Estimation of customer activity patterns in open malls by means of combining localization and process mining techniques

In this work we present a method to estimate the activity patterns made by shoppers in open malls based on localization information and process mining techniques. We present our smart phone application for logging information from sensors and a process mining system to discover what kind of activity pattern is made by the shoppers based in the key information of the localization which combine data mining, parallelization and monitoring techniques. Our system has been tested in a challenging scenario such as open mall (mixed classical mall and isolated shops in the street) where location is obtained by means of Global Positioning System (GPS) and a WiFi localization system that is helped by a parallel computing method to speed up the whole process. Activity patterns made by shoppers are obtained by process mining techniques which are applied to the data. The process models obtained provide hints that may help mall's managers to take specific actions considering the activity patterns that customers usually perform while in the mall.