Abstract

International audience; In this paper, we describe a framework that allows to automate and repeat business process execution on different cloud configurations. We present how and why the different components of the experimentation pipeline-like Ansible, Docker and Jenkins-have been set up, and the kind of results we obtained on a large set of configurations from the AWS public cloud. It allows us to calculate actual prices regarding the cost of process execution, in order to compare not only pure performance but also the economic dimension of process execution.

Abstract

In an effort to curb air pollution, the city of Delhi (India), known to be one of the most populated, polluted, and congested cities in the world has run a trial experiment in two phases of 15 days intervals. During the experiment, most of four-wheeled vehicles were constrained to move on alternate days based on whether their plate numbers ended with odd or even digits. While the local government of Delhi represented by A. Kejriwal (leader of AAP party) advocated for the benefits of the experiment, the prime minister of India, N. Modi (former leader of BJP) defended the inefficiency of the initiative. This later has led to a strong polarization of public opinion towards OddEven experiment. This real-world urban experiment provided the scientific community with a unique opportunity to study the impact of political leaning on humans perception at a large-scale. We collect data about pollution and traffic congestion to measure the real effectiveness of the experiment. We use Twitter to capture the public discourse about the experiment in order to study people’s opinion within different dimensions: time, location, and topics. Our results reveal a strong influence of political affiliation on how people perceived the outcomes of the experiment. For instance, AAP supporters were significantly more enthusiastic about the success of OddEven compared to BJP supporters. However, taking into account location of people revealed that personal experience is able to overcome political bias.

Abstract

In recent years, the growing prevalence of social networks makes it possible to utilize human users as sensors to inspect city environment and human activities. Consequently, valuable insights can be gained by applying data mining techniques to the data generated through social networks. In this work, a practical approach to combine data mining techniques with statistical analysis is proposed to implement crowd sensing in a smart city. A case study to analyze the relationship between weather conditions and traffic congestion in Beijing based on tweets posted on Sina Weibo platform is presented to demonstrate the proposed approach. Following the steps of data pre-processing and topic determination, we applied Granger Causality Test to study the causal relationships between weather conditions, traffic congestion and human outdoor activity. The mediation analysis is also implemented to verify human outdoor activity as a mediator variable significantly carrying the influence of good weather to traffic congestion. The result demonstrates that outdoor activity serves as a mediator transmitting the effect of good weather on traffic congestion. In addition, the causes of negative emotion are also studied.