Varied & Bespoke Needs of Caregivers: Organizing and Communicating Diabetes Care for Children in Era of DIY

Type 1 diabetes in children is a complex chronic condition that requires high levels of coordination among a range of caregivers in order to communicate and organize care for best health outcomes. We examined three types of care, at home, at school, and at the clinic, to explore the communication needs of a range of caregivers. Not only were there differences between types of caregiver, but there were also varied personal preferences for communication, different levels of knowledge regarding caregiving that required different forms of communication, and changes in child health leading to different care needs and communication styles. We frame these findings within the new trend of diabetes technologies that allow for cloud connected communication in order to show the need to respect varied and individual communication practices. Technologies that link current health data over the cloud may not have a one-size-fits-all solution for all caregivers, however, open-source DIY health trends may be a way towards supporting personalized communication needs

What is Business Process Automation Anyway?

Many organizations strive to increase the level of automation in their business processes. While automation historically was mainly concerned with automating physical labor, current automation efforts mostly focus on automation in a digital manner, thus targeting work that is related to the interaction between humans and computers. This type of automation, commonly referred to as business process automation, has many facets. Yet, academic literature mainly focuses on Robotic Process Automation, a specific automation capability. Recognizing that leading vendors offer automation capabilities going way beyond that, we use this paper to develop a detailed understanding of business process automation in industry. To this end, we conduct a structured market analysis of the 18 predominant vendors of business process automation solutions as identified by Gartner. As a result, we provide a comprehensive overview of the business process automation capabilities currently offered by industrial vendors. We show which types and facets of automation exist and which aspects represent promising directions for the future

What is Business Process Automation Anyway?

Many organizations strive to increase the level of automation in their business processes. While automation historically was mainly concerned with automating physical labor, current automation efforts mostly focus on automation in a digital manner, thus targeting work that is related to the interaction between humans and computers. This type of automation, commonly referred to as business process automation, has many facets. Yet, academic literature mainly focuses on Robotic Process Automation, a specific automation capability. Recognizing that leading vendors offer automation capabilities going way beyond that, we use this paper to develop a detailed understanding of business process automation in industry. To this end, we conduct a structured market analysis of the 18 predominant vendors of business process automation solutions as identified by Gartner. As a result, we provide a comprehensive overview of the business process automation capabilities currently offered by industrial vendors. We show which types and facets of automation exist and which aspects represent promising directions for the future

Trace Clustering for User Behavior Mining

Business information systems support a large variety of business processes and tasks, yet organizations rarely understand how users interact with these systems. User Behavior Mining aims to address this by applying process mining techniques to UI logs, i.e., detailed records of interactions with a system\u27s user interface. Insights gained from this type of data hold great potential for usability engineering and task automation, but the complexity of UI logs can make them challenging to analyze. In this paper, we explore trace clustering as a means to structure UI logs and reduce this complexity. In particular, we apply different trace clustering approaches to a real-life UI log and show that the cluster-level process models reveal useful information about user behavior. At the same time, we find configurations in which trace clustering fails to generate satisfactory partitions. Our results also demonstrate that recently proposed representation learning techniques for process traces can be effectively employed in a realistic setting