On the adoption of blockchain for business process monitoring

Being the blockchain and distributed ledger technologies particularly suitable to create trusted environments where participants do not trust each other, business process management represents a proper setting in which these technologies can be adopted. In this direction, current research work primarily focuses on blockchain-oriented business process design, or on execution engines able to enact processes through smart contracts. Conversely, less attention has been paid to study if and how blockchains can be beneficial to business process monitoring. This work aims to fill this gap by (1) providing a reference architecture for enabling the adoption of blockchain technologies in business process monitoring solutions, (2) defining a set of relevant research challenges derived from this adoption, and (3) discussing the current approaches to address the aforementioned challenges.

     

An OWL-based semantic business process monitoring framework

Process monitoring phase is one of the service-oriented business process (SOBP) lifecycle phases. Traditional process monitoring approaches have been only achieved at the syntactic level of the process monitoring contexts, which causes the communication problems such as ambiguous understandings and divergent interpretations. To solve the problems, the process monitoring should be achieved at the semantic level as well as at the syntax level of the process monitoring context. In order to support semantic monitoring operations, an ontology-based monitoring framework for the SOBP execution is suggested in this paper. The suggested framework combines a BPEL4WS process model with the semantic monitoring context which is expressed with OWL.     

A framework for visually monitoring business process compliance

Any enterprise must ensure that its business processes comply with imposed compliance rules. The latter stem, for example, from corporate guidelines, legal regulations, and best practices. In general, a compliance rule may constrain multiple perspectives of a business process, including behavior (i.e. control flow), data, time, resources, and interactions with business partners. As a particular challenge, compliance cannot be completely ensured at design time, but needs to be continuously monitored during process enactment as well, i.e., it has to be dynamically checked whether compliance rules are satisfied or temporarily/permanently violated. This paper presents a comprehensive framework for visually monitoring business process compliance. As opposed to existing approaches, the framework supports the visual monitoring of all relevant process perspectives based on the extended Compliance Rule Graph (eCRG) language. Furthermore, it not only allows for the detection of violations, but additionally highlights their causes. Finally, the framework assists users in both monitoring business process compliance and ensuring the compliant continuation of running business processes. Overall, the framework provides a fundamental contribution towards the real-time monitoring of compliance in process-driven enterprises.     

Patterns and tools for business process monitoring customization

In a cross-organizational service-based process provisioning scenario, one provider is likely to execute a given business process to serve several customers. Each customer may hold different expectations about the way this process can be monitored. We present a solution allowing the provider to support the requirements of different customers on the monitoring of a given process, i.e., offering them the opportunity to customize the way a process will be monitored. We propose a multi-dimensional classification model of patterns for process monitoring and rules to compose the patterns to design customized monitoring infrastructures. The fit for purpose of the patterns is evaluated empirically, whereas the feasibility of our solution is demonstrated by a tool supporting process monitoring customization adhering to our pattern design and composition methodology.     

Analyzing business process anomalies using autoencoders

Businesses are naturally interested in detecting anomalies in their internal processes, because these can be indicators for fraud and inefficiencies. Within the domain of business intelligence, classic anomaly detection is not very frequently researched. In this paper, we propose a method, using autoencoders, for detecting and analyzing anomalies occurring in the execution of a business process. Our method does not rely on any prior knowledge about the process and can be trained on a noisy dataset already containing the anomalies. We demonstrate its effectiveness by evaluating it on 700 different datasets and testing its performance against three state-of-the-art anomaly detection methods. This paper is an extension of our previous work from 2016 (Nolle et al. in Unsupervised anomaly detection in noisy business process event logs using denoising autoencoders. In: International conference on discovery science, Springer, pp 442–456, 2016). Compared to the original publication we have further refined the approach in terms of performance and conducted an elaborate evaluation on more sophisticated datasets including real-life event logs from the Business Process Intelligence Challenges of 2012 and 2017. In our experiments our approach reached an \(F_1\) score of 0.87, whereas the best unaltered state-of-the-art approach reached an \(F_1\) score of 0.72. Furthermore, our approach can be used to analyze the detected anomalies in terms of which event within one execution of the process causes the anomaly.     

Decentralized collaborative business process execution using blockchain

World Wide Web - Blockchain technology enables several untrustworthy parties to execute inter-organizational business processes in a tamper-proof manner. Existing approaches are based on smart...     

Measures and mechanisms for process monitoring in evolving business networks

The literature on monitoring of cross-organizational processes, executed within business networks, considers monitoring only in the network formation phase, since network establishment determines what can be monitored during process execution. In particular, the impact of evolution in such networks on monitoring is not considered. When a business network evolves, e.g. contracts are introduced, updated, or dropped, or actors join or leave the network, the monitoring requirements of the network actors change as well. As a result, the monitorability of processes in the network may be disrupted. This paper proposes a framework to solve the problem of preserving the monitorability of processes in an evolving business network. We first propose a formal model of business networks, contracts, and monitoring requirements. Then, we model network evolution and the mechanisms to preserve the monitorability of the processes in the network for different types of evolution. In particular, the preservation of monitorability requires the actors in the network to take appropriate actions in case of dependencies between already established contracts, and update their monitoring infrastructure to satisfy the new monitoring requirements introduced by evolution. We also define a set of metrics that can be used for supporting decisions regarding the potential evolution of a business network. A case study in healthcare and the discussion of a prototype implementation show the applicability of our framework in real-world scenarios.     

Analysis and improvement of business process models using spreadsheets

Software in general is thoroughly analyzed before it is released to its users. Business processes often are not – at least not as thoroughly as it could be – before they are released to their users, e.g., employees or software agents. This paper ascribes this practice to the lack of suitable instruments for business process analysts, who design the processes, and aims to provide them with the necessary instruments to allow them to also analyze their processes. We use the spreadsheet paradigm to represent business process analysis tasks, such as writing metrics and assertions, running performance analysis and verification tasks, and reporting on the outcomes, and implement a spreadsheet-based tool for business process analysis. The results of two independent user studies demonstrate the viability of the approach.     

以扩展分区为核心的流程网分析方法

业务流程网是一种用于业务流程建模的新工具。介绍了业务流程网基本概念后,提出一种用于业务流程网状态空间计算的新方法。该方法以自动机研究领域的分区方法为基础,为分区扩展成本信息,从而得到一种扩展分区,进而给出状态空间计算方法,最后证明了：对于有界的业务流程网来说,扩展分区的计算是可终止的,并且最小成本可达问题是可判定的。     

Network-based social coordination of business processes

This paper presents a social coordination approach that addresses the issue of conflicts over resources during business process execution. A business process consists of tasks that persons and/or machines execute. The resources, that business processes require at run-time, are sometimes limited and/or not-renewable. The approach uses a set of social relations that connect tasks/persons/machines together. These relations are the basis of developing specialized networks that capture the interactions during business process execution and are used to recommend corrective actions when conflicts over resources occur. These actions are dependent on the properties of tasks, persons and machines properties which referred to as transactional, activity, and operational, respectively. A system that demonstrates the approach is also discussed.     

核熵成分分析算法及其在化工过程监控中的应用研究

针对化工过程监测数据复杂、非线性等特点,本文将一种新的降维算法一核熵成分分析算法应用到化工过程监控。与其他的多元统计分析方法相比,核熵成分分析算法可以保证数据降维过程中的信息损失最小从而建立更加可靠的统计模型,进而提高故障检测的检出率。与核主成分分析相似,核熵成分分析也是将数据映射到一个高维空间,在高维空间中进行主元分析,不同之处是KECA在选取主元时采用了信息保有量较大的主元,使得数据在降维后的信息损失量更少。本文使用某石化企业的润滑油重质过程的数据测试算法监控效果,核熵成分分析算法的故障检出率为98.2%,比核主成分分析算法(69.706%)要高。实验结果显示,核熵成分分析算法的化工过程监控效果优于核主成分分析算法。     

On the satisfiability of authorization requirements in business process

Satisfiability problem of authorization requirements in business process asks whether there exists an assignment of users to tasks that satisfies all the requirements, and methods were proposed to solve this problem. However, the proposed methods are inefficient in the sense that a step of the methods is searching all the possible assignments, which is time-consuming. This work proposes a method to solve the satisfiability problem of authorization requirements without browsing the assignments space. Our method uses improved separation of duty algebra (ISoDA) to describe a satisfiability problem of qualification requirements and quantification requirements (Separation of Duty and Binding of Duty requirements). Thereafter, ISoDA expressions are reduced into multi-mutual-exclusive expressions. The satisfiabilities of multi-mutual-exclusive expressions are determined by an efficient algorithm proposed in this study. The experiment shows that our method is faster than the state-of-the-art methods.     

Run-time prediction of business process indicators using evolutionary decision rules

Predictive monitoring of business processes is a challenging topic of process mining which is concerned with the prediction of process indicators of running process instances. The main value of predictive monitoring is to provide information in order to take proactive and corrective actions to improve process performance and mitigate risks in real time. In this paper, we present an approach for predictive monitoring based on the use of evolutionary algorithms. Our method provides a novel event window-based encoding and generates a set of decision rules for the run-time prediction of process indicators according to event log properties. These rules can be interpreted by users to extract further insight of the business processes while keeping a high level of accuracy. Furthermore, a full software stack consisting of a tool to support the training phase and a framework that enables the integration of run-time predictions with business process management systems, has been developed. Obtained results show the validity of our proposal for two large real-life datasets: BPI Challenge 2013 and IT Department of Andalusian Health Service (SAS).     

Web-log-driven business activity monitoring

Business process transformation defines a new level of business optimization that manifests as a range of industry-specific initiatives that bring processes, people, and information together to optimize efficiency. This new optimization level is possible because the Web has assumed the role of a common infrastructure. To examine how BPT can optimize an organization's processes, we describe a corporate initiative that was developed within IBM's supply chain organization to transform the import compliance process that supports the company's global logistics. The initiative sought to give IBM greater awareness of regulatory compliance exceptions - information critical to the corporation and its importing partners.     

Prediction of business process durations using non-Markovian stochastic Petri nets

Companies need to efficiently manage their business processes to deliver products and services in time. Therefore, they monitor the progress of individual cases to be able to timely detect undesired deviations and to react accordingly. For example, companies can decide to speed up process execution by raising alerts or by using additional resources, which increases the chance that a certain deadline or service level agreement can be met. Central to such process control is accurate prediction of the remaining time of a case and the estimation of the risk of missing a deadline.To achieve this goal, we use a specific kind of stochastic Petri nets that can capture arbitrary duration distributions. Thereby, we are able to achieve higher prediction accuracy than related approaches. Further, we evaluate the approach in comparison to state of the art approaches and show the potential of exploiting a so far untapped source of information: the elapsed time since the last observed event. Real-world case studies in the financial and logistics domain serve to illustrate and evaluate the approach presented.     

On an on-line dynamic direct coordination method in process industry

A two-level production control system based on an on-line dynamic direct coordination method is discussed. Within the structure, each local unit makes decisions keeping to the coupling outputs as recommended by the coordinator. An application to a system in process industry is given.     

On the refactoring of activity labels in business process models

Large corporations increasingly utilize business process models for documenting and redesigning their operations. The extent of such modeling initiatives with several hundred models and dozens of often hardly trained modelers calls for automated quality assurance. While formal properties of control flow can easily be checked by existing tools, there is a notable gap for checking the quality of the textual content of models, in particular, its activity labels. In this paper, we address the problem of activity label quality in business process models. We designed a technique for the recognition of labeling styles, and the automatic refactoring of labels with quality issues. More specifically, we developed a parsing algorithm that is able to deal with the shortness of activity labels, which integrates natural language tools like WordNet and the Stanford Parser. Using three business process model collections from practice with differing labeling style distributions, we demonstrate the applicability of our technique. In comparison to a straightforward application of standard natural language tools, our technique provides much more stable results. As an outcome, the technique shifts the boundary of process model quality issues that can be checked automatically from syntactic to semantic aspects.     

一种分离业务规则的灵活业务过程建模方法

针对动态环境中业务应用的不断变化,将易变的业务逻辑从过程控制结构中分离,并且通过业务规则的声明性表达式描述,由此构建了业务过程模型BPM4DBL(Business Process Model for Dynamic Business Logic)。在建立规则元素的模型定义后,给出了业务规则的定义、分类和可执行语言描述,最后给出一个BPM4DBL的具体应用实例。