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.
Highlights
SOcial Workflows (SOW) coordinate the activities carried out by a group of users [15] who either individually or in cooperation try to achieve a certain objective
Independent of the results shown in the table, the main conclusion of this experiment is that the master-worker architecture can be deployed in a cloud environment by applying different configurations and that it is a good option to accelerate the location algorithms proposed in the paper
We have presented a method to estimate the activity patterns associated with customers in open malls
Summary
SOcial Workflows (SOW) coordinate the activities carried out by a group of users [15] who either individually or in cooperation try to achieve a certain objective. We present the complete system implemented in the project BAI4SOW, which enhances the proposal made in [26] This system consists of a localization system to locate the customers and identify the activities that they are carrying out, and of the application of process mining and natural language generation techniques to this information in order to analyze the behavior of the customers to extract insights and linguistic reports from it. We present the results obtained in BAI4SOW project, whose objective is in the development of both process mining algorithms for the analysis of SOW containing geolocated activities, and automatic linguistic reports that can be helpful for marketing campaign design The rest of this manuscript is organized as follows.
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