In the Internet of Things (IoT), the online performance of many online services is determined by their distribution resources, which are connected to many different devices. The expected performance of a resource service primarily depends on the optimal use of the service in satisfying end-to-end quality requirements to support its successful execution. Therefore, the performance of a resource service is dynamic and should be discovered as a benchmark to detect a performance anomaly online. A performance anomaly is referred to as a business anomaly because it depends on its usage. The performance is measured by the quality of service (QoS) that is possessed by a resource service. In this paper, an approach based on the resource service QoS is proposed to detect a business anomaly via mining business process data in collaborative tasks in the IoT. First, a resource-service chain (RSC) is considered to be an analysis object because resource services are employed as a ''service flow'' by a business process. The similarity between any two RSCs is measured according to the QoS indicator values of resource services. Based on the similarity, a clustering algorithm is presented to resolve clustering centers that are considered to be QoS benchmarks. Second, according to the QoS benchmarks of RSCs, the thresholds of QoS indicators of a business anomaly are determined. Third, an algorithm is presented to detect anomalies of the business process. Finally, the proposed approach is illustrated by a simulation experiment. The experimental results show that the approach can be used to effectively detect a business anomaly online.

In distributed cloud manufacturing (CMfg) systems, multi-resource service can complete more complex manufacturing tasks than single resource service. Especially in business process, all the resource services are invoked in a certain sequence, which is called the Resource-Service Chain (RSC). The RSC, as a sequential composition of resource services, expresses the scheduling and the flow of servicing to a distributed business process. A perfect composition can improve utilization ratio and efficient matching availability of resource services greatly. However, most of the existing methods for resource service composition paid no attention to the temporal relationship between resource services. Moreover, the methods strongly depend on relevant element to be considered. Inspired by biological evolution, a Resource-Service Chain Composition Evolutionary (RSCCE) algorithm is proposed. Specifically, RSCCE tries to find multiple optimal solutions, namely all RSCs in a workflow with given constraints. To begin, initial sets of composite resource service are resolved by calculating the degree of dependency between resource services, so as to obtain initial RSCs by workflow. Then, RSCCE algorithm applies genetic algorithm to search for the extended of each initial RSC, a longer chain composing of it, to improve the reuse of RSC. Under this approach, gene and chromosome represent resource service and the entire RSC respectively. If the propagated chromosomes violate the sequence of resource service, as constraint in RSCCE algorithm, they will be repaired to obtain a valid solution. Finally, we take a multi-enterprise collaborative business process as an example to simulate our approach. Experimental results confirm the effectiveness of the approach.