Kannattavuusraportoinnin laadun arviointi data warehouse ratkaisussa Case: Paperiteollisuuden myynnin ja asiakaspalvelun tietojärjestelmä

Tutkielman tavoitteena oli selvittää, onko tutkielman tilaajan toteuttaman kannattavuusraportoinnin laatu käyttäjien mielestä riittävä. Kannattavuusraportointi on toteutettu data warehouse tekniikalla. Tutkielman tavoitteina oli myös määrittää, mitä ohjelmiston laatu tarkoittaa ja miten sitä voidaan arvioida. Tutkimuksessa käytettiin kvalitatiivista tutkimusmenetelmää. Laadun arviointiin käytetty aineisto kerättiin haastattelemalla seitsemäätoista kannattavuusraportoinnin aktiivikäyttäjää. Tutkielmassa ohjelmiston laatu tarkoittaa sen kykyä täyttää tai ylittää käyttäjiensä kohtuulliset toiveet ja odotukset. Laatua arvioitiin standardin ISO/IEC 9126 määrittelemällä kuudella laatuominaisuudella, jotka kuvaavat minimaalisella päällekkäisyydellä ohjelmiston laadun. Lisäksi arvioinnissa hyödynnettiin varsinaiseen standardiin kuulumatonta informatiivista liitettä, joka tarkentaa ISO/IEC 9126 standardissa esitettyjä laadun ominaispiirteitä. Tutkimuksen tuloksena voidaan todeta, että käyttäjien mukaan kannattavuusraportointi on tarpeeksi laadukas, sillä se pystyy tarjoamaan helppokäyttöisiä, oikeanmuotoisia raportteja riittävän hyvällä vasteajalla käyttäjien tarpeisiin. Tehokkaasta hyödyntämisestä voidaan päätellä data warehousen rakentamisen onnistuneen. Tutkimuksessa nousi esiin myös runsaasti kehittämis- ja parannusideoita, jotka toimivat yhtenä kehitystyön apuvälineenä tulevaisuudessa.

Traducción técnica del capítulo III del libro Advanced Data Warehouse design: from conventional to spatial and temporal applications

The proposal to work on this final project came after several discussions held with Dr. Elzbieta Malinowski Gadja, who in 2008 published the book entitled Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications (Data-Centric Systems and Applications). The project was carried out under the technical supervision of Dr. Malinowski and the direct beneficiary was the University of Costa Rica (UCR) where Dr. Malinowski is a professor at the Department of Computer Science and Informatics. The purpose of this project was twofold: First, to translate chapter III of said book with the intention of generating educational material for the use of the UCR and, second, to venture in the field of technical translation related to data warehouse. For the first component, the goal was to generate a final product that would eventually serve as an educational tool for the post-graduate courses of the UCR. For the second component, this project allowed me to acquire new skills and put into practice techniques that have helped me not only to perfom better in my current job as an Assistant Translator of the Inter-American BAnk (IDB), but also to use them in similar projects. The process was lenggthy and required torough research and constant communication with the author. The investigation focused on the search of terms and definitions to prepare the glossary, which was the basis to start the translation project. The translation process itself was carried out by phases, so that comments and corrections by the author could be taken into account in subsequent stages. Later, based on the glossary and the translated text, illustrations had been created in the Visio software were translated. In addition to the technical revision by the author, professor Carme Mangiron was in charge of revising the non-technical text. The result was a high-quality document that is currently used as reference and study material by the Department of Computer Science and Informatics of Costa Rica.

Mejoramiento en los procesos de desarrollo, carga, validación y explotación implicados en un Data Warehouse mediante la creación de una fábrica de información corporativa aplicada al área de ventas de la empresa Gamesa

Tesis (Maestría en Ciencias de la Administración con Especialidad en Sistemas) UANL

Guía para la construcción de un Data Warehouse

[Tesis] ( Maestría en Informática Administrativa con Especialidad en Procesos Productivos de Negocios) U.A.N.L.

Estratégia para extração, transformação e armazenamento em Data Warehouse ativo baseada em políticas configuráveis de propagação de dados

Pós-graduação em Ciência da Computação - IBILCE

Aplicação do algoritmo Rough Sets em um Data Warehouse

This Project aims to develop methods for data classification in a Data Warehouse for decision-making purposes. We also have as another goal the reduction of an attribute set in a Data Warehouse, in which a given reduced set is capable of keeping the same properties of the original one. Once we achieve a reduced set, we have a smaller computational cost of processing, we are able to identify non-relevant attributes to certain kinds of situations, and finally we are also able to recognize patterns in the database that will help us to take decisions. In order to achieve these main objectives, it will be implemented the Rough Sets algorithm. We chose PostgreSQL as our data base management system due to its efficiency, consolidation and finally, it’s an open-source system (free distribution)

Prototipo di un sistema data warehouse per le Università italiane basato su tecnologie semantiche

Tesi riguardante le differenze tra Semantic Web e Web Tradizionale

Data warehouse e cruscotto direzionale per l'analisi del personale in un'azienda di servizi

Obiettivo della tesi è la progettazione e lo sviluppo di un sistema di BI e di relativa reportistica per un'azienda di servizi. Il tutto realizzato mediante la suite Microsoft Business Intelligence.

Progettazione ed implementazione di un data warehouse di supporto alla profilazione dei consumi energetici domestici

Sviluppo e analisi di un dataset campione, composto da circa 3 mln di entry ed estratto da un data warehouse di informazioni riguardanti il consumo energetico di diverse smart home.

Modelling spatial decisión support systems focused on the development of a data warehouse: applying software engineering

Nowadays, organizations have plenty of data stored in DB databases, which contain invaluable information. Decision Support Systems DSS provide the support needed to manage this information and planning médium and long-term ?the modus operandi? of these organizations. Despite the growing importance of these systems, most proposals do not include its total evelopment, mostly limiting itself on the development of isolated parts, which often have serious integration problems. Hence, methodologies that include models and processes that consider every factor are necessary. This paper will try to fill this void as it proposes an approach for developing spatial DSS driven by the development of their associated Data Warehouse DW, without forgetting its other components. To the end of framing the proposal different Engineering Software focus (The Software Engineering Process and Model Driven Architecture) are used, and coupling with the DB development methodology, (and both of them adapted to DW peculiarities). Finally, an example illustrates the proposal.

Enrichment of the Phenotypic and Genotypic Data Warehouse analysis using Question Answering systems to facilitate the decision making process in cereal breeding programs

Currently there are an overwhelming number of scientific publications in Life Sciences, especially in Genetics and Biotechnology. This huge amount of information is structured in corporate Data Warehouses (DW) or in Biological Databases (e.g. UniProt, RCSB Protein Data Bank, CEREALAB or GenBank), whose main drawback is its cost of updating that makes it obsolete easily. However, these Databases are the main tool for enterprises when they want to update their internal information, for example when a plant breeder enterprise needs to enrich its genetic information (internal structured Database) with recently discovered genes related to specific phenotypic traits (external unstructured data) in order to choose the desired parentals for breeding programs. In this paper, we propose to complement the internal information with external data from the Web using Question Answering (QA) techniques. We go a step further by providing a complete framework for integrating unstructured and structured information by combining traditional Databases and DW architectures with QA systems. The great advantage of our framework is that decision makers can compare instantaneously internal data with external data from competitors, thereby allowing taking quick strategic decisions based on richer data.