Abstract Cloud computing is one of the emerging technologies in computer science in which services are provided through the internet on‐demand. Workflow scheduling is considered to be an NP‐hard problem and has a significant issue in the cloud environment. Finding the polynomial‐time solutions for workflow scheduling problem is difficult with most of the existing algorithms designed for traditional computing platforms. Some existing meta‐heuristics algorithms proposed for workflow scheduling problem are stuck in the local optimal solution and fails to give the global optimal solution. In this article, a hybrid of particle swarm optimization and gray wolf optimization, named the PSO‐GWO algorithm, is proposed for workflow scheduling. The proposed algorithm was tested to reduce the total executing cost (TEC) and total execution time (TET) of the dependent tasks in the cloud computing environment. The proposed algorithm takes advantage of both the standard PSO and GWO algorithms and does not stick in the local optimal solution. The experiment results show that the PSO‐GWO outperformed compared with the standard PSO and GWO algorithm in TEC and TET.

In the insurance industry, timely and effective interaction with customers are at the core of everyday operations and processes that are key for a satisfactory customer experience. These interactions often result in sequences of data derived from events that occur over time. Such recurrent patterns can provide valuable information that can be used in a variety of ways to improve customer related work-flows. In this paper we demonstrate the application of a recently proposed algorithm to uncover such time patterns that takes into account the time between events to form such patterns. We use temporal customer data generated from two different use-cases (satisfaction and fraud) to show that this algorithm successfully detects patterns that occur in the insurance context.