TY - JOUR
T1 - ICON Loop Carpooling Show Case
JF - Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach
Y1 - 2017
A1 - Mirco Nanni
A1 - Lars Kotthoff
A1 - Riccardo Guidotti
A1 - Barry O'Sullivan
A1 - Dino Pedreschi
AB - In this chapter we describe a proactive carpooling service that combines induction and optimization mechanisms to maximize the impact of carpooling within a community. The approach autonomously infers the mobility demand of the users through the analysis of their mobility traces (i.e. Data Mining of GPS trajectories) and builds the network of all possible ride sharing opportunities among the users. Then, the maximal set of carpooling matches that satisfy some standard requirements (maximal capacity of vehicles, etc.) is computed through Constraint Programming models, and the resulting matches are proactively proposed to the users. Finally, in order to maximize the expected impact of the service, the probability that each carpooling match is accepted by the users involved is inferred through Machine Learning mechanisms and put in the CP model. The whole process is reiterated at regular intervals, thus forming an instance of the general ICON loop.
VL - 10101
UR - https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=314
ER -
TY - JOUR
T1 - The Inductive Constraint Programming Loop
JF - IEEE Intelligent Systems
Y1 - 2017
A1 - Bessiere, Christian
A1 - De Raedt, Luc
A1 - Tias Guns
A1 - Lars Kotthoff
A1 - Mirco Nanni
A1 - Siegfried Nijssen
A1 - Barry O'Sullivan
A1 - Paparrizou, Anastasia
A1 - Dino Pedreschi
A1 - Simonis, Helmut
AB - Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, which we call the inductive constraint programming loop. In this approach data is gathered and analyzed systematically in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other.
ER -
TY - ABST
T1 - Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach.
Y1 - 2016
A1 - Bessiere, Christian
A1 - De Raedt, Luc
A1 - Lars Kotthoff
A1 - Siegfried Nijssen
A1 - Barry O'Sullivan
A1 - Dino Pedreschi
AB - A successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge. This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on “Inductive Constraint Programming” and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases.
ER -
TY - CONF
T1 - Find Your Way Back: Mobility Profile Mining with Constraints
T2 - Principles and Practice of Constraint Programming
Y1 - 2015
A1 - Lars Kotthoff
A1 - Mirco Nanni
A1 - Riccardo Guidotti
A1 - Barry O'Sullivan
AB - Mobility profile mining is a data mining task that can be formulated as clustering over movement trajectory data. The main challenge is to separate the signal from the noise, i.e. one-off trips. We show that standard data mining approaches suffer the important drawback that they cannot take the symmetry of non-noise trajectories into account. That is, if a trajectory has a symmetric equivalent that covers the same trip in the reverse direction, it should become more likely that neither of them is labelled as noise. We present a constraint model that takes this knowledge into account to produce better clusters. We show the efficacy of our approach on real-world data that was previously processed using standard data mining techniques.
JF - Principles and Practice of Constraint Programming
PB - Springer International Publishing
CY - Cork
ER -