Matheuristics to solve the Traveling Analyst Problem
Résumé
Exploratory Data Analysis (EDA), the notoriously tedious task of interactively analyzing datasets to gain insights, has attracted a lot of attention lately in the data management community. We recently proposed a formal definition of EDA as a multi-objective optimization problem, coined the Traveling Analyst Problem (TAP) and inspired by the Orienteering Problem (OP). The present work investigates the use of matheuristics to compute near optimal solutions to the TAP. We introduce four heuristics including two Variable Partitioning Local Search matheuristics and two Local Branching matheuristics. We present the results of experiments on realistic instances of various sizes to illustrate their effectiveness.
Origine : Fichiers produits par l'(les) auteur(s)
Licence : CC BY NC - Paternité - Pas d'utilisation commerciale
Licence : CC BY NC - Paternité - Pas d'utilisation commerciale