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1	Dátová architektúra a dátové sklady v bankovníctve / Data architecture and data marts in banks Juhás, Michal January 2008 (has links) In this diploma thesis I analyze data architecture and data marts in a bank area. Firstly I analyze different theoretical approaches to build the data warehouse. Consequently I specialize in operational and analytical data marts -- I analyze their business value, differences and location at the warehouse. In the third chapter I analyze reasons that have led to implement Asset Liability Management data mart. In the following chapter I analyze a life-cycle of this data mart not only from the technical point of view, but also from the project management perspective. The main benefit from this thesis is in the analysis of these aspects of ALM life cycle.
2	Architecture and development of BI solution on MS Dynamics AX 2012 platform / Architektúra a vývoj BI riešenia na platforme MS Dynamics AX 2012 Eperješi, Vladimír January 2014 (has links) This thesis deals with the analysis and design of business intelligence solution for the needs of reporting for Microsoft Dynamics AX 2012 platform. Solution includes development guidelines for possible development in future. In the first part of the thesis there is introduced ERP system MS Dynamics AX 2012 which serves as a data source for reports. The following sections analyze the data sources and the need for enterprise reporting. Significant part of thesis is the analysis and design of individual basic dimensions and indicators together with the proposal of data flows and architecture solution. In chapters 6-9 are described best practices for creating data warehouse through all the layers of development. In chapter 10 are unified guidelines for the development of different parts of solution such as dimensional OLAP cubes for case of additional customer requirements.
3	Web application development with .NET : 3-tier architecture Dhali, Salle January 2012 (has links) The reason for performing this project work is to develop a Web application for the Student Union of Mid Sweden University applying the modern and comprehensive Microsoft .NET framework platform architecture. At present, the existing web application is divided into several modules which are built of server‐side scripting language technique and an open source database. The customer would like to develop the entire web applications using the Microsoft development tools and technologies in order to determine the possible benefit which could be obtained in terms of cost, maintenance, flexibility and the security perspective issues and also in terms of user friendly interactions options for all the involving partners in an effective way. The primary aim for the project is to start building a bookstore module for the Students Union that is responsible for selling literature to the students at the University. The module will also be integrated into a database system into which an administrator, a member of staff working in the Student Union, will be able to add a new book when it arrives and also update or delete if necessary later on. In addition to this module application all the book’s details belong to a certain category viewable to the students. The other part of this project work is aiming at finding a pattern similar to the bookstore module in which ordinary users can authenticate them towards a database and be able to add their curriculum vitae data entry and update it at a later stage as required. Human‐computer interaction ASP.NET .Net C# SQL ADO.NET N‐ tier distributed data architecture Computer and systems science Data- och systemvetenskap
4	Predicción de la demanda para un general sales service agent (GSSA) mediante regresión lineal simple / Demand forecasting for a general sales service agent through simple linear regression Rojas García, Freddy Wiliam 09 December 2020 (has links) Pacific Feeder Services (PFS) es un agente general de venta de espacios aéreos de distintas aerolíneas; por ejemplo, Korean Air, Aeroméxico, Alitalia, Aerolíneas Argentinas y Gol. Estas aerolíneas no cuentan con infraestructura propia en el Perú, de modo que PFS actúa como representante de estas aerolíneas ante sus clientes. En el presente trabajo de investigación se utilizará la metodología iterativa de la ciencia de datos para abordar el problema relacionado a la demanda, puesto que esta es incierta en algunos meses del año. Para ello, se plantea la siguiente hipótesis: ¿Será una regresión lineal simple el modelo adecuado para realizar el pronóstico de los volúmenes de la demanda que tendrá PFS en los próximos meses? El objetivo por alcanzar será proyectar la demanda mediante una regresión lineal simple, para lo cual se está tomando como base los datos de los kilos exportados por PFS en el año 2019. Asimismo, el presente trabajo de investigación académico presenta una arquitectura de datos funcional y una arquitectura de datos tecnológica que da soporte al modelo de regresión lineal simple. La primera explica cuáles son los insumos, almacenamiento y consumo que se requieren para implementar el mencionado modelo, mientras que la segunda expone las herramientas del modelo. Finalmente, el trabajo acaba con las conclusiones y recomendaciones asociadas a la correcta implementación del modelo de regresión lineal simple en el caso específico de PFS. / Pacific Feeder Services (PFS) is a general sales service agent (GSSA) whose main duty is to commercialize air freight capacity of different airlines; for example, Korean Air, Aeroméxico, Alitalia, Aerolineas Argentinas and Gol. These airlines do not have their own infrastructure in the country, so PFS acts as a representative of these airlines to their customers. In this research paper, the iterative methodology of data science will be used to address the problem related to demand, inasmuch as this is uncertain in some months of the year. To do this, the following hypothesis is proposed: Will a simple linear regression be the appropriate model to forecast the volumes of demand that PFS will have in the coming months? The objective to be achieved will be to project the demand through a simple linear regression, for which the data of the kilos exported by PFS in 2019 is being taken as a basis. Likewise, this academic research paper presents a functional data architecture and a technological data architecture that supports the simple linear regression model. The first explains what the inputs, storage and consumption required to implement the mentioned model are, while the second exposes the tools of the model. Finally, the research paper ends with the conclusions and recommendations associated with the correct implementation of the simple linear regression model in the specific case of PFS. / Trabajo de investigación Modelado de datos Regresión lineal simple Arquitectura de datos Data modeling Simple linear regression Data architecture
5	Determinación de variables que impiden a los alumnos inscribirse en el Programa Arizona - UPC / Determining variables that prevent students from enrolling in the Arizona Program - UPC Marquina Zambrano, Estefania Nataly, Matos Miranda, Rodrigo Omar, Mayta Villegas, Jiovanny Edgar, Miranda Inoñan, Cristhian Anderson, Valdiviezo Villafuerte, Wilmer Oswaldo 15 July 2021 (has links) En el siguiente trabajo de investigación se utilizó la metodología de Ciencia de datos para buscar posibles soluciones al problema que tiene el programa de Arizona de la Universidad de Ciencias Aplicadas (UPC) ya que no se logra llegar a la meta de alumnos inscritos y admitidos a pesar de presentar grandes beneficios en el aspecto profesional y educativo. Se analizó la data histórica de los años 2019, 2020 y 2021 la cual fue recolectada directamente de la universidad y se estableció el enfoque prescriptivo. Se identificaron las variables más relevantes de la base de datos como el nivel de inglés, la carrera elegida, el ingreso familiar promedio, entre otras, con la finalidad realizas el análisis descriptivo para crear visualizaciones y generar gráficos que muestren y nos ayuden a contar la historia. La arquitectura de datos se desarrolló con la finalidad de ver el proceso de análisis para que luego se pueda desarrollar e implementar un modelo que nos ayude a predecir el comportamiento de los alumnos frente al programa mediante el árbol de decisiones. Los resultados más resaltantes del estudio fueron que el nivel de inglés es una de las limitantes para la inscripción de los alumnos. Asimismo, el factor económico influye en la decisión, ya que es más probable que un alumno con ingreso promedio familiar mayor a 10,000.00 soles se matricule y, por último, que el modelo mediante el árbol de decisiones nos permite identificar si un alumno se matricula o no en el programa de Arizona. / In the following research work, the Data Science methodology was used to find possible solutions to the problem that the Arizona program of the University of Applied Sciences (UPC) has since it is not possible to reach the goal of students enrolled and admitted to despite presenting great benefits in the professional and educational aspect. The historical data of the years 2019, 2020 and 2021 was analyzed, which was collected directly from the university and the prescriptive approach was established. The most relevant variables of the database were identified such as the level of English, the chosen career, the average family income, among others, in order to carry out the descriptive analysis to create visualizations and generate graphs to help us to count the story. The data architecture was developed in order to see the analysis process so that later a model can be developed and implemented that helps us predict the behavior of students in front of the program through the decision tree. The most outstanding results of the study were that the level of English is one of the limitations for the enrollment of students. Likewise, the economic factor influences the decision, since it is more likely that a student with an average family income greater than 10,000.00 soles will enroll and, finally, that the model through the decision tree allows us to identify whether a student enrolls or not on the Arizona show. / Trabajo de investigación Ciencia de datos Arquitectura de datos Programa de Arizona Data science Data architecture Arizona Program
6	A Logistic Regression Analysis of Utah Colleges Exit Poll Response Rates Using SAS Software Stevenson, Clint W. 27 October 2006 (has links) (PDF) In this study I examine voter response at an interview level using a dataset of 7562 voter contacts (including responses and nonresponses) in the 2004 Utah Colleges Exit Poll. In 2004, 4908 of the 7562 voters approached responded to the exit poll for an overall response rate of 65 percent. Logistic regression is used to estimate factors that contribute to a success or failure of each interview attempt. This logistic regression model uses interviewer characteristics, voter characteristics (both respondents and nonrespondents), and exogenous factors as independent variables. Voter characteristics such as race, gender, and age are strongly associated with response. An interviewer's prior retail sales experience is associated with whether a voter will decide to respond to a questionnaire or not. The only exogenous factor that is associated with voter response is whether the interview occurred in the morning or afternoon. Exit Poll sampling weighting questionnaires interviewers voters polling places precincts backward selection SAS proc logistic surveylogistic model selection Utah Colleges Exit Poll UCEP SUDAAN SQL data management weight adjustments predicted probabilities logistic regression holdout research validation sample design data architecture adjustment exogenous statistics political science voters nonresponse response rate retail sales age race gender time of day Statistics and Probability

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