Abstract

Abstract

Abstract

Abstract
Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way. Therefore, detecting significant places and the frequency of movements between them is fundamental to understand human behavior. In this paper, we propose a method for discovering user habits without any a priori or external knowledge by introducing a density-based clustering for spatio-temporal data to identify meaningful places and by applying a Gaussian Mixture Model (GMM) over the set of meaningful places to identify the representations of individual habits. To evaluate the proposed method we use two real-world datasets. One dataset contains high-density GPS data and the other one contains GSM mobile phone data in a coarse representation. The results show that the proposed method is suitable for this task as many unique habits were identified. This can be used for understanding users' behavior and to draw their characterizing profiles having a panorama of the mobility patterns from the data.

Abstract
The link prediction task has found numerous applications in real-world scenarios. However, in most of the cases like interactions, purchases, mobility, etc., links can re-occur again and again across time. As a result, the data being generated is excessively large to handle, associated with the complexity and sparsity of networks. Therefore, we propose a very fast, memory-less, and dynamic sampling-based method for predicting recurring links for a successive future point in time. This method works by biasing the links exponentially based on their time of occurrence, frequency, and stability. To evaluate the efficiency of our method, we carried out rigorous experiments with massive real-world graph streams. Our empirical results show that the proposed method outperforms the state-of-the-art method for recurring links prediction. Additionally, we also empirically analyzed the evolution of links with the perspective of multi-graph topology and their recurrence probability over time.

Abstract
The smart cities concept - use of connected services and intelligent systems to support decision making in cities governance - aims to build better sustainability and living conditions for urban spaces, which are more complex every day. This work expects to optimize the waste collection circuits for non-residential customers in a city in Portugal. It is developed through the implementation of a simple, low-cost methodology when compared to commercial-available sensor systems. The main goal is to build a classifier for each client, being able to forecast the presence or absence of containers and, in a second step, predict how many containers of glass, paper or plastic would be available to be collected. Data were acquired during the period of one year, from January to December 2017, from more than 100 customers, resulting in a 26.000+ records dataset. Due to its degree of interpretability, we use Decision trees, implemented with a sliding window, which ran through the months of the year, stacking it one-by-one and/or merging few groups aiming the best correct predictions score. This project results in more efficient waste-collection routes, increasing the operation profits and reducing both costs and fuel-consumption, therefore diminishing it environmental footprint.