Estudio de patrones de interacción entre los estudiantes y la Plataforma de Tele-Enseñanza en la UPM

Vivimos en una sociedad en la que la información ha adquirido una vital importancia. El uso de Internet y el desarrollo de nuevos sistemas de la información han generado un ferviente interés tanto de empresas como de instituciones en la búsqueda de nuevos patrones que les proporcione la clave del éxito. La Analítica de Negocio reúne un conjunto de herramientas, estrategias y técnicas orientadas a la explotación de la información con el objetivo de crear conocimiento útil dentro de un marco de trabajo y facilitar la optimización de los recursos tanto de empresas como de instituciones. El presente proyecto se enmarca en lo que se conoce como Gestión Educativa. Se aplicará una arquitectura y modelo de trabajo similar a lo que se ha venido haciendo en los últimos años en el entorno empresarial con la Inteligencia de Negocio. Con esta variante, se pretende mejorar la calidad de la enseñanza, agilizar las decisiones dentro de la institución académica, fortalecer las capacidades del cuerpo docente y en definitiva favorecer el aprendizaje del alumnado. Para lograr el objetivo se ha decidido seguir las etapas del Knowledge Discovery in Databases (KDD), una de las metodologías más conocidas dentro de la Inteligencia de Negocio, que describe el procedimiento que va desde la selección de la información y su carga en sistemas de almacenamiento, hasta la aplicación de técnicas de minería de datos para la obtención nuevo conocimiento. Los estudios se realizan a partir de la información de la activad de los usuarios dentro la plataforma de Tele-Enseñanza de la Universidad Politécnica de Madrid (Moodle). Se desarrollan trabajos de extracción y preprocesado de la base de datos en crudo y se aplican técnicas de minería de datos. En la aplicación de técnicas de minería de datos, uno de los factores más importantes a tener en cuenta es el tipo de información que se va a tratar. Por este motivo, se trabaja con la Minería de Datos Educativa, en inglés, Educational Data Mining (EDM) que consiste en la aplicación de técnicas de minería optimizadas para la información que se genera en entornos educativos. Dentro de las posibilidades que ofrece el EDM, se ha decidido centrar los estudios en lo que se conoce como analítica predictiva. El objetivo fundamental es conocer la influencia que tienen las interacciones alumno-plataforma en las calificaciones finales y descubrir nuevas reglas que describan comportamientos que faciliten al profesorado discriminar si un estudiante va a aprobar o suspender la asignatura, de tal forma que se puedan tomar medidas que mejoren su rendimiento. Toda la información tratada en el presente proyecto ha sido previamente anonimizada para evitar cualquier tipo de intromisión que atente contra la privacidad de los elementos participantes en el estudio. ABSTRACT. We live in a society dominated by data. The use of the Internet accompanied by developments in information systems has generated a sustained interest among companies and institutions to discover new patterns to succeed in their business ventures. Business Analytics (BA) combines tools, strategies and techniques focused on exploiting the available information, to optimize resources and create useful insight. The current project is framed under Educational Management. A Business Intelligence (BI) architecture and business models taught up to date will be applied with the aim to accelerate the decision-making in academic institutions, strengthen teacher´s skills and ultimately improve the quality of teaching and learning. The best way to achieve this is to follow the Knowledge Discovery in Databases (KDD), one of the best-known methodologies in B.I. This process describes data preparation, selection, and cleansing through to the application of purely Data Mining Techniques in order to incorporate prior knowledge on data sets and interpret accurate solutions from the observed results. The studies will be performed using the information extracted from the Universidad Politécnica de Madrid Learning Management System (LMS), Moodle. The stored data is based on the user-platform interaction. The raw data will be extracted and pre-processed and afterwards, Data Mining Techniques will be applied. One of the crucial factors in the application of Data Mining Techniques is the kind of information that will be processed. For this reason, a new Data Mining perspective will be taken, called Educational Data Mining (EDM). EDM consists of the application of Data Mining Techniques but optimized for the raw data generated by the educational environment. Within EDM, we have decided to drive our research on what is called Predictive Analysis. The main purpose is to understand the influence of the user-platform interactions in the final grades of students and discover new patterns that explain their behaviours. This could allow teachers to intervene ahead of a student passing or failing, in such a way an action could be taken to improve the student performance. All the information processed has been previously anonymized to avoid the invasion of privacy.

Novas tendências, o big data: deteção de fraudes e transações no comércio local: SAF-T com tecnologia big data

Dissertação apresentada à Escola Superior de Tecnologia do Instituto Politécnico de Castelo Branco para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Desenvolvimento de Software e Sistemas Interactivos, realizada sob a orientação científica da categoria profissional do orientador Doutor Eurico Ribeiro Lopes, do Instituto Politécnico de Castelo Branco.

Approximate processing of massive continuous quantile queries over high-speed data streams

Quantile computation has many applications including data mining and financial data analysis. It has been shown that an is an element of-approximate summary can be maintained so that, given a quantile query d (phi, is an element of), the data item at rank [phi N] may be approximately obtained within the rank error precision is an element of N over all N data items in a data stream or in a sliding window. However, scalable online processing of massive continuous quantile queries with different phi and is an element of poses a new challenge because the summary is continuously updated with new arrivals of data items. In this paper, first we aim to dramatically reduce the number of distinct query results by grouping a set of different queries into a cluster so that they can be processed virtually as a single query while the precision requirements from users can be retained. Second, we aim to minimize the total query processing costs. Efficient algorithms are developed to minimize the total number of times for reprocessing clusters and to produce the minimum number of clusters, respectively. The techniques are extended to maintain near-optimal clustering when queries are registered and removed in an arbitrary fashion against whole data streams or sliding windows. In addition to theoretical analysis, our performance study indicates that the proposed techniques are indeed scalable with respect to the number of input queries as well as the number of items and the item arrival rate in a data stream.

Pragmatic development of service based real-time change data capture

This thesis makes a contribution to the Change Data Capture (CDC) field by providing an empirical evaluation on the performance of CDC architectures in the context of realtime data warehousing. CDC is a mechanism for providing data warehouse architectures with fresh data from Online Transaction Processing (OLTP) databases. There are two types of CDC architectures, pull architectures and push architectures. There is exiguous data on the performance of CDC architectures in a real-time environment. Performance data is required to determine the real-time viability of the two architectures. We propose that push CDC architectures are optimal for real-time CDC. However, push CDC architectures are seldom implemented because they are highly intrusive towards existing systems and arduous to maintain. As part of our contribution, we pragmatically develop a service based push CDC solution, which addresses the issues of intrusiveness and maintainability. Our solution uses Data Access Services (DAS) to decouple CDC logic from the applications. A requirement for the DAS is to place minimal overhead on a transaction in an OLTP environment. We synthesize DAS literature and pragmatically develop DAS that eciently execute transactions in an OLTP environment. Essentially we develop effeicient RESTful DAS, which expose Transactions As A Resource (TAAR). We evaluate the TAAR solution and three pull CDC mechanisms in a real-time environment, using the industry recognised TPC-C benchmark. The optimal CDC mechanism in a real-time environment, will capture change data with minimal latency and will have a negligible affect on the database's transactional throughput. Capture latency is the time it takes a CDC mechanism to capture a data change that has been applied to an OLTP database. A standard definition for capture latency and how to measure it does not exist in the field. We create this definition and extend the TPC-C benchmark to make the capture latency measurement. The results from our evaluation show that pull CDC is capable of real-time CDC at low levels of user concurrency. However, as the level of user concurrency scales upwards, pull CDC has a significant impact on the database's transaction rate, which affirms the theory that pull CDC architectures are not viable in a real-time architecture. TAAR CDC on the other hand is capable of real-time CDC, and places a minimal overhead on the transaction rate, although this performance is at the expense of CPU resources.

An online assessment and development tool for business incubators:optimising business-incubation management practice, performance and impact, Project Report

This project is focused on exchanging knowledge between ABS, UKBI and managers of business incubators in the UK. The project relates to exploitation of extant knowledge-base on assessing and improving business incubation management practice and performance and builds on two earlier studies. It addresses a pressing need for assessing and benchmarking business incubation input, process and outcome performance and highlighting best practice. The overarching aim of this project was to obtain proof-of-concept for a business incubation performance assessment and benchmarking online tool, fine-tune it and put it in use by nurturing a community of business incubation management practice, aligned by the resultant tool. The purpose was to offer an appropriate set of measures, in areas identified by relevant research on business incubation performance management and impact as critical, against which: 1.The input and process performance of business incubation management practice can be assessed and benchmarked within the auspices of a community of incubator managers concerned with best practice 2.The outcome performance and impact of business incubators can be assessed longitudinally. As such, the developed online assessment framework is geared towards the needs of researchers, policy makers and practitioners concerned with business incubation performance, added value and impact.

A retrospective-longitudinal examination of the relationship between apportionment of seat time in community-college algebra courses and student academic performance

During the past decade, there has been a dramatic increase by postsecondary institutions in providing academic programs and course offerings in a multitude of formats and venues (Biemiller, 2009; Kucsera & Zimmaro, 2010; Lang, 2009; Mangan, 2008). Strategies pertaining to reapportionment of course-delivery seat time have been a major facet of these institutional initiatives; most notably, within many open-door 2-year colleges. Often, these enrollment-management decisions are driven by the desire to increase market-share, optimize the usage of finite facility capacity, and contain costs, especially during these economically turbulent times. So, while enrollments have surged to the point where nearly one in three 18-to-24 year-old U.S. undergraduates are community college students (Pew Research Center, 2009), graduation rates, on average, still remain distressingly low (Complete College America, 2011). Among the learning-theory constructs related to seat-time reapportionment efforts is the cognitive phenomenon commonly referred to as the spacing effect, the degree to which learning is enhanced by a series of shorter, separated sessions as opposed to fewer, more massed episodes. This ex post facto study explored whether seat time in a postsecondary developmental-level algebra course is significantly related to: course success; course-enrollment persistence; and, longitudinally, the time to successfully complete a general-education-level mathematics course. Hierarchical logistic regression and discrete-time survival analysis were used to perform a multi-level, multivariable analysis of a student cohort (N = 3,284) enrolled at a large, multi-campus, urban community college. The subjects were retrospectively tracked over a 2-year longitudinal period. The study found that students in long seat-time classes tended to withdraw earlier and more often than did their peers in short seat-time classes (p < .05). Additionally, a model comprised of nine statistically significant covariates (all with p-values less than .01) was constructed. However, no longitudinal seat-time group differences were detected nor was there sufficient statistical evidence to conclude that seat time was predictive of developmental-level course success. A principal aim of this study was to demonstrate—to educational leaders, researchers, and institutional-research/business-intelligence professionals—the advantages and computational practicability of survival analysis, an underused but more powerful way to investigate changes in students over time.

Informationsförvaltning inom en stor organisation : En fallstudie på Trafikverket

Denna studie syftar till att undersöka hur en stor organisation arbetar med förvaltning av information genom att undersöka dess nuvarande informationsförvaltning, samt undersöka eventuella förslag till framtida informationsförvaltning. Vidare syftar studien också till att undersöka hur en stor organisation kan etablera en tydlig styrning, samverkan, hantering och ansvars- och rollfördelning kring informationsförvaltning. Denna studie är kvalitativ, där datainsamlingen sker genom dokumentstudier och intervjuer. Studien bedrivs med abduktion och är en normativ fallstudie då studiens mål är att ge vägledning och föreslå åtgärder till det fall som uppdragsgivaren har bett mig att studera. Fallet i denna studie är ett typiskt fall, då studiens resultat kan vara i intresse för fler än studiens uppdragsgivare, exempelvis organisationer med liknande informationsmiljö. För att samla teori till studien så har jag genomfört litteraturstudier om ämnen som är relevanta för studiens syfte: Informationsförvaltning, Business Intelligence, Data Warehouse och dess arkitektur, samt Business Intelligence Competency Center. Denna studie bidrar med praktiskt kunskapsbidrag, då studien ger svar på praktiska problem. Uppdragsgivaren har haft praktiska problem i och med en icke fungerade informationsförvaltning, och denna studie har bidragit med förslag på framtida informationsförvaltning. Förslaget på framtida informationsförvaltning involverar ett centraliserat Data Warehouse, samt utvecklingen utav en verksamhet som hanterar informationsförvaltning och styrningen kring informationsförvaltningen inom hela organisationen.

Decisions, decisions everywhere (in the open data era).

Abstract: Decision support systems have been widely used for years in companies to gain insights from internal data, thus making successful decisions. Lately, thanks to the increasing availability of open data, these systems are also integrating open data to enrich decision making process with external data. On the other hand, within an open-data scenario, decision support systems can be also useful to decide which data should be opened, not only by considering technical or legal constraints, but other requirements, such as "reusing potential" of data. In this talk, we focus on both issues: (i) open data for decision making, and (ii) decision making for opening data. We will first briefly comment some research problems regarding using open data for decision making. Then, we will give an outline of a novel decision-making approach (based on how open data is being actually used in open-source projects hosted in Github) for supporting open data publication. Bio of the speaker: Jose-Norberto Mazón holds a PhD from the University of Alicante (Spain). He is head of the "Cátedra Telefónica" on Big Data and coordinator of the Computing degree at the University of Alicante. He is also member of the WaKe research group at the University of Alicante. His research work focuses on open data management, data integration and business intelligence within "big data" scenarios, and their application to the tourism domain (smart tourism destinations). He has published his research in international journals, such as Decision Support Systems, Information Sciences, Data & Knowledge Engineering or ACM Transaction on the Web. Finally, he is involved in the open data project in the University of Alicante, including its open data portal at http://datos.ua.es

Context-awareness privacy in data communications

Internet users consume online targeted advertising based on information collected about them and voluntarily share personal information in social networks. Sensor information and data from smart-phones is collected and used by applications, sometimes in unclear ways. As it happens today with smartphones, in the near future sensors will be shipped in all types of connected devices, enabling ubiquitous information gathering from the physical environment, enabling the vision of Ambient Intelligence. The value of gathered data, if not obvious, can be harnessed through data mining techniques and put to use by enabling personalized and tailored services as well as business intelligence practices, fueling the digital economy. However, the ever-expanding information gathering and use undermines the privacy conceptions of the past. Natural social practices of managing privacy in daily relations are overridden by socially-awkward communication tools, service providers struggle with security issues resulting in harmful data leaks, governments use mass surveillance techniques, the incentives of the digital economy threaten consumer privacy, and the advancement of consumergrade data-gathering technology enables new inter-personal abuses. A wide range of fields attempts to address technology-related privacy problems, however they vary immensely in terms of assumptions, scope and approach. Privacy of future use cases is typically handled vertically, instead of building upon previous work that can be re-contextualized, while current privacy problems are typically addressed per type in a more focused way. Because significant effort was required to make sense of the relations and structure of privacy-related work, this thesis attempts to transmit a structured view of it. It is multi-disciplinary - from cryptography to economics, including distributed systems and information theory - and addresses privacy issues of different natures. As existing work is framed and discussed, the contributions to the state-of-theart done in the scope of this thesis are presented. The contributions add to five distinct areas: 1) identity in distributed systems; 2) future context-aware services; 3) event-based context management; 4) low-latency information flow control; 5) high-dimensional dataset anonymity. Finally, having laid out such landscape of the privacy-preserving work, the current and future privacy challenges are discussed, considering not only technical but also socio-economic perspectives.

Hotel Vila Galé Lagos

Dissertação de Mestrado, Direção e Gestão Hoteleira, Escola Superior de Gestão, Hotelaria e Turismo, Universidade do Algarve, 2016

Profitability performance assessment in the Australian construction industry: a global relational two-stage DEA method

Effective performance measurement drives performance and supports the development of construction. Only minimal literature measuring construction performance, efficiency and effectiveness simultaneously can be identified. A global relational two-stage data envelopment analysis (DEA) method is here proposed in order to produce effective and informative performance results. A relational two-stage DEA method systematically measures overall efficiency for a whole construction system and also yields scores for the individual stages of construction. The DEA results can be directly compared through global benchmark technology. The Australian construction industry is employed in order to implement the new method, in which profitability performance as a vital indicator of business survival, and its two dimensions of efficiency and effectiveness, are measured. The construction profitability performance and efficiency measures obtained provide evidence of underperformance and a slight imbalance in Australia between 1991 and 2012, while the measures obtained for the effectiveness factor indicate better achievement. The approach here developed promotes progress in modelling two-stage performance measurement and it can be replicated worldwide by construction projects, organizations or industries in order to quantify their performance, identify internal inefficiency components and recognize competitive advantages for promoting sustainable development.

V for variety: Lessons learned from complex smart cities data harmonization and integration

With emerging trends for Internet of Things (IoT) and Smart Cities, complex data transformation, aggregation and visualization problems are becoming increasingly common. These tasks support improved business intelligence, analytics and enduser access to data. However, in most cases developers of these tasks are presented with challenging problems including noisy data, diverse data formats, data modeling and increasing demand for sophisticated visualization support. This paper describes our experiences with just such problems in the context of Household Travel Surveys data integration and harmonization. We describe a common approach for addressing these harmonizations. We then discuss a set of lessons that we have learned from our experience that we hope will be useful for others embarking on similar problems. We also identify several key directions and needs for future research and practical support in this area.

La cultura y el liderazgo en la estrategia de crecimiento por exportaciones del modelo de desarrollo japonés (1960-1980)

Normalmente el desarrollo de un país se ha explicado desde una perspectiva tradicional en términos de su crecimiento económico, teniendo en cuenta indicadores macroeconómicos como el PIB, la inflación y el desempleo. Poca atención se le ha puesto a la importancia que para el desarrollo de un país representan el capital humano y el proceso de liderazgo. Debido a lo anterior, mediante este estudio de caso, se pretende entender el éxito de la estrategia de crecimiento por exportaciones de Japón entre los años 1960-1980 teniendo en cuenta estos aspectos. Así, se busca sustentar que la incorporación de un tipo de liderazgo transformacional- transaccional y los elementos propios de su cultura como el confucianismo y el budismo, le imprimieron una perspectiva no economicista al éxito del modelo de desarrollo como parte de la triada empresa-estado-universidad. Lo anterior se realizará partiendo de un análisis cualitativo y con un enfoque en la economía política internacional y en el liderazgo. Este último estudiado desde las disciplinas de la administración, la sociología y la psicología

Sistema de referenciação geográfica

Nos dias de hoje o acesso à informação por parte das empresas é vital para o bom desempenho das suas funções. As empresas de telecomunicações não fogem à regra, a sua posição no mercado está dependente das decisões que são tomadas com base na avaliação dessa informação. Para suportar os processos de apoio à decisão é coerente recorrer-se a Data Warehouses que permitem integrar informação de diversas fontes, verificando a sua qualidade, actualização e coerência, organizando-a para um fácil acesso e consulta de vários pontos de vista. Numa empresa de telecomunicações móvel, um Data Mart geográfico baseado na informação de tráfego da companhia que pode identificar as localizações preferenciais dos utilizadores na rede é muito importante porque fornece indicadores muito úteis para o departamento de marketing e negócio da empresa de maneira a que se saiba onde e como actuar para permitir que esta se desenvolva e ganhe vantagem no mercado. ABSTRACT: Today the access to information by enterprises is vital for the company’s performance. Telecommunications companies are no exception. Their position in the market is dependent on the decisions that are taken based on the evaluation of such information. To support the decision making process Data Warehouse is today an extremely useful tool; it integrates information from different sources, checking on its validity, quality and update, coherence, organizing it for an easy access and search from various perspectives. ln a mobile telecommunications company a geographical Data Mart-based traffic information that can identify the preferential locations of users on the network is very important It provides useful indicators to the Department of Marketing and Business there by allowing you to know where and how to act and boosting the development of the company.