A systematic literature review on the use of big data for sustainable tourism

Sustainable tourism research focuses on mitigating or remediating environmental, social and economic impacts on tourism. In the past years, Big Data approaches have been applied to the field of tourism allowing for remarkable progress. However, there seems to be little evidence to support that such approaches are an inspiration to sustainable tourism and are being implemented. In this context, we aim to obtain a comprehensive overview of the use of Big Data in sustainable tourism to address various issues and understand how Big Data can support decision-making in such scenarios. To that end, this paper reports on the results of a literature review via a combination of a Systematic Literature Review (SLR) in Software Engineering, and the use of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. In summary, we investigated four facets: (a) sources of big data, (b) approaches, (c) purposes, and (d) contexts of application. The results suggest that the use of various approaches have impacted practices in sustainable tourism. The findings provide a thorough understanding of the state of the art of Big Data application in sustainable tourism and provide valuable insights to foster growth both in terms of research and practice

New recommendation to predict export value using big data and machine learning technique

Official statistics on monthly export values have a publicity lag between the current period and the published publication. None of the previous researchers estimated the value of exports for the monthly period. This circumstance is due to limitations in obtaining supporting data that can predict the criteria for the current export value of goods. AIS data is one type of big data that can provide solutions in producing the latest indicators to forecast export values. Statistical Methods and Conventional Machine Learning are implemented as forecasting methods. Seasonal ARIMA and Artificial Neural Network (ANN) methods are both used in research to forecast the value of Indonesia’s exports. However, ANN has a weakness that requires high computational costs to obtain optimal parameters. Genetic Algorithm (GA) is effective in increasing ANN accuracy. Based on these backgrounds, this paper aims to develop and select an AIS indicator to predict the monthly export value in Indonesia and optimize ANN performance by combining the ANN algorithm with the genetic algorithm (GA-ANN). The research successfully established five indicators that can be used as predictors in the forecasting model. According to the model evaluation results, the genetic algorithm has succeeded in improving the performance of the ANN model as indicated by the resulting RMSE GA-ANN value, which is smaller than the RMSE of the ANN model

Designing for Sustainability:Lessons Learned from Four Industrial Projects

Scientific research addressing the relation between software and sustainability is slowly maturing in two focus areas, related to `sustainable software' and `software for sustainability'. The first is better understood and may include research foci like energy-efficient software and software maintainability. It most-frequently covers `technical' concerns. The second, `software for sustainability', is much broader in both scope and potential impact, as it entails how software can contribute to sustainability goals in any sector or application domain. Next to the technical concerns, it may also cover economic, social, and environmental sustainability. Differently from researchers, practitioners are often not aware or well-trained in all four types of software sustainability concerns. To address this need, in previous work we have defined the Sustainability-Quality Assessment Framework (SAF) and assessed its viability via the analysis of a series of software projects. Nevertheless, it was never used by practitioners themselves, hence triggering the question: What can we learn from the use of SAF in practice? To answer this question, we report the results of practitioners applying the SAF to four industrial cases. The results show that the SAF helps practitioners in (1) creating a sustainability mindset in their practices, (2) uncovering the relevant sustainability-quality concerns for the software project at hand, and (3) reasoning about the inter-dependencies and trade-os of such concerns as well as the related short- and long-term implications. Next to improvements for the SAF, the main lesson for us as researchers is the missing explicit link between the SAF and the (technical) architecture design