Crowdsourcing for Top-K Query Processing over Uncertain Data

Querying uncertain data has become a prominent application due to the proliferation of user-generated content from social media and of data streams from sensors. When data ambiguity cannot be reduced algorithmically, crowdsourcing proves a viable approach, which consists of posting tasks to humans and harnessing their judgment for improving the confidence about data values or relationships. This paper tackles the problem of processing top- K queries over uncertain data with the help of crowdsourcing for quickly converging to the realordering of relevant results. Several offline and online approaches for addressing questions to a crowd are defined and contrasted on both synthetic and real data sets, with the aim of minimizing the crowd interactions necessary to find the realordering of the result set

Behavioral Communities and the Atomic Structure of Networks

We develop a theory of `behavioral communities' and the `atomic structure' of networks. We define atoms to be groups of agents whose behaviors always match each other in a set of coordination games played on the network. This provides a microfoundation for a method of detecting communities in social and economic networks. We provide theoretical results characterizing such behavior-based communities and atomic structures and discussing their properties in large random networks. We also provide an algorithm for identifying behavioral communities. We discuss applications including: a method of estimating underlying preferences by observing behavioral conventions in data, and optimally seeding diffusion processes when there are peer interactions and homophily. We illustrate the techniques with applications to high school friendship networks and rural village networks

An urban bikeway network design model for inclusive and equitable transport policies

Abstract This study suggests an optimization framework to plan and design a network of bike lanes in an urban context, based on equity principles and subject to a given available budget. The novelty of the proposal consists in an objective function that aims at minimizing the existing inequities among different population groups in terms of accessibility/opportunity to the bikeways. The proposed methodology represents a reliable decision support system tool that could help transport authorities/managers to select the priority areas of their future investments related to the cycling infrastructures. To prove the effectiveness and value of the methodology, an application with relevant analysis to a test case study is presented

Automated Problem Decomposition for the Boolean Domain with Genetic Programming

Researchers have been interested in exploring the regularities and modularity of the problem space in genetic programming (GP) with the aim of decomposing the original problem into several smaller subproblems. The main motivation is to allow GP to deal with more complex problems. Most previous works on modularity in GP emphasise the structure of modules used to encapsulate code and/or promote code reuse, instead of in the decomposition of the original problem. In this paper we propose a problem decomposition strategy that allows the use of a GP search to find solutions for subproblems and combine the individual solutions into the complete solution to the problem