Data Marts

A obtenção de informações gerenciais relevantes, confiáveis e necessárias para a tomada de decisões sempre foi um desafio a ser enfrentado nas empresas. O objetivo deste artigo é apresentar um modelo de gestão da informação centralizado com a utilização das tecnologias compreendidas no ambiente de banco de dados Microsoft SQL Server e Mysql visando disponibilizar informações no modelo self-service para que as áreas de negócio da empresa desenvolvam seus próprios dashboards e relatórios gerenciais utilizando a ferramenta Qlikview, desta forma, desafogando, a área de tecnologia da informação da empresa que sempre tem recursos humanos escassos.

With globalization and industrialization we need to automate the processes so that efficiency can be increased in the over all perspective for which we are using the new concept which has emerged called artificial intelligence. By which we are making our machines more intelligent, efficient as well as reliable. There are various aspect of the machine learning models in which artificial intelligence data sets play up a major role. Now lets see how it works. Data set can be a single data base table or a single statistical data matrix, where every column of the table has a particular variable and each row corresponds to a given member of the data set. Machine learning heavily depends on data sets which trains the artificial intelligent models so that the required output can be desired from the experiment. As well as only the gathering of data is will not give you the correct output but the proper classification and labeling of data sets holds most of the importance. Types of data sets in artificial intelligence We have three different data sets: training set , validation set and testing set. Our artificial intelligence projects success depends mostly on the training dataset which is used to train an algorithm to understand how it works as well as how to imply the concepts of "Neural networks" .More over it includes both input and the expected output. It makes up the majority of 60 per cent of data. The testing models are fit to parameters in a process that is known as adjusting weights. A validation set is a set of data used to train the artificial intelligence with the goal to find and optimize the best model to solve a given problem. The validation set is also known as the dev set. It is used to select and tune the final artificial intelligence model. It makes up about 20 per cent of the bulk of data used. The validation set contrasts with the training and test sets in that it is an intermediate phase used for choosing the best model to optimizing it. Validation is considered a part of the training phase. It is in this phase that parameter tuning occurs for optimizing the selected model. Over fitting is checked and avoided in the validation set to eliminate errors that can be caused for future predictions and observations if an analysis corresponds to precisely to a specific dataset.

Building a useful and responsive Decision Support System (DSS) requires a deep understanding of the pertinent application domain before starting the system design. In this paper we report about an attempt to develop a mobile-based DSS for smallholder livestock keepers with Arusha region as a case study. The objective of the reported study is to provide an information tool for decision making to the smallholder livestock keepers. The development process involved: 1) employing information gathering techniques to understand smallholder livestock keepers’ information needs 2) studying the current methods that are used for information flow among livestock stakeholders. (i.e. smallholder livestock keepers, extension officers and livestock researchers) 3) analysis of the current situation within Arusha: located in the northern parts of Tanzania in terms of mobile phones penetration, with prospects of leveraging the high mobile phone penetration rate for enhanced information sharing among the smallholder livestock keepers and 4) exploration of options for the platform/model to be used for information access and delivery. The outputs of the above four activities were used to inform the requirements elicitation, and design phases of the mobile-based DSS system development. In addition, the mentioned four activities were supplemented by an extensive literature review of related works on requirements engineering in DSS development. It is anticipated that once the system has been developed, it will be of help to livestock keepers, improving farm-level productivity and decision making process. Findings from the study indicate that majority of smallholder livestock keepers in the selected area possess mobile phones and are in need of access to specific information to support their livestock related decision making. However, information access platforms/models that are currently in place do not cater for a satisfactory solution to their needs. Analysis of various options for designing a DSS platform has shown that a model that considers the administrative, organizational structure, as well as roles of relevant stakeholders in the livestock information flow will be useful for the studied context. The proposed Role-based Information Decision Support (RIDS) Model will facilitate data querying, analysis and information delivery based on users’ information requirements for the design of the DSS’s data marts. This will, in turn, be the basis for implementing a system of information sharing and delivery mechanism that will improve the decision making process and livestock management for smallholder livestock keepers in the studied geographical environment.

En el Hospital Regional Ernesto Torres Galdames, de la ciudad de Iquique, en la región de Tarapacá-Chile, se implementó una solución en base al uso de herramientas de Inteligencia de Negocios, para el apoyo en la toma de decisiones asociada a la información de prácticas profesionales. Se consideró un análisis de los procesos de la Unidad de Docencia, para conocer su modelo de procesos, realizar la selección de las herramientas a implementar, establecer los procesos de Extracción, Transformación y Carga, y la presentación de los datos y reportes. En base a la Metodología de Kimball se construyó un data mart, cuyo uso permitió a la Unidad de Docencia observar tendencias, cantidades de estudiantes registrados y, analizar y coordinar de mejor forma los cupos de prácticas profesionales.