Automatic linguistic reporting of customer activity patterns in open malls

Abstract

In this work, we present a complete system to produce an automatic linguistic reporting about the customer activity patterns inside open malls, a mixed distribution of classical malls joined with the shops on the street. These reports can assist to design marketing campaigns by means of identifying the best places to catch the attention of customers. Activity patterns are estimated with process mining techniques and the key information of localization. Localization is obtained with a parallelized solution based on WiFi fingerprint system to speed up the solution. In agreement with the best practices for human evaluation of natural language generation systems, the linguistic quality of the generated report was evaluated by 41 experts who filled in an online questionnaire. Results are encouraging, since the average global score of the linguistic quality dimension is 6.17 (0.76 of standard deviation) in a 7-point Likert scale. This expresses a high degree of satisfaction of the generated reports and validates the adequacy of automatic natural language textual reports as a complementary tool to process model visualization.

Information-seeking dialogue for explainable artificial intelligence: Modelling and analytics

“…Process mining is the subfield of data science that aims to provide tools for discovering insights into operational processes and thus supports process improvements [76]. Following the process mining terminology [50], an instance of a process (i.e., a specific explanatory dialogue) is denoted as a trace τ . Subsequently, each trace consists of the set of activities A (in this case, locutions).…”