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Öğe Exploration with process mining on how temperature change affects hospital emergency departments(Springer Science and Business Media Deutschland GmbH, 2021) Lull, Juan José; Doğan, Onur; Celda, Angeles; Mandingorra, Jesus; Lemus, Lenin; Pla, Miguel Ángel Mateo; Fernandez-Llatas, CarlosThe way patients are treated in Hospital Emergency Departments changes during the year, depending on many factors. One key component is weather temperature. Some seasonal maladies are tightly related to temperature, such as flu in cold weather or sunburn in hot weather. In this study, data from a hospital in Valencia was used to explore how harsh weather changes affect the emergency department, obtaining information about probable impacts of global warming effects in healthcare systems. Process mining techniques helped in the discovery of changes in the Emergency Departments. Some illnesses, such as heat stroke, are more prevalent during heatwaves, but more interestingly, the time to attend patients is also higher. Rapid changes in temperature are also analyzed through Process Mining techniques. © 2021, Springer Nature Switzerland AG.Öğe Individual behavior modeling with sensors using process mining(MDPI, 2019) Doğan, Onur; Martinez-Millana, Antonio; Rojas, Eric; Sepulveda, Marcos; Munoz-Gama, Jorge; Traver, Vicente; Fernandez-Llatas, CarlosUnderstanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have advanced in the latest years and contributed to unobtrusive human behavior tracking, artificial intelligence has not coped yet with the problem of variability and dynamism of these behaviors. Process mining is an emerging discipline capable of adapting to the nature of high-variate data and extract knowledge to define behavior patterns. In this study, we analyze data from 25 in-house residents acquired with indoor location sensors by means of process mining clustering techniques, which allows obtaining workflows of the human behavior inside the house. Data are clustered by adjusting two variables: the similarity index and the Euclidean distance between workflows. Thereafter, two main models are created: (1) a workflow view to analyze the characteristics of the discovered clusters and the information they reveal about human behavior and (2) a calendar view, in which common behaviors are rendered in the way of a calendar allowing to detect relevant patterns depending on the day of the week and the season of the year. Three representative patients who performed three different behaviors: stable, unstable, and complex behaviors according to the proposed approach are investigated. This approach provides human behavior details in the manner of a workflow model, discovering user paths, frequent transitions between rooms, and the time the user was in each room, in addition to showing the results into the calendar view increases readability and visual attraction of human behaviors, allowing to us detect patterns happening on special days.Öğe Process mining application for analysis of customer’s different visits in a shopping mall(Springer Verlag, 2020) Doğan, Onur; Fernandez-Llatas, Carlos; Öztayşi, BaşarIndoor customers may have different purposes to visit a shopping mall. Understanding the visiting aims results in better customer relationship management. One of the ways to explain the customer purpose is to discover customer paths. Customers mainly visit stores related to their purposes. The main problem is to discover customer paths from paths. Since customers have changeable mood and there are many stores in a shopping mall, customer paths are generally too complex to evaluate. To overcome that problem, we use process mining technique. Process mining is a technique that has some algorithms to discover business processes from event logs in the databases. In this study, we consider the visited stores as an activity in a process. PALIA Suite, a process mining tool that includes several clustering methods for processes, is used to discover and cluster indoor customer paths to evaluate differences among the visits. © 2020, Springer Nature Switzerland AG.Öğe Process-centric customer analytics: Understanding visit purposes of predicted age groups with discovered paths(Old City Publishing Inc, 2020) Doğan, Onur; Öztayşi, Başar; Fernandez-Llatas, CarlosIn a general manner, customer relationship management engages in understanding customer needs and meet them. Most of the investments are either far from customer needs or based on a primitive data collection method. However, customers mainly do not behave with the same ideas to shop in the retail domain. Several studies aim to understand the visiting purposes of customers using various methods. This study seeks to uncover the visit purposes of customers from their paths. Due to customers' unpredictable moods and plenty of stores in the shopping mall, the discovered paths are usually too complicated to analyze. Process mining that can overcome this obstacle is a method that creates process flows from event logs in the databases. In this study, the visited stores were seen as an activity in a business process. PALIA, a discovery algorithm in process mining, was applied to find and cluster customer paths. This study contributes to the literature by examining customer needs from their indoor paths, which were created by the PALIA algorithm. It facilitates to analyze discrepancies among the visits for the same customer. Moreover, the discovered paths are considered according to the age groups predicted by Levenshtein fuzzy kNN (L-FkNN).Öğe Segmentation of indoor customer paths using intuitionistic fuzzy clustering: Process mining visualization(IOS Press, 2020) Doğan, Onur; Öztayşi, Başar; Fernandez-Llatas, CarlosThere are some studies and methods in the literature to understand customer needs and behaviors from the path. However, path analysis has a complex structure because the many customers can follow many different paths. Therefore, clustering methods facilitate the analysis of the customer location data to evaluate customer behaviors. Therefore, we aim to understand customer behavior by clustering their paths. We use an intuitionistic fuzzy c-means clustering (IFCM) algorithm for two-dimensional indoor customer data; case durations and the number of visited locations. Customer location data was collected by Bluetooth-based technology devices from one of the major shopping malls in Istanbul. Firstly, we create customer paths from customer location data by using process mining that is a technique that can be used to increase the understandability of the IFCM results. Moreover, we show with this study that fuzzy methods and process mining technique can be used together to analyze customer paths and gives more understandable results. We also present behavioral changes of some customers who have a different visit by inspecting their clustered paths.
