Big data warehouse framework for smart revenue management

Revenue Management’s most cited definitions is probably “to sell the right accommodation to the right customer, at the right time and the right price, with optimal satisfaction for customers and hoteliers”. Smart Revenue Management (SRM) is a project, which aims the development of smart automatic techniques for an efficient optimization of occupancy and rates of hotel accommodations, commonly referred to, as revenue management. One of the objectives of this project is to demonstrate that the collection of Big Data, followed by an appropriate assembly of functionalities, will make possible to generate a Data Warehouse necessary to produce high quality business intelligence and analytics. This will be achieved through the collection of data extracted from a variety of sources, including from the web. This paper proposes a three stage framework to develop the Big Data Warehouse for the SRM. Namely, the compilation of all available information, in the present case, it was focus only the extraction of information from the web by a web crawler – raw data. The storing of that raw data in a primary NoSQL database, and from that data the conception of a set of functionalities, rules, principles and semantics to select, combine and store in a secondary relational database the meaningful information for the Revenue Management (Big Data Warehouse). The last stage will be the principal focus of the paper. In this context, clues will also be giving how to compile information for Business Intelligence. All these functionalities contribute to a holistic framework that, in the future, will make it possible to anticipate customers and competitor’s behavior, fundamental elements to fulfill the Revenue Management

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.

Methods of Estimation in Multiple Linear Regression: Application to Clinical Data

Nesse artigo, tem-se o interesse em avaliar diferentes estratégias de estimação de parâmetros para um modelo de regressão linear múltipla. Para a estimação dos parâmetros do modelo foram utilizados dados de um ensaio clínico em que o interesse foi verificar se o ensaio mecânico da propriedade de força máxima (EM-FM) está associada com a massa femoral, com o diâmetro femoral e com o grupo experimental de ratas ovariectomizadas da raça Rattus norvegicus albinus, variedade Wistar. Para a estimação dos parâmetros do modelo serão comparadas três metodologias: a metodologia clássica, baseada no método dos mínimos quadrados; a metodologia Bayesiana, baseada no teorema de Bayes; e o método Bootstrap, baseado em processos de reamostragem.

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)

Hyperinsulinemic hypoglycemia of the infancy. Analysis of clinical data from a Brazilian sample

Objective: To review the presentation of hyperinsulinemic hypoglycemia of the infancy (HHI), its treatment and histology in Brazilian pediatric endocrinology sections. Materials and method: The protocol analyzed data of birth, laboratory results, treatment, surgery, and pancreas histology. Results: Twenty-five cases of HHI from six centers were analyzed: 15 male, 3/25 born by vaginal delivery. The average age at diagnosis was 10.3 days. Glucose and insulin levels in the critical sample showed an average of 24.7 mg/dL and 26.3 UI/dL. Intravenous infusion of the glucose was greater than 10 mg/kg/min in all cases (M:19,1). Diazoxide was used in 15/25 of the cases, octreotide in 10, glucocorticoid in 8, growth hormone in 3, nifedipine in 2 and glucagon in 1. Ten of the cases underwent pancreatectomy and histology results showed the diffuse form of disease. Conclusion: This is the first critic review of a Brazilian sample with congenital HHI. Arq Bras Endocrinol Metab. 2012; 56(9): 666-71

Computational framework to support integration of biomolecular and clinical data within a translational approach

Background The use of the knowledge produced by sciences to promote human health is the main goal of translational medicine. To make it feasible we need computational methods to handle the large amount of information that arises from bench to bedside and to deal with its heterogeneity. A computational challenge that must be faced is to promote the integration of clinical, socio-demographic and biological data. In this effort, ontologies play an essential role as a powerful artifact for knowledge representation. Chado is a modular ontology-oriented database model that gained popularity due to its robustness and flexibility as a generic platform to store biological data; however it lacks supporting representation of clinical and socio-demographic information. Results We have implemented an extension of Chado – the Clinical Module - to allow the representation of this kind of information. Our approach consists of a framework for data integration through the use of a common reference ontology. The design of this framework has four levels: data level, to store the data; semantic level, to integrate and standardize the data by the use of ontologies; application level, to manage clinical databases, ontologies and data integration process; and web interface level, to allow interaction between the user and the system. The clinical module was built based on the Entity-Attribute-Value (EAV) model. We also proposed a methodology to migrate data from legacy clinical databases to the integrative framework. A Chado instance was initialized using a relational database management system. The Clinical Module was implemented and the framework was loaded using data from a factual clinical research database. Clinical and demographic data as well as biomaterial data were obtained from patients with tumors of head and neck. We implemented the IPTrans tool that is a complete environment for data migration, which comprises: the construction of a model to describe the legacy clinical data, based on an ontology; the Extraction, Transformation and Load (ETL) process to extract the data from the source clinical database and load it in the Clinical Module of Chado; the development of a web tool and a Bridge Layer to adapt the web tool to Chado, as well as other applications. Conclusions Open-source computational solutions currently available for translational science does not have a model to represent biomolecular information and also are not integrated with the existing bioinformatics tools. On the other hand, existing genomic data models do not represent clinical patient data. A framework was developed to support translational research by integrating biomolecular information coming from different “omics” technologies with patient’s clinical and socio-demographic data. This framework should present some features: flexibility, compression and robustness. The experiments accomplished from a use case demonstrated that the proposed system meets requirements of flexibility and robustness, leading to the desired integration. The Clinical Module can be accessed in http://dcm.ffclrp.usp.br/caib/pg=iptrans webcite.

Clinical Data Mining: Traumatic Brain Injury

Il trauma cranico é tra le piú importanti patologie traumatiche. Ogni anno 250 pazienti ogni 100.000 abitanti vengono ricoverati in Italia per un trauma cranico. La mortalitá é di circa 17 casi per 100.000 abitanti per anno. L’Italia si trova in piena “media” Europea considerando l’incidenza media in Europa di 232 casi per 100.000 abitanti ed una mortalitá di 15 casi per 100.000 abitanti. Degli studi hanno indicato come una terapia anticoagulante é uno dei principali fattori di rischio di evolutiviá di una lesione emorragica. Al contrario della terapia anticoagulante, il rischio emorragico correlato ad una terapia antiaggregante é a tutt’oggi ancora in fase di verifica. Il problema risulta rilevante in particolare nella popolazione occidentale in quanto l’impiego degli antiaggreganti é progressivamente sempre piú diffuso. Questo per la politica di prevenzione sostenuta dalle linee guida nazionali e internazionali in termini di prevenzione del rischio cardiovascolare, in particolare nelle fasce di popolazione di etá piú avanzata. Per la prima volta, é stato dimostrato all’ospedale di Forlí[1], su una casistica sufficientemente ampia, che la terapia cronica con antiaggreganti, per la preven- zione del rischio cardiovascolare, puó rivelarsi un significativo fattore di rischio di complicanze emorragiche in un soggetto con trauma cranico, anche di grado lieve. L’ospedale per approfondire e convalidare i risultati della ricerca ha condotto, nell’anno 2009, una nuova indagine. La nuova indagine ha coinvolto oltre l’ospedale di Forlí altri trentuno centri ospedalieri italiani. Questo lavoro di ricerca vuole, insieme ai ricercatori dell’ospedale di Forlí, verificare: “se una terapia con antiaggreganti influenzi l’evolutivitá, in senso peggiorativo, di una lesione emorragica conseguente a trauma cranico lieve - moderato - severo in un soggetto adulto”, grazie ai dati raccolti dai centri ospedalieri nel 2009. Il documento é strutturato in due parti. La prima parte piú teorica, vuole fissare i concetti chiave riguardanti il contesto della ricerca e la metodologia usata per analizzare i dati. Mentre, la seconda parte piú pratica, vuole illustrare il lavoro fatto per rispondere al quesito della ricerca. La prima parte é composta da due capitoli, che sono: • Il capitolo 1: dove sono descritti i seguenti concetti: cos’é un trauma cra- nico, cos’é un farmaco di tipo anticoagulante e cos’é un farmaco di tipo antiaggregante; • Il capitolo 2: dove é descritto cos’é il Data Mining e quali tecniche sono state usate per analizzare i dati. La seconda parte é composta da quattro capitoli, che sono: • Il capitolo 3: dove sono state descritte: la struttura dei dati raccolti dai trentadue centri ospedalieri, la fase di pre-processing e trasformazione dei dati. Inoltre in questo capitolo sono descritti anche gli strumenti utilizzati per analizzare i dati; • Il capitolo 4: dove é stato descritto come é stata eseguita l’analisi esplorativa dei dati. • Il capitolo 5: dove sono descritte le analisi svolte sui dati e soprattutto i risultati che le analisi, grazie alle tecniche di Data Mining, hanno prodotto per rispondere al quesito della ricerca; • Il capitolo 6: dove sono descritte le conclusioni della ricerca. Per una maggiore comprensione del lavoro sono state aggiunte due appendici. La prima tratta del software per data mining Weka, utilizzato per effettuare le analisi. Mentre, la seconda tratta dell’implementazione dei metodi per la creazione degli alberi decisionali.

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

Tesi riguardante le differenze tra Semantic Web e Web Tradizionale