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

Toward adaptive management of coastal MPAs: The influence of different conservation targets and costs on the design of no-take areas

There is still much discussion on the most appropriate location, size and shape of marine protected areas (MPAs). These three factors were analyzed for a small coastal MPA, the Luiz Saldanha Marine Park (LSMP), for which a very limited amount of local ecological information was available when implemented in 1998. Marxan was used to provide a number of near-optimal solutions considering different levels of protection for the various conservation features and different costs. These solutions were compared with the existing no-take area of the LSMP. Information on 11 habitat types and distribution models for 3 of the most important species for the local artisanal fisheries was considered. The human activities with the highest economic and ecological impact in the study area (commercial and recreational fishing and scuba diving) were used as costs. The results show that the existing no-take area is actually located in the best area. However, the no-take area offers limited protection to vagile fish and covers a very small proportion of some of the available habitats. An increase in the conservation targets led to an increase in the number of no-take areas. The comparative framework used in this study can be applied elsewhere, providing relevant information to local stakeholders and managers in order to proceed with adaptive management. (C) 2015 Elsevier B.V. All rights reserved.