Data Week UPC 2021 día 1 / Data WeeK UPC es un evento anual organizado por las Facultades de Negocios e Ingeniería, con el propósito de reunir a investigadores y expertos en la gestión empresarial para reflexionar acerca del papel de la Ciencia de Datos en la generación de valor en las organizaciones. Nueve expositores de distintas instituciones se unirán a las 4 fechas del Data Week 2021 este 23, 25, 26 y 27 de noviembre, para reflexionar acerca de los retos en el proceso de la transformación de datos para la toma de decisiones. No se pierdan la oportunidad de participar en este espacio en el que discutiremos las principales tendencias en cuanto a la aplicación de la ciencia de datos en la gestión empresarial. 7:00 PM BUSINESS ANALYTICS: DEL ROCKET SCIENCE A UNA SOLUCIÓN DE NEGOCIOS El análisis correcto de los datos y el entendimiento de sus patrones tienen el potencial de aportar en la competitividad de una organización. En esta charla se analizarán algunas de las estrategias que permiten orientar los negocios hacia una cultura Data Driven. 8:00 PM TRANSFORMACIÓN DIGITAL La definición de una estrategia digital y el adecuado análisis y explotación de datos corporativos representan grandes retos para las organizaciones, en esta charla se abordará esta problemática y su relación con la transformación digital.

Proporciona los pasos y procedimientos para acceder al recurso Kopernio.

Proporciona los pasos y procedimientos para acceder al recurso Web of Science.

Memoria para optar al título de Ingeniero Civil Industrial / La investigación en el campo de la educación está avanzando hacía el uso de la data educacional disponible, de distintas fuentes como lo son los sistemas de gestión del aprendizaje, con diversas herramientas que comprenden la analítica del aprendizaje (LA, por sus siglas en inglés). Con el objetivo de incorporar esta data para darle un uso con modelos de recomendación, para sugerir al alumno que acciones tomar considerando su metodología y uso de la plataforma de gestión de aprendizaje. Se emplean diversas herramientas, particularmente regresiones para estimar las acciones de mayor impacto en su desempeño.

While it is evident that network services continue to play an ever-increasing role in our daily lives, it is less evident that our information infrastructure requires a concerted, well-conceived, and fastidiously executed strategy to remain viable. Government agencies, Non-Governmental Organizations (\NGOs"), and private organizations are all targets for malicious online activity. Security has deservedly become a serious focus for organizations that seek to assume a more proactive posture; in order to deal with the many facets of securing their infrastructure. At the same time, the discipline of data science has rapidly grown into a prominent role, as once purely theoretical machine learning algorithms have become practical for implementation. This is especially noteworthy, as principles that now fall neatly into the field of data science has been contemplated for quite some time, and as much as over two hundred years ago. Visionaries like Thomas Bayes [18], Andrey Andreyevich Markov [65], Frank Rosenblatt [88], and so many others made incredible contributions to the field long before the impact of Moore's law [92] would make such theoretical work commonplace for practical use; giving rise to what has come to be known as "Data Science". / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

The emerging global middle class, which is expected to double by 2050 desires more walkable, liveable neighbourhoods, and as distances between work and other amenities increases, cities are becoming less monocentric and becoming more polycentric. African cities could be described as walking cities, based on the number of people that walk to their destinations as opposed to other means of mobility but are often not walkable. Walking is by far the most popular form of transportation in Africa’s rapidly urbanising cities, although it is not often by choice rather a necessity. Facilitating this primary mode, while curbing the growth of less sustainable mobility uses requires special attention for the safety and convenience of walking in view of a Global South context. In this regard, to further promote walking as a sustainable mobility option, there is a need to assess the current state of its supporting infrastructure and begin giving it higher priority, focus and emphasis. Mobile phones have emerged as a useful alternative tool to collect this data and audit the state of walkability in cities. They eliminate the inaccuracies and inefficiencies of human memories because smartphone sensors such as GPS provides information with accuracies within 5m, providing superior accuracy and precision compared to other traditional methods. The data is also spatial in nature, allowing for a range of possible applications and use cases. Traditional inventory approaches in walkability often only revealed the perceived walkability and accessibility for only a subset of journeys. Crowdsourcing the perceived walkability and accessibility of points of interest in African cities could address this, albeit aspects such as ease-of-use and road safety should also be considered. A tool that crowdsources individual pedestrian experiences; availability and state of pedestrian infrastructure and amenities, using state-of-the-art smartphone technology, would over time also result in complete surveys of the walking environment provided such a tool is popular and safe. This research will illustrate how mobile phone applications currently in the market can be improved to offer more functionality that factors in multiple sensory modalities for enhanced visual appeal, ease of use, and aesthetics. The overarching aim of this research is, therefore, to develop the framework for and test a pilot-version mobile phone-based data collection tool that incorporates emerging technologies in collecting data on walkability. This research project will assess the effectiveness of the mobile application and test the technical capabilities of the system to experience how it operates within an existing infrastructure. It will continue to investigate the use of mobile phone technology in the collection of user perceptions of walkability, and the limitations of current transportation-based mobile applications, with the aim of developing an application that is an improvement to current offerings in the market. The prototype application will be tested and later piloted in different locations around the globe. Past studies are primarily focused on the development of transport-based mobile phone applications with basic features and limited functionality. Although limited progress has been made in integrating emerging advanced technologies such as Augmented Reality (AR), Machine Learning (ML), Big Data analytics, amongst others into mobile phone applications; what is missing from these past examples is a comprehensive and structured application in the transportation sphere. In turn, the full research will offer a broader understanding of the iii information gathered from these smart devices, and how that large volume of varied data can be better and more quickly interpreted to discover trends, patterns, and aid in decision making and planning. This research project attempts to fill this gap and also bring new insights, thus promote the research field of transportation data collection audits, with particular emphasis on walkability audits. In this regard, this research seeks to provide insights into how such a tool could be applied in assessing and promoting walkability as a sustainable and equitable mobility option. In order to get policy-makers, analysts, and practitioners in urban transport planning and provision in cities to pay closer attention to making better, more walkable places, appealing to them from an efficiency and business perspective is vital. This crowdsourced data is of great interest to industry practitioners, local governments and research communities as Big Data, and to urban communities and civil society as an input in their advocacy activities. The general findings from the results of this research show clear evidence that transport-based mobile phone applications currently available in the market are increasingly getting outdated and are not keeping up with new and emerging technologies and innovations. It is also evident from the results that mobile smartphones have revolutionised the collection of transport-related information hence the need for new initiatives to help take advantage of this emerging opportunity. The implications of these findings are that more attention needs to be paid to this niche going forward. This research project recommends that more studies, particularly on what technologies and functionalities can realistically be incorporated into mobile phone applications in the near future be done as well as on improving the hardware specifications of mobile phone devices to facilitate and support these emerging technologies whilst keeping the cost of mobile devices as low as possible.

Rapid growth of Big Data and Internet of Things (IoT) provides promising potentials to the advancements of methods and applications in increasing public awareness on climate change. The fundamental principle behind this method is to provide quantifiable calculation approach on several major factors that affect climate change, where one of the most well-known factors is the Greenhouse Gases (GHG) with CO2, methane, and nitrous oxide as major contributors. By utilizing Big Data and IoT, an approximate release of GHG can be calculated and embedded inside common household devices such as thermostats, water/heat/electricity/gas meter. An example is the CO2 released by a cubic of water. By using reverse calculation, an approximate CO2 release can be sequentially retrieved as follows: (1) water meter measures consumption, (2) calculate hp and kWh of pump used to supply one m3 of water, (3) calculate the amount of fossil fuel needed to produce one kWh, and (4) calculate CO2 released to the atmosphere from burning of fossil fuel per metric tons/barrel. Such analytical approaches are then embedded on household devices by providing updated information on GHG produced by hourly/daily/weekly/monthly energy usage, hence educating the public and increasing their awareness of climate change. This approach can be developed to provide an alarm of percentage of GHG released to the atmosphere by the excessive use of electricity/water/gas. Further actions in order to influence socio-economic function can later be established such as by establishing a rewards program by the government for people who can successfully manage their GHG emission.

OBJECTIVE: The purpose of this study is to determine if there are any acoustic variables that can determine compromised lung function in patients with asthma and COPD. METHODS: This study involved using mobile and wearable technology to record voice and respiratory changes during various speaking and breathing tasks before and after administration of albuterol. Collaborators at Samsung Research America, Inc. used algorithms to measure pause time, pause frequency, respiratory rate, and inhale:exhale ratio. These variables were correlated with spirometry values before and after albuterol to assess clinical significance. RESULTS: We identified several acoustic markers that significantly correlate with lung function in patients with asthma and COPD. In particular, we found that the ratio of the one-second forced expiratory volume to forced vital capacity (FEV1/FVC) after administration of albuterol significantly correlated with the inhale:exhale ratio in asthma patients during the tidal breathing task. The post-albuterol FEV1/FVC significantly correlated with the inhale:exhale ratio in COPD patients during the supine breathing task. The pre-albuterol FVC significantly correlated with the pause frequency in asthma patients during the scripted speech task. CONCLUSION: The results in this study indicate that pause frequency and inhale:exhale ratio may be important biomarkers for identifying a respiratory illness, such as asthma and COPD. More research needs to be done using digital health to monitor disease symptoms with a larger sample size.

Many students do not finish their introductory programming courses in higher education and it is difficult to identify why. While this thesis doesn’t concern itself with investigating drop outs, this fact motivated the authors to research if there was a correlation between problematic student work patterns and the students' success in the course.The aim of this thesis is to identify problematic student work patterns. To do this, we conducted a case study on the introductory programming course 1DV025 at Linnaeus University, focusing on quantitative data. This data was collected by developing an application which fetches student data from the GitLab repository of the course. After conducting statistical testing on the data, the results were analyzed in order to determine distinguishing traits that can be an indication of problematic student work patterns.The analysis uncovered that these do exist for some students that fail their examination assignments. Hopefully, the conclusions of this report can help teachers to recognize problematic student work patterns and apply preemptive actions accordingly.

There are more and more people watching video streams over the Internet, and this has led to an increase in companies that compete for viewers. To improve the users experience, these companies can measure how their services are performing. The aim of this thesis was to recommend a way to measure the quality of service for a real time video streaming service. Three methods were presented; to buy the information from a content delivery network, extend existing analytics software or build a custom solution using packet sniffing. It was decided to extend existing analytics software. An evaluation was made on which software to extend. Four solutions were compared: Google Analytics, Mixpanel, Ooyala IQ and Piwik. The comparison was made using the analytic hierarchy process, comparing each alternative in their performance in criteria such as API maturity, flexibility, visualization and support. The recommended software to extend when building a real time video streaming service is Ooyala IQ which excel at flexibility and is easy to implement into existing solutions. It also had great capacity, offering no limit on how many events it can track per month, and finally it offers great dedicated support via telephone or email. / Det finns fler och fler personer som tittar på video strömmar på Internet, detta har lett till att nya företag har startats som konkurerar om tittare. För att förbättra kundupplevelsen kan man mäta hur tjänsten presterar. Målet med examensarbetet var att rekommendera hur man kan mäta tjänstekvalite för en realtidsvideoströmningstjänst. Tre olika lösningsförslag presenterades; att köpa informationen från en content delivery network, att bygga vidare på tillgängliga analytisk mjukvara eller att bygga ett eget paketsniffarprogram. Det bestämdes att bygga vidare på tillgänglig analytisk mjukvara. Fyra olika mjukvara jämfördes: Google Analytics, Mixpanel, Ooyala IQ och Piwik. Jämförelsen gjordes med hjälp av analytical hierarchy process, de olika alternativen jämfördes med avseende på: hur moget API:et var, flexibilitet, visualiseringen av data och support. Rekommendationen är att använda sig av Ooyala IQ som utmärker sig med avseende på flexibilitet, det var enkelt att använda deras API i sin egen lösning, det fanns ingen gräns på hur många händelser man kunde lagra per månad, och slutligen så fanns det dedikerad supportpersonal att nå via telefon eller email.