Navigation systems often aid users when traveling in unfamiliar locations. Current navigation systems tend to focus on identifying the shortest or fastest routes between two points. However, path cost expressed in terms of length and distance (i.e., the utilitarian qualities of travel) is only one attribute which could be used in route recommendation. Recent years have seen navigation systems moving beyond such utilitarian attributes. These include systems aimed at providing users with scenic, safe or attractive routes among other dimensions. In this paper, we contribute to the existing research domain by providing an overview of the different quality-aware route navigation systems that have been proposed in past research for pedestrians. In particular, we examine the different qualities which have been used as key criteria in route recommendation. As the outcome of the paper, we provide a categorization of these systems based on our proposed SWEEP taxonomy. In addition, we outline the various data sources, algorithms and evaluation approaches that have been used to implement quality-aware route navigation systems. Afterwards, we conclude by discussing potential problems encountered in previous studies and highlight promising directions for future research.

Twitter is one of the largest online platforms where people exchange information. In the first few years since its emergence, researchers have been exploring ways to use Twitter data in various decision making scenarios, and have shown promising results. In this review, we examine 28 newer papers published in last five years (since 2016) that continued to advance Twitter-aided decision making. The application scenarios we cover include product sales prediction, stock selection, crime prevention, epidemic tracking, and traffic monitoring. We first discuss the findings presented in these papers, that is how much decision making performance has been improved with the help of Twitter data. Then we offer a methodological analysis that considers four aspects of methods used in these papers, including problem formulation, solution, Twitter feature, and information transformation. This methodological analysis aims to enable researchers and decision makers to see the applicability of Twitter-aided methods in different application domains or platforms.