Summary The rapidly growing information technologies such as cloud computing, edge computing, artificial intelligence offer cutting‐edge technical means for virtual reality of industrial manufacturing process. An efficient and accurate method for situation awareness (SA) to monitor the process of marginal layer plays an essential role to enhance the safety of industrial equipment. In this study, a manufacturing life cycle is described and characterized as operation and remote controlling of industrial machineries. The edge servers are first employed to track physical devices. Then a novel unified adaptive deep classification framework for SA with semi‐supervised classification method is developed to find unknown patterns in a huge amount of industrial data. Furthermore, a robust model based on the selection of adaptive local neighbor is proposed to find out an optimal weight and high similarity of neighbors. The combination of the proposed framework and model can realize real‐time SA of modern manufacturing the execution system. Plentiful experiments have been conducted to demonstrate the high accuracy and reliability of the proposed framework.

With the popularity and application of cloud computing, edge computing, artificial intelligence and other information technologies, the virtual realization of physical world for industrial manufacturing process has become possible and played a very big role. It is urgent to find an efficient and accurate method for industrial equipment safety by situation awareness (SA) to monitor the process. In this paper, a whole life cycle of industry has been described and characterized for the operation and control of modern industrial remote control machinery. And the edge servers are employed for the operation process of tracking physical devices. Then a novel framework of graph convolutional networks (GCN) for semi-supervised classification method is proposed to find unknown patterns in huge amounts of industrial data, which can obtain real-time situation awareness for the execution of modern industry. Plentiful experiments have been conducted to show higher accuracy and stability of our framework.

With the rapid development of e-business, large volume of business processes need to be handled in a constrained time. There is always a security issue related to on-time completion in many applications in the economic fields. So, how to effectively manage and organize business processes became very important. By using cloud computing, instance-intensive processes can be handled more effectively by applying just-right virtual machines. Hence, the management of cloud resources became an important issue that many researchers focus on to fully utilize the advantage of cloud. In this paper, we mainly discuss the queuing theory and put forward our novel dynamic process scheduling model based on queuing theory, which is named M/G/k/l- P for business processes. This model can solve the issue of allocating appropriate number of cloud resources based on the number of tasks and execution stages to ensure whether the numbers of cloud resources are sufficient and adequate or not, which can improve the security issue for business process. The service discipline in our model can provide a dynamic process by setting different priorities to improve the experience of users. Evaluations prove that the queuing model of M/G/k/l- P can work very well for business workflow scheduling.

Due to some datasets may acquire a large amount of information to train the annotation model, but some datasets may lack sufficient information, multi-label transfer learning has attracted a lot of attention recently. The correlation between inherent class label among multiple labels is integrated into data structure and can cover the relationship of samples. However, it is usually not accurate enough. In order to use their advantages and eliminate their weaknesses, this paper presents a new framework which integrates the techniques of Green's function inspired label correlation and label covariance analysis. Meanwhile, the framework adopts low-rank based method to learn the groups clearly. At the same time, due to the edge of annotation system be very difficult to determine, our framework is designed to carry out the appropriate hyperplane margin for multi-label datasets. In this framework, label covariance is first utilized by perceptron criterion analyzer, then linear discriminant function can dispose the unlabeled part of multi-label data easily. In addition, a low-rank based margin mechanism is presented to optimize the performance of our framework. We evaluate our framework in the performance of label correlation and covariance builders for transfer annotation. The derived results are encouraging and we achieve about 5% improvement on average accuracy.

Understanding the mechanism of people behavior is very important in the study of social networks. Social media has generated massive data, which brings about opportunities and challenges to discovering collective behavior in social networks which contains plentiful features. In this paper, we propose to predict users' collective behavior based on the analysis of some users' information in a social network. Specifically, given some behavior knowledge of people in a social network, we intend to infer other people's knowledge in the same network whose behavior is unknown. As we know, social network with colossal size contains millions of user nodes. The dimension of networks challenges approaches of inference learning for collective behavior prediction. How to efficiently extract meaningful knowledge from large-scale data is very important. To address this issue, we propose a method which directly learns the behavior with the exact number of clusters in network. This novel method learns a similarity matrix from the original graph structure so that a subsequent clustering procedure can be directly applied to it. Experimental results on four datasets present the effectiveness of this distinct inference approach.

The utilization of cloud computing resources contributes to a fast growth of e-business and e-government where businesses and government agencies often require processing a large volume of service requests in a constrained period of time. In order to achieve on-time completion of a large volume of time-constrained parallel processes, this paper proposes a novel dynamic checkpoint selection strategy (CSSDM) for monitoring cloud business workflows. CSSDMmonitors the execution time points rather than activities along the business workflows. The paper also provides some parameters that can track the execution process of each virtual machine. This mechanism significantly increases the efficiency in monitoring large volume processes so as to improve the quality of service in both e-business and e-government sectors. Simulation results show that CSSDMhas excellent performance in different scenarios even when certain amounts of noises are added.

The rapid development of cloud computing service allows scientific research data to be easily moved into cloud. Users pay for computational and storing resources to deal with their data in the cloud therefore how to manage those resources has become an urgent issue to be solved. Large application data need to be handled every day, thus a good storage strategy can save monetary cost as well as improve computational performance. In order to satisfy different demands of users, various storage strategies have been proposed. Aiming at proposing an optimized strategy, the paper constructs data importance and time importance models to evaluate data sets, which makes strategy by choosing appropriate data sets to store based on outcomes. Combining these two models, our strategy performances well both in monetary cost and computational performance. Various experiments show excellent results that the strategy performs better than normal storage strategies in both computational performance and monetary cost. The time complexity of our strategy is O(n2).

The rapid development of cloud computing service allows data and computation intensive applications to be easily moved into cloud. Users pay for computing and storing resources to deal with their data in the cloud, therefore how to manage those resources has become an important issue. In order to satisfy different demands of users, various storage strategies have been proposed. Aiming at presenting a cost-effective and computation-efficient strategy, the paper considers both the monetary cost and response time to decide whether a generated data set should be stored or not based on the proposed data importance model. The main focus of this model is to compare the attributes and relationships between data sets. To better illustrate the effectiveness and efficiency of our strategy, both theoretical analysis and simulations are conducted in this paper. Various experiments show excellent results that our strategy outperforms its counterparts in both cost and computation.

Summary Utilization of cloud computing resources has made a fast growth in e‐business. Business and government agencies often need to handle large volume of service requests, the so‐called instance‐intensive business processes in a constrained period. On‐time completion for instance‐intensive business processes within the constrained time is a very important issue. In the past few years, traditional optimal task scheduling has been well researched and proven to be a nondeterministic polynomial (NP) time–complete problem. So many heuristic and metaheuristic algorithms are put forward to solve the issue with near‐optimal solutions. However, most of them just treat a single workflow instance as a multistep task without considering that steps within a task can be different types of activities. To explain multistep features of business workflows, a typical motivating instance‐intensive business example of security exchange and a multistep scheduling model for business workflows are introduced in this paper. Then our near‐optimal dynamic priority scheduling (DPS) strategy is proposed on the basis of the idea of Min‐Min heuristic algorithm and greedy philosophy. Compared to the first come first served and constrained Min‐Min by makespan and standard deviation, DPS can make a more optimized choice in each round of scheduling towards overall outcome. To show the effectiveness of DPS, theoretical minimum execution time (METtheory) is used as a benchmark for evaluation based on simulation. The results show that the ratios between METtheory and DPS are more than 98.5% by scheduling different orders of magnitude tasks from 1000 to 1 000 000. In particular, the ratio between METtheory and DPS is nearly 99.9% with 1 000 000 tasks, which means that our DPS can get the near‐optimal result when scheduling large number of tasks.

Due to fast development of e-commerce, logistics, network and cloud computing, many businesses have changed their traditional production and sale patterns. This brings big opportunities and challenges to logistics. Among the process of logistics, delivery is a key issue. This paper mainly focuses on logistics scheduling based on cloud business workflows. A constrained Dijkstra algorithm is proposed to select an optimal route for logistics transportation with various parameters. We then put forward a distribution logistics scheduling algorithm to solve tracking large numbers of expresses based on business workflows. The simulation indicates the effectiveness of our two novel algorithms.

Staff assignment is a very important task in the research of workflow resource management. Currently, many well-known workflow applications still rely on human resource assigners such as process initiator or process monitor to perform staff assignment task. In this paper, we propose a semi-automatic workflow staff assignment method which can decrease the workload of staff assigner based on a novel semi-supervised machine learning framework. Our method can be applied to learn all kinds of activities that each actor is capable of based on the workflow event log. After we have learned all labeled data, we can suggest a suitable actor to undertake the specified activities when a new process is assigned. With the proposed method, we can get an average prediction accuracy of 97% and 91% on the data sets of two manufacturing enterprise applications respectively.

Lasso-type variable selection has increasingly expanded its machine learning applications. In this paper, uncorrelated Lasso is proposed for variable selection, where variable de-correlation is considered simultaneously with variable selection, so that selected variables are uncorrelated as much as possible. An effective iterative algorithm, with the proof of convergence, is presented to solve the sparse optimization problem. Experiments on benchmark data sets show that the proposed method has better classification performance than many state-of-the-art variable selection methods.