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    A framework for sustainable and data-driven smart campus
    (Scitepress, 2020) Köstepen, Zeynep Nur; Akkol, Ekin; Doğan, Onur; Bitim, Semih; Hızıroğlu, Abdülkadir
    As small cities, university campuses contain many opportunities for smart city applications to increase service quality and use of public resources efficiency. Enabling technologies for Industry 4.0 play an important role in the goal of building a smart campus. The study contributes to the digital transformation process of.Izmir Bakircay University which is a newly established university in Turkey. The aim of the study is to plan a road map for establishing a smart and sustainable campus. A framework including an architectural structure and the application process, for the development of a smart campus have been revealed in the study. The system application is designed to be 3 stages. The system, which is planned to be built on the existing information systems of the university, includes data collection from sensors and data processing to support the management processes. The proposed framework expects to support some value-added operations such as increasing personnel productivity, increasing the quality of classroom training, reducing energy consumption, accelerating interpersonal communication and finding the fastest solution to the problems on campus. Therefore, not only a smart campus but also a system is designed for sustainability and maximum benefit from the facilities.
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    Understanding patient activity patterns in smart homes with process mining
    (Springer International Publishing Ag, 2022) Doğan, Onur; Akkol, Ekin; Oluçoğlu, Müge
    Especially in people over 50 years of age, sedentary lifestyle can cause muscle loss called sarcopenia. Inactivity causes undesirable outcomes such as excessive weight gain and muscle loss. Weight gain can lead to a variety of problems, including deteriorating of the musculoskeletal system, joint problems, and sleep problems. In order to provide better service, it can be beneficial to understand human behavior in terms of health services. Process mining, which can be considered a part of knowledge graphs, is a crucial methodology for process improvement since it offers a model of the process that can be analyzed and optimized. This study uses process mining approaches to examine data from three patient that were collected using indoor location sensors, allowing the collection of flows of human behavior in the home. The analyses indicated how much time was spent by the patients of the house in each room during the day as well as how frequently they occurred. The movement of patients from room to room was observed daily and subjected to a variety of analyses. With the help of user pathways, lengths of stay in the rooms, and frequency of presence, it has been possible to reveal the details of daily human behavior. Inferences about the habits of the participants were revealed day by day.