Real-time detection of anomalous paths through networks

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.The proliferation of increasingly inexpensive mobile devices capable of transmitting accurate positional information to other devices and servers has led to a variety of applications ranging from health situation monitoring to GPS-based offender monitoring. One of the resultant challenges is in understanding, in real-time, when incoming observations merit further examination. In this research, we investigate an approach for identifying anomalous paths through networks using real-time comparisons to a previously learned model. Our approach, the development of a series of “posterior weighted graphs” allows us to both determine which underlying model a particular path most closely represents as well as evaluate this relationship in real-time as more observations become available. Here we present the posterior weighted graph approach for examining path similarity and an extension for detecting anomalies in real-time. Our results illustrate how we can distinguish from among multiple candidate paths and, likewise, when observations no longer match an expected model

Path Clustering Based on a Novel Dissimilarity Function for Ride-Sharing Recommenders

Ride-sharing practice represents one of the possible answers to the traffic congestion problem in today's cities. In this scenario, recommenders aim to determine similarity among different paths with the aim of suggesting possible ride shares. In this paper, we propose a novel dissimilarity function between pairs of paths based on the construction of a shared path, which visits all points of the two paths by respecting the order of sequences within each of them. The shared path is computed as the shortest path on a directed acyclic graph with precedence constraints between the points of interest defined in the single paths. The dissimilarity function evaluates how much a user has to extend his/her path for covering the overall shared path. After computing the dissimilarity between any pair of paths, we execute a fuzzy relational clustering algorithm for determining groups of similar paths. Within these groups, the recommenders will choose users who can be invited to share rides. We show and discuss the results obtained by our approach on 45 paths

多元情報のプロファイリングに基づくソーシャルメディアからの個人の再特定

電気通信大学202