Measuring business process similarity plays an important role in the analysis, management and optimization of business in big companies. In the early days, experts paid major attention to calculating business similarity according to corresponding process models. However, models only express ideal behavior of business processes without any undesired or unexpected business routines. In order to fully model business behavior, some researchers use system logs in similarity measuring. But previous system-logs-based similarity measurements have limitations on: (1) satisfaction of algorithm properties, (2) distribution of similarity values, and (3) complexity of algorithm. In this article, we take the advantages of token logs in process behavioral similarity measuring. Firstly, the Token Log Profile, modeled with a relation matrix, is defined as an abstraction of the initial token logs. Then, similarity between business processes is calculated based on their Token Log Profiles according to the proposed algorithm. Besides, we extend the properties that similarity algorithms should satisfy for evaluating the proposed algorithm. The experimental and analytical results show that our algorithm achieves very promising accuracy and efficiency while satisfying all the proposed properties compared with state-of-the-art algorithms.

Mobile edge computing as a novel computing paradigm brings remote cloud resource to the edge servers nearby mobile users. Within one-hop communication range of mobile users, a number of edge servers equipped with enormous computation and storage resources are deployed. Mobile users can offload their partial or all computation tasks of a workflow application to the edge servers, thereby significantly reducing the completion time of the workflow application. However, due to the open nature of mobile edge computing environment, these tasks, offloaded to the edge servers, are susceptible to be intentionally overheard or tampered by malicious attackers. In addition, the edge computing environment is dynamical and time-variant, which results in the fact that the existing quasistatic workflow application scheduling scheme cannot be applied to the workflow scheduling problem in dynamical mobile edge computing with malicious attacks. To address these two problems, this paper formulates the workflow scheduling problem with risk probability constraint in the dynamic edge computing environment with malicious attacks to be a Markov Decision Process (MDP). To solve this problem, this paper designs a reinforcement learning-based security-aware workflow scheduling (SAWS) scheme. To demonstrate the effectiveness of our proposed SAWS scheme, this paper compares SAWS with MSAWS, AWM, Greedy, and HEFT baseline algorithms in terms of different performance parameters including risk probability, security service, and risk coefficient. The extensive experiments results show that, compared with the four baseline algorithms in workflows of different scales, the SAWS strategy can achieve better execution efficiency while satisfying the risk probability constraints.

Cyber–physical system (CPS) is a complex system that contains multiple components working cooperatively. According to its characteristics, we propose an object-oriented generalized stochastic Petri net (OGSPN), in which the CPS is abstracted into several types of objects and its logical structure and working process is visually described. Moreover, we model and measure the time consumed by each activity in CPS for quantitative analysis. To simplify the process of performance analysis on this model, in this article we propose a compression algorithm to convert OGSPN into a generalized stochastic Petri net (GSPN). Considering the uncertainty in CPS, we use a fuzzy mathematics based method to process the compressed model of GSPN for improving the accuracy of the performance analysis. We apply our method to a real-world thick metal plate production line in a manufacturing company, and the availability of our method is verified by extensive experiments.

The applications of action recognition in real-world scenarios are challenging. Although state-of-the-art methods have demonstrated good performance on large scale datasets, we still face complex practical problems and inappropriate models. In this work, we propose a novel local image directed graph neural network (LI-DGNN) to solve a real-world production scenario problem which is the completeness identification of accessories during the range hood packing process in a kitchen appliance manufacturing workshop. LI-DGNN integrates skeleton-based action recognition and local image classification to make good use of both human skeleton data and appearance information for action recognition. The experimental results demonstrate the high recognition accuracy and good generalization ability on the range hood packing dataset (RHPD) which is generated in the industrial packing process. The results can meet the recognition requirements in the actual industrial production process.

Mobile edge computing (MEC) enables to provide relatively rich computing resources in close proximity to mobile users, which enables resource-limited mobile devices to offload workloads to nearby edge servers, and thereby greatly reducing the processing delay of various mobile applications and the energy consumption of mobile devices. Despite its advantages, when a large number of mobile users simultaneously offloads their computation tasks to an edge server, due to the limited computation and communication resources of edge server, inefficiency resource allocation will not make full use of the limited resource and cause waste of resource, resulting in low system performance (the weighted sum of the number of processed tasks, the number of punished tasks, and the number of dropped tasks). Therefore, it is a challenging problem to effectively allocate the computing and communication resources to multiple mobile users. To cope with this problem, we propose a performance-aware resource allocation (PARA) scheme, the goal of which is to maximize the long-term system performance. More specifically, we first build the multiuser resource allocation architecture for computing workloads and transmitting result data to mobile devices. Then, we formulate the multiuser resource allocation problem as a Markova Decision Process (MDP). To achieve this problem, a performance-aware resource allocation (PARA) scheme based on a deep deterministic policy gradient (DDPG) is adopted to derive optimal resource allocation policy. Finally, extensive simulation experiments demonstrate the effectiveness of the PARA scheme.

With the continuous expansion of the software market and the updating of the maturity of the software development process, the performance requirements of software users are becoming increasingly prominent. Performance issues are essentially related to the source code. For solving the same problem, different programmers may write completely different "correct" code with the same functionality but have different performance. Most online judge system on programming make use of automated grading systems, usually rely on test results to quantify the correctness and performance for the submitted source code. However, traditional dynamic testing takes a lot of time, and the discovery of performance problems is usually after the fact even for those small scale programs. Therefore, we proposed DeepTLE which is used to effectively predict the performance of submitted source code before it runs. DeepTLE can automatically learn the semantic and structural features of the source code. In order to verify the effect of our approach, we applied it to the source code collected from the program competition website to predict if the source code would be time limit exceed or not without running its test cases. Experiment results show that our method can save 96% of the time cost compared to the dynamic testing, and the accuracy of the prediction reaches 82%.

Due to the big volume of data and complex execution, event logs of business processes inevitably contain various errors. In the field of process mining, if we derive process models from the event data without repairing, it is very likely that the resulting process is extremely different from what we expect. Current methods of repairing logs generally compare the log with an existing reference model to seek an optimal alignment, which requires that there should be a reliable reference model. Therefore, this paper presents an approach which only refers to the log itself to repair mistaken traces. We identify loop structures and frequent event sequences (sound conditions) between certain events. For each trace, basic trace and loop events are separated in advance. The basic trace is split into several parts to get repaired one by one according to sound conditions. Then loop events are added back and checked according to corresponding loop structure we discover. The repaired log should be as clean as possible and as similar to the original log as possible so that correctness and integrity of the original log are guaranteed. Experimental results based on different logs prove that our approach is effective and efficient.

With the continuous expansion of software market and the updating of the maturity of the software development process, the performance requirements of software users have gradually become prominent. Performance issues are closely related to the source code. Thus, with the increasing complex of the software product, its performance changed during the evolution of the software product. Performance optimization related work has always been an important goal for developers who usually coding at a low-level. However, performance problems are well studied on architecture level. All too often, some developers are ignorant of the way their code modifications affect performance and simply to wait until performance drops to a point that is unacceptable to the business side. As software developers did a lot of daily work at code level, we think code level performance awareness can help developers in sight of the performance of the code that they are working with. To deal with this, we firstly build performance-aware code change model to identify the performance changes and its related code changes at the granularity of function between each two reversions of a program. Then, we analyzed the evolution history of the code performance and mined the frequent code change patterns that used to improve performance. We have build related tool to implement the proposed approach and applied it to 8 open source projects.

The rapid development of web service provides many opportunities for companies to migrate their business processes to the Internet for wider accessibility and higher collaboration efficiency. However, the open, dynamic and ever-changing Internet also brings challenges in protecting these business processes. There are certain process monitoring methods and the recently proposed ones are based on state changes of process artifacts or places, however, they do not mention defending process interactions from outer tampering, where events could not be detected by process systems, or saving fault-handling time. In this paper, we propose a novel Token-based Interaction Monitoring framework based on token carried data to safeguard process collaboration and reduce problem solving time. Token is a more common data entity in processes than process artifacts and they cover all tasks' executions. Comparing to detecting places' state change, we set security checking points at both when tokens are just produced and to be consumed. This will ensure that even if data is tampered after being created it would be detected before being used. For applying monitoring framework, we develop a collaboration constructing method with token-based process mining techniques to derive global interaction processes as well as organize historical process data in forms of token.

With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device’s energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment.

Cloud computing has become a revolutionary paradigm by provisioning on-demand and low cost computing resources for customers. As a result, scientific workflow, which is the big data application, is increasingly prone to adopt cloud computing resources. However, internal failure (host fault) is inevitable in such large distributed computing environment. It is also well studied that cloud data center will experience malicious attacks frequently. Hence, external failure (failure by malicious attack) should also be considered when executing scientific workflows in cloud. In this paper, a fault-tolerant scheduling (FTS) algorithm is proposed for scientific workflow in cloud computing environment, the aim of which is to minimize the workflow cost with the deadline constraint even in the presence of internal and external failures. The FTS algorithm, based on tasks replication method, is one of the widely used fault tolerant mechanisms. The experimental results in terms of real-world scientific workflow applications demonstrate the effectiveness and practicality of our proposed algorithm.

Cloud computing is a suitable platform to execute the deadline-constrained scientific workflows which are typical big data applications and often require many hours to finish. Moreover, the problem of energy consumption has become one of the major concerns in clouds. In this paper, we present a cost and energy aware scheduling (CEAS) algorithm for cloud scheduler to minimize the execution cost of workflow and reduce the energy consumption while meeting the deadline constraint. The CEAS algorithm consists of five sub-algorithms. First, we use the VM selection algorithm which applies the concept of cost utility to map tasks to their optimal virtual machine (VM) types by the sub-makespan constraint. Then, two tasks merging methods are employed to reduce execution cost and energy consumption of workflow. Further, In order to reuse the idle VM instances which have been leased, the VM reuse policy is also proposed. Finally, the scheme of slack time reclamation is utilized to save energy of leased VM instances. According to the time complexity analysis, we conclude that the time complexity of each sub-algorithm is polynomial. The CEAS algorithm is evaluated using Cloudsim and four real-world scientific workflow applications, which demonstrates that it outperforms the related well-known approaches.

In the case of a traditional court judge, the facts are based on the law as the cornerstone, the fact that can be proved by the legal evidences. As we all know, assisting judges to manage evidence chain information can significantly improve the efficiency and quality of judges. Therefore, based on this idea, this paper will introduce the design and implementation of Visual Modeling Tool for evidence chain. The tool can help the judge to build various types of evidence chain, and can help to improve the work efficiency of judges. This visual modeling tool is divided into two main forms of visualization, includes the Graphical Mode and Table Mode. It means the same data with different display forms. So that the judge can deal with a large number of complex and varied evidence of chain information quickly and easily. Also, the efficiency of the judge to handle the case can be significantly improved.

This paper presents a method of the association statistics between the cause of action and the statute. According to the close relationship between the cause of action and the statute in the written judgment, this paper puts forward the statistical analysis of the cause of action and the statute. The method mainly includes the pretreatment of semi-structured written judgments, reading information of the cause of action and the statute from structured documents, standardizing statutes, depositing in the database, generating EXCEL form of the association statistics from the cause of action to the statue and generating TXT form of the association statistics from the statue to the cause of action. In the process of reasoning and assessment, we can achieve the prediction of statutes and narrow the size of the cause of action.

The traditional approach to measure text similarity is based on the TF-IDF algorithm to get the document vector, and then use the cosine similarity algorithm to calculate the text similarity. However, this method of statistical way ignores the potential semantics of the articles or words. By some means, this method only aims at the word itself. But with the Latent Semantic Analysis, the semantic space is added on the basis of calculate TF-IDF. Each word and document can have a position in semantic space by Singular Value Decomposition. That allows the semantic analysis, document clustering, and the relationship between semantic class and document class can be finished at the same time. Here, we summarize the text similarity measures, and gradually extend to the Latent Semantic Analysis. The experiment shows that the statutes predicted by LSA are more accurate than that only by TF-IDF.

Judgment documents contain a wealth of valuable information. The original judgment documents are written in pure text format, so we cannot obtain information directly, which hinders the study of the judgment documents. We propose an approach to parse Chinese judgment documents into structured documents to solve this problem. Divide a judgment document into logical segments, and then extract and label information items from these logical segments. Use information items to build analytic document information model and the model is output into a structured XML document.