I. Introduction

The tourism industry is a major contributor to the economy of many countries and is faced by multiple challenges. Tourism is a complex industry where many factors affect its operation and where a huge amount of significant data is collected. Decision makers need to be supported by the right tools to support their decisions based on proper analysis and interpretation of the data. Data mining, machine learning and computer modeling techniques can be effectively used for the purpose of helping decision makers in increasing the revenue generated from the tourism industry.The purpose of this special session is to bring together researchers working in computational methods, from diverse communities, to present and discuss the latest contributions in this field that can be of high value for the Tourism Industry.
Special topics of interest include but are not limited to:

• Forecasting models

• Machine Learning methods for Time series prediction

• Forecast combination

• Optimization and price setting

• Dynamic Price setting Models for Hotels

• Decision support tools and Judgmental forecasting

• Future Studies and Impact of catastrophic events

• Hotel Revenue Management

• Tourism demand forecast

• Repeated Visitation analysis methods
  
  Organized by:
  
  The Data Mining and Computer Modeling Center of Excellence, Tourism project.

II. Scanning through the issue

The first paper by Nesreen Kamel and Amir F. Atiya and Neamat El Gayar and Hisham El-Shishiny studied the performance of alternative models such as machine learning methods The goal is to investigate how different machine learning models can be applied in the tourism prediction problem and to assess the performance of seven well known machine learning methods. Furthermore, They investigate the effect of including the time index as an input variable. Specifically, they considered the tourism demand time series for Hong Kong inbound travel.

 The second paper by Nedaa Agami and Mohamed Saleh and Ahmed Omran and Hisham El-Shishiny focused on assessing the impacts of wildcards on the future revenues of the tourism industry, in Egypt. This tool complements the forecasting module of the ongoing project of "Data Mining for improving the tourism industry revenue in Egypt" (funded by the Data Mining and Computer Modeling Center of Excellence, Ministry of Communications and Information Technology) in two distinct aspects. First, the tool adds a qualitative aspect by utilizing an advanced qualitative futures studies method, called Real-Time Delphi Survey, in order to estimate probabilities and impacts associated with wildcards based on experts' opinions. In that project, experts are weighted according to various attributes. Second, the tool generates various scenarios (not just a
single forecast) by modifying the extrapolation of historical trends in view of expectations about future wildcards. This is done by utilizing an advanced quantitative futures studies method, called trend impact analysis.

The third paper by Athanasius Zakhary and Neamat El Gayar and Amir F. Atiya  examined in more details a popular forecasting model that uses reservation data, referred to in the literature as the pickup method.In particular, They present a new framework for the pickup technique with 8 different variations and compare the results of these variations using a variety of simulated hotel reservations data.

The fourth paper by Neamat El Gayar and Abdeltawab M.A. Hendawi and Athanasius Zakhary and Hisham El-Shishiny,They proposed a conceptual RM model that relies on an accurate room demand forecast model and a dynamic room pricing and allocation model. The system also attempts to combine expert knowledge with statistical models to provide a exible and effective decision support tool for revenue maximization.

The fifth paper by Hossam Said Shehata and Hanan S. Kattara and Mohamed Farid El-Sahn They aimed to determine the degree of success of 5-star hotels in Egypt in implementing Revenue Management System (RMS), the level of hotel managers’ awareness of the importance of applying it and how this awareness may affect the implementation of such a system. An in depth–interview has been conducted with a sample of five star hotels in Egypt. Results proved the existence of an appropriate level of implementation. The level of awareness among mangers proved a need for much effort.
 

Acknowledgments.

The board editors would like to take this opportunity to thank all those authors who submitted papers, and all of the reviewers who took such care in reviewing these papers. As editors. We hope that the papers in this issue will stimulate further progress in this direction. We believe that the best is yet to come.

III. BibTex of Contents (Table of Contents)