Enabling Configurable Workflows in Smart Environments with Knowledge-based Process Fragment Reuse

In today’s smart environments, the serviceli-zation of various resources has produced a tremendous number of IoT- and cloud-based smart services. Thanks to the pivotal role of pillar paradigms, such as edge/cloud computing, Internet of Things, and business process management, it is now possible to combine and translate these service-like resources into configurable workflows, to cope with users’ complex needs. Examples include treatment workflows in smart healthcare, delivery plans in drone-based missions, transportation plans in smart urban networks, etc. Rather than composing atomic services to obtain these workflows, reusing existing process fragments has several advantages, mainly the fast, secure, and configurable compositions. However, reusing smart process fragments has not yet been addressed in the context of smart environments. In addition, existing solutions in smart environments suffer from the complexity (e.g., multi-modal transportation in smart mobility) and privacy issues caused by the heterogeneity (e.g., package delivery in smart economy) of aggregated services. Moreover, these services may be conflicting in specific domains (e.g. medication/treatment workflows in smart healthcare), and may affect user experience. To solve the above issues, the present paper aims to accelerate the process of generating configurable treatment workflows w.r.t. the users’ requirements and their smart environment specificity. We exploit the principles of software reuse to map each sub-request into smart process fragments, which we combine using Cocke-Kasami-Younger (CKY) method, to finally obtain the suitable workflow. This contribution is preceded by a knowledge graph modeling of smart environments in terms of available services, process fragments, as well as their dependencies. The built information network is, then, managed using a graph representation learning method, in order to facilitate its processing and composing high-quality smart services. Experimental results on a real-world dataset proved the effectiveness of our approach, compared to existing solutions.