Friday, Oct 23, 12:30-13:30h, in Pav. K16 Rodrigo Gonçalves will present research he has performed in the context of the Daipex project; see below for the abstract.
Title: Spatio-Temporal Data Mining with Event Logs from High Volume Logistics Information
In logistics, software aids for transportation planning and scheduling are often based in approximations and abstractions that do not take into account real-world data. The aim of this work is to provide an analysis and methodology, based on real-world data, on how to obtain probability density functions for prediction of activity duration. Given a large spatio-temporal database of events, where each event consists of the fields event ID, time, location, and event type, the aim is to extract valuable information about activities duration. The process is not straightforward since the log is human-influenced creating uncertainty related with the time at which the events are logged. In order to overcome this, a novel framework is proposed: it uses the spatio-temporal trajectories to identify regions-of-interest based on speed, and builds an ROI activity time-line using the activities extracted from event logs.