Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be overcome to fully extract the potential benefits of such an approach (such as data-in-motion analytics). In this paper, we investigate the challenges of running ML/DL on edge devices in a distributed way, paying special attention to how techniques are adapted or designed to execute on these restricted devices. The techniques under discussion pervade the processes of caching, training, inference, and offloading on edge devices. We also explore the benefits and drawbacks of these strategies.

Urban data is gradually being opened to the public. Tools for exploitation, analysis and discovery of new knowledge in large data sets are the key to enable citizens to make sense of such large amount of data. The purpose of this work is to analyze how data analysis associated with visualization techniques in different levels can lead to the improvement of the interpretability of open data. With the support of machine learning techniques, these visualizations may improve pattern identification in urban data sets. To guide our discussion, a case study was conducted analyzing socioeconomic data released by the Chicago city government. We discussed the use of different visualizations in this scenario, tailored for univariate, bivariate and multivariate analysis. We also performed an evaluation of the different forms of visualization proposed in this work. We could observe that allowing the user to explore open urban data using some specific visualizations may lead to more effective data interpretation.

Machine learning (ML) solutions have been proposed to make public transportation more attractive. Works that employ ML in bus transportation focus on various problems, such as travel time prediction or passenger flow prediction. These solutions look to improve elements of transportation services, such as the availability of information on passengers’ travel time and the reliability and regularity of the service. An analysis of the solutions proposed in the literature for public transportation by bus can reveal opportunities for data scientists and transportation professionals, and highlight problems that have been only slightly explored. In addition, mapping information about modeling these solutions (e.g., types of data produced by devices on the transportation network, which can be used in modeling a solution) could help beginner data scientists develop public transportation solutions. Transportation professionals can benefit from an overview of possible transportation solutions to improve transportation problems and direct government agency efforts to implement these solutions. This paper presents a survey of ML-based solutions for public bus transportation and details the modeling of these solutions (e.g., data types, ML algorithms). In addition, the problems tackled in the literature are categorized into four themes, and the solutions proposed to deal with them are schematized, highlighting problems that are little explored.