Answering Complex Location-Based Queries with Crowdsourcing

International audienceCrowdsourcing platforms provide powerful means to execute queries that require some human knowledge, intelligence and experience instead of just automated machine computation, such as image recognition, data filtering and labeling. With the development of mobile devices and the rapid prevalence of smartphones that boosted mobile Internet access, location-based crowdsourcing is quickly becoming ubiquitous, enabling location-based queries assigned to and performed by humans. In sharp contrast of existing location-based crowdsourcing approaches that focus on simple queries, in this paper, we describe a crowdsourcing process model that supports queries including several crowd activities, and can be applied in a variety of location-based crowdsourcing scenarios. We also propose different strategies for managing this crowdsourcing process. Finally, we describe the architecture of our system, and present an experimental study conducted on pseudo-real dataset that evaluates the process outcomes depending on these execution strategies

Optimization of Orchestration of Geocrowdsourcing Activities

International audienceGeocrowdsourcing has proved to be very valuable in crisis situations. Calling to citizen on the ground or to experts or trained amateurs to help in the mapping of crisis situation is a recognized and valuable practice. However, despite the experience gained from real and dramatic situations, it remains difficult to set up and execute complex processes that require actions of both people on the ground and people on the web, and to understand how to get the best result at the minimal cost in term of users actions. In this paper, we describe a process that can be used to assess a global situation on a map using a combination of services and user operations. We want to understand how best to distribute a limited amount of human actions between different kind of tasks in order to get the most reliable result. Since it is difficult to conduct experimentation, we have decided to use simulation to reach a result that could be applied on the ground. This simulation relies on a geolocalised corpus of tweets. It provides some hints about how to deploy an exercise on the ground that are discussed as a conclusion. In addition, we propose a binary integer programming (BIP) making best use of the available workers