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- Big data warehouse framework for smart revenue managementPublication . Ramos, Célia M. Q.; Correia, Marisol B.; Rodrigues, J. M. F.; Martins, Daniel; Serra, FranciscoRevenue 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
- New forecasting methods for hotel revenue management systemsPublication . Pereira, Luis; da Silva, Joana Marques; Serra, FranciscoAn accurate forecasting module is a key element of any revenue management system. This module includes demand forecasting, which involves tasks of forecasting complex seasonal time series. The challenge of producing accurate demand forecasts requires the application of suitable forecasting methods to address that complexity. The aim of this paper is to evaluate a new innovation state space modeling framework, based on innovations approach, developed for forecasting time series with complex seasonal patterns. This modeling framework provides an alternative to existing models of exponential smoothing, since it is capable of tackling seasonal complexities such a multiple seasonal periods and high frequency seasonality.