In today’s ever-changing business environment, enterprises face numerous challenges, including rapid changes in customer needs, intense market competition, and swift technological advancements. Consequently, business process modeling has emerged as a pivotal strategy to tackle these challenges, optimize operations, and enhance efficiency. Traditionally, manual modeling required participants to possess domain knowledge and modeling skills, rendering the process highly complex and prone to errors. To address this, business process recommendation techniques have been introduced to effectively assist business process modeling by leveraging node information from business process repositories. However, existing research on process recommendation often overlooks crucial factors such as nodetype information and fails to integrate actual execution log data, resulting in suboptimal recommendation results. In this study, we consider the node-type information contained in the process to design the edge expansion model and combine it with the log information generated during the actual execution of the process to calculate the confidence level comprehensively and as the basis for the recommendation to conduct offline mining, the results of mining to assist business process analysts in completing the process modeling. Furthermore, we perform a comparative analysis between our proposed algorithm and mainstream algorithms in process recommendation, utilizing a real dataset. The results demonstrate the superiority of our method in terms of precision and branch structure recommendation.

Each activity in the service workflow interacts with services as required to meet complex business needs and quickly adapt to market changes. The design of each activity's input/output interface parameters influences whether it can successfully map to appropriate and interactive services. In practice, suitable activity interface parameters should possess 3-features:$realism$,$relevance$, and$compatibility$, as popular parameters originating from the real world and closely related to activity semantics are apt to match user-expected services. However, existing research requires expert specification or ontology-based inference, resulting in outdated, inconsistent parameters that lack necessary elements, making it challenging to match expected services. Therefore, we propose an automated method combining Transformer and weighted HITS to recommend interface parameters with 3-features on activity function requirement. It filters similar Endpoints (EPs) based on the activity's semantics by supervised Transformer-based learning of multi-domain APIs and unsupervised EPs matching. Next, a node-weighted heterogeneous graph is built based on similar EPs and their interface parameter relationships. We then apply a node-weighted HITS to explore mutual gain relationships within the graph and calculate parameter compatibilities. Finally, a top-$k$non-redundant compatible parameter list and corresponding different formats are recommended for the activity. The method's effectiveness and efficiency are verified using a real API service dataset from RapidAPI.

With the rapid development of manufacturing and cloud computing, more and more emerged cloud services provide a promising way to perform complex requirements efficiently. Workflow offers an effective way to assemble disparate services and interacts with them to construct business logic for user requests. Meanwhile, workflow engines are responsible for the control of workflow execution. However, existing engines usually support interaction between workflows and services or computing resources by tight binding approaches, which lack flexibility and scalability. Therefore, a flexible and decoupled architecture is essential to support automatic workflow management. To fill this gap, this paper proposes a novelservice-centered cloud-native workflow architecture- SCAFE. The introduction of the service layer in SCAFE decouples the upper business services and lower execution resources, facilitating the independent and joint management of three execution objects (request - service - execution instance) involved in the cloud workflow lifecycle. We present a 2-stage scheduling model for the new architecture to support customized service optimization and resource-aware task scheduling. In addition, a fault-tolerant mechanism is integrated into resolving task execution exceptions quickly. We have successfully implemented a prototype tool to verify its flexibility and crucial functions, thus advancing the field workflow engines in cloud-native environments.

The applications of mobile devices are increasingly becoming computationally intensive while the computing capability of the user’s mobile device is limited. Traditional approaches offload the tasks of mobile applications to the remote cloud. However, the rapid growth of mobile devices has made it a challenge for the remote cloud to provide computing and storage capacities with low communication delays due to the fact that the remote cloud is geographically far away from mobile devices. Reducing the completion time of applications in mobile devices through the technical expending mobile cloudlets which are moving collocated with Access Points (APs) is necessary. To address the above issues, this paper proposes EACP-CA (Enhanced Adaptive Cloudlets Placement approach based on Covering Algorithm), an enhanced adaptive cloudlet placement approach for mobile applications in a given network area. We apply the CA (Covering Algorithm) to adaptively cluster the mobile devices based on their geographical locations, the aggregation regions of the mobile devices are identified, and the cloudlet destination locations are also confirmed according to the clustering centers. In addition, we can also obtain the traces between the original and destination locations of these mobile cloudlets. To increase the efficiency, we parallelize CA on Spark. Extensive experiments show that the proposed approach outperforms the existing approach in both effectiveness and efficiency.