que permetin generar coneixement sobre les dades de manera que facilitin la presa de decissions. Aquesta tesi explora la utilizació de tècniques dʼInfovis/VA per ajudar en lʼexploració dʼespais Web. Més concretament, presentem el desenvolupament dʼun prototipus molt flexible que hem utilitzat per analitzar tres tipus diferents dʼespais Web amb diferents objectius informacionals: lʼanèlisi de la usabilitat de pàgines Web, lʼavaluació del comportament dels estudiants en entorns virtuals dʼaprenentatge i lʼexploració de lʼestructura de grans converses asíncrones existents en fòrums online. Aquesta tesi pretén acceptar el repte proposat per la comunitat dʼInfoVis/VA de fer recerca en condicions més reals, introduint els problemes relacionats en lʼanàlisi dels espais Web ja esmentats, i explorant els avantatges dʼutilizar les visualitzacions proporcionades per la nostra eina amb usuaris reals.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 78-80).

abstract: Over the past forty years the nonprofit sector has experienced a steady rise in the professionalization of its employees and its operations. Some have argued that this trend is in large part a reaction to the requirements foisted upon the nonprofit sector through the passage of the Tax Reform Act of 1969. While some scholars have detailed a number of unintended consequences that have resulted from this trend toward professionalization, in general scholars and practitioners have accepted it as a necessary step along the path toward ensuring that service is administered in an accountable and responsible manner. I analyze the contemporary trend in professionalization of the nonprofit sector from a different angle--one which seeks to determine how the nonprofit sector came to problematize the nature of its service beginning in the early twentieth century, as well as the consequences of doing so, rather than reinforce the existing normative arguments. To this end, I employ an "analytics of government" from an ethical and political perspective which is informed by Michel Foucault's conception of genealogy, as well as his work on governing rationalities, in order to reveal the historical and political forces that contribute to the nonprofit sector's professionalization and that shape its current processes, institutions, and norms. I ultimately argue that these forces serve to reinforce a broad movement away from the charitable impulse that motivates individuals to engage in personal acts of compassion and toward a philanthropic enterprise by which knowledge is rationally applied toward reforming society rather than aiding individuals. This movement toward institutional philanthropy and away from individual charity supplants the needs of the individual with the needs of the organization. I then apply this analysis to propose an alternate governing model for the nonprofit sector--one that draws on Foucault's exploration of ancient writings on love, self-knowledge, and governance--in order to locate a space for the individual in nonprofit life. / Dissertation/Thesis / Ph.D. Public Administration 2011

O desenvolvimento das capacidades dinâmicas habilita a empresa à inovar de forma mais eficiente, e por conseguinte, melhorar seu desempenho. Esta tese apresenta um framework para mensuração do grau de desenvolvimento das capacidades dinâmicas da empresa. Através de técnicas de text mining uma bag of words específica para as capacidades dinâmicas é proposta, bem como, baseado na literatura é proposto um conjunto de rotinas para avaliar a operacionalização e desenvolvimento das capacidades dinâmicas. Para avaliação das capacidades dinâmicas, foram aplicadas técnicas de text mining utilizando como fonte de dados os relatórios anuais de catorze empresas aéreas. Através da aplicação piloto foi possível realizar um diagnóstico das empresas aéreas e do setor. O trabalho aborda uma lacuna da literatura das capacidades dinâmicas, ao propor um método quantitativo para sua mensuração, assim como, a proposição de uma bag of words específica para as capacidades dinâmicas. Em termos práticos, a proposição pode contribuir para a tomada de decisões estratégicas embasada em dados, possibilitando assim inovar com mais eficiência e melhorar desempenho da firma. / The development of dynamic capabilities enables the company to innovate more efficiently and therefore improves its performance. This thesis presents a framework for measuring the dynamic capabilities development. Text mining techniques were used to propose a specific bag of words for dynamic capabilities. Furthermore, based on the literature, a group of routines is proposed to evaluate the operationalization and development of dynamic capabilities. In order to evaluate the dynamic capabilities, text mining techniques were applied using the annual reports of fourteen airlines as the data source. Through this pilot application it was possible to carry out a diagnosis of the airlines and the sector as well. The thesis approaches a dynamic capabilities literature gap by proposing a quantitative method for its measurement, as well as, the proposition of a specific bag of words for dynamic capabilities. The proposition can contribute to strategic decision making based on data, allowing firms to innovate more efficiently and improve performance.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In this paper, we proposed a wearable technology model to control and monitor hypertension during pregnancy. We enhanced prior models by adding a series of health parameters that could potentially prevent and correct hypertension disorders in pregnancy. Our proposed model also emphasizes the application of real-time data analysis for the healthcare organization. In this process, we also assessed the current technologies and systems applications offered in the market. The model consists of four phases: 1. The health parameters of the patient are collected through a wearable device; 2. The data is received by a mobile application; 3. The data is stored in a cloud database; 4. The data is analyzed on real-time using a data analytics application. The model was validated and piloted in a public hospital in Lima, Peru. The preliminary results showed an increased-on number of controlled patients by 11% and a reduction of maternal deaths by 7%, among other relevant health factors that allowed healthcare providers to take corrective and preventive actions. / Revisión por pares

Given the evolution of Cloud Computing in recent years, users and clients adopting Cloud Computing for both personal and business needs have increased at an unprecedented scale. This has naturally led to the increased deployments and implementations of Cloud datacentres across the globe. As a consequence of this increasing adoption of Cloud Computing, Cloud datacentres are witnessed to be massive energy consumers and environmental polluters. Whilst the energy implications of Cloud datacentres are being addressed from various research perspectives, predicting the future trend and behaviours of workloads at the datacentres thereby reducing the active server resources is one particular dimension of green computing gaining the interests of researchers and Cloud providers. However, this includes various practical and analytical challenges imposed by the increased dynamism of Cloud systems. The behavioural characteristics of Cloud workloads and users are still not perfectly clear which restrains the reliability of the prediction accuracy of existing research works in this context. To this end, this thesis presents a comprehensive descriptive analytics of Cloud workload and user behaviours, uncovering the cause and energy related implications of Cloud Computing. Furthermore, the characteristics of Cloud workloads and users including latency levels, job heterogeneity, user dynamicity, straggling task behaviours, energy implications of stragglers, job execution and termination patterns and the inherent periodicity among Cloud workload and user behaviours have been empirically presented. Driven by descriptive analytics, a novel user behaviour forecasting framework has been developed, aimed at a tri-fold forecast of user behaviours including the session duration of users, anticipated number of submissions and the arrival trend of the incoming workloads. Furthermore, a novel resource optimisation framework has been proposed to avail the most optimum level of resources for executing jobs with reduced server energy expenditures and job terminations. This optimisation framework encompasses a resource estimation module to predict the anticipated resource consumption level for the arrived jobs and a classification module to classify tasks based on their resource intensiveness. Both the proposed frameworks have been verified theoretically and tested experimentally based on Google Cloud trace logs. Experimental analysis demonstrates the effectiveness of the proposed framework in terms of the achieved reliability of the forecast results and in reducing the server energy expenditures spent towards executing jobs at the datacentres.

The popularity of location empowered devices such as GPS enabled smart-phones has immensely amplified the use of location-based services in social networks. This happened by allowing users to share Geo-tagged contents such as current locations/check-ins with their social network friends. These location-aware social networks are called Location-based Social Networks (LBSN), and examples include Foursquare and Gowalla. The data of LBSNs are being used for providing different kinds of services such as the recommendation of locations, friends, activities, and media contents, and the prediction of user's locations. To provide such services, different queries are utilized that exploit activity/check-in data of users. Usually, LBSN data is divided into two parts, a social graph that encapsulates the friendships of users and an activity graph that maintains the visit history of users at locations. Such a data separation is scalable enough for processing queries that directly utilize friendship information and visit history of users. These queries are called user and activity analytic queries. The visits of users at locations create relationships between those locations. Such relationships can be built on different features such as common visitors, geographical distance, and mutual location categories between them. The process of analysing such relationships for optimizing location-based services is termed Location Analytics. In location analytics, we expose the subjective nature of locations that can further be used for applications in the domain of prediction of visitors, traffic management, route planning, and targeted marketing.In this thesis, we provide a general LBSN data model which can support storage and processing of queries required for different applications, called location analytics queries. The LBSN data model we introduce, segregates the LBSN data into three graphs: the social graph, the activity graph, and the location graph. The location graph maintains the interactions of locations among each other. We define primitive queries for each of these graphs. In order to process an advanced query, we express it as a combination of these primitive queries and process them on corresponding graphs in parallel. We further provide a distributed data processing framework called GeoSocial-GraphX (GSG). GSG implements the aforementioned LBSN data model for efficient and scalable processing of the queries. We further exploit the location graph for providing novel location analytics queries in the domain of influence maximization and visitor prediction. We introduce a notion of location influence. Such influence can capture the interactions of locations based on their visitors and can be used for propagation of information between them. The applications of such a query lie in the domain of outdoor marketing, and simulation of virus and news propagation. We also provide a unified system IMaxer that can evaluate and compare different information propagation mechanisms. We further exploit the subjective nature of locations by analysing the mobility behaviour of their visitors. We use such information to predict the individual visitors as well as the groups of visitors (cohorts) in future for those locations. The prediction of visitors can be used for better event planning, traffic management, targeted marketing, and ride-sharing services.In order to evaluate the proposed frameworks and approaches, we utilize data from four real-life LBSNs: Foursquare, Brightkite, Gowalla, and Wee Places. The detailed LBSN data mining and statistically significant experimental evaluation results show the effectiveness, efficiency, and scalability of our proposed methods. Our proposed approaches can be employed in real systems for providing life-care services. / Doctorat en Sciences de l'ingénieur et technologie / The portal is not showing my complete name. The name (my complete name), I want to have on the diploma is "Muhammad Aamir Saleem". Please correct this issue. / info:eu-repo/semantics/nonPublished

Operational support as an area of process mining aims to predict the temporal performance of individual cases and the overall business process. Although seasonal effects, delays and performance trends are well-known to exist for business processes, there is up until now no prediction model available that explicitly captures this. In this paper, we introduce time series Petri net models. These models integrate the control flow perspective of Petri nets with time series prediction. Our evaluation on the basis of our prototypical implementation demonstrates the merits of this model in terms of better accuracy in the presence of time series effects.

When creating and administering regulations, regulators have to demonstrate that regulations accomplish intended societal outcomes at costs that do not outweigh their benefits. While regulators have this responsibility as custodians of the regulatory ecosystem, they are also required to create and administer regulations transparently and impartially, addressing the needs and concerns of all stakeholders involved. This is in addition to regulators having to deal with various administrative bottlenecks, competing internal priorities, as well as financial and human resource limitations. Nonetheless, governments, regulated parties, citizens and interest groups can each express different views on the relevance and performance of a piece of regulation. These views range from too many regulations burdening business operations to perceptions that crises in society are the results of insufficient regulations. As such, regulators have to be innovative, employing methods that show that regulations are effective, and justify the introduction, evolution or repeal of regulations. The regulatory process has been the topic of various studies with several such studies exploring the use of information systems at the software level to confirm compliance with regulations and evaluate issues related to non-compliance. The rationale is that if information systems can improve operational functions in organizations, they can also help measure compliance. However, the research focus has been on enabling regulated parties to comply with regulations rather than on enabling regulators to assess or enforce compliance or show that regulations are effective. Regulators need to address concerns of too much regulations or too little regulations with data-driven evidence especially in this age of big data and artificial intelligence enhanced tools. A method that facilitates evidencebased decision-making using data for enacting, implementing and reviewing regulations is now inevitable. In response to the above challenges, this thesis explores the use of a goaloriented modelling method and a data analytics software, to create a method that enables monitoring, assessing and reporting on the effectiveness of regulations and regulatory initiatives. This Goal-oriented Regulatory Intelligence Method (GoRIM) provides an intelligent approach to regulatory management, as well as a feedback loop in the use of data from and within the regulatory ecosystem to create and administer regulations. To demonstrate its applicability, GoRIM was applied to three case studies involving regulators in three different real regulatory scenarios, and its feasibility and utility were evaluated. The results indicate that regulators found GoRIM promising in enabling them to show, with evidence, whether their regulations are effective.

Graphs are commonly selected as a model of scientific information: graphs can successfully represent imprecise, uncertain, noisy data; and graph theory has a well-developed mathematical apparatus forming a solid and sound foundation for graph research. Design and experimental confirmation of new, scalable, and practical analytics for massive graphs have been actively researched for decades. Our work concentrates on developing new accurate and efficient algorithms that calculate the most influential nodes and communities in an arbitrary graph. Our algorithms for graph decomposition into families of most influential communities compute influential communities faster and using smaller memory footprint than existing algorithms for the problem. Our algorithms solving the problem of influence maximization in large graphs use much smaller memory than the existing state-of-the-art algorithms while providing solutions with equal accuracy. Our main contribution is designing data structures and algorithms that drastically cut the memory footprint and scale up the computation of influential communities and nodes to massive modern graphs. The algorithms and their implementations can efficiently handle networks of billions of edges using a single consumer-grade machine. These claims are supported by extensive experiments on large real-world graphs of different types. / Graduate