Exploring the CSCW spectrum using process mining

Process mining techniques allow for extracting information from event logs. For example, the audit trails of a workflow management system or the transaction logs of an enterprise resource planning system can be used to discover models describing processes, organizations, and products. Traditionally, process mining has been applied to structured processes. In this paper, we argue that process mining can also be applied to less structured processes supported by computer supported cooperative work (CSCW) systems. In addition, the ProM framework is described. Using ProM a wide variety of process mining activities are supported ranging from process discovery and verification to conformance checking and social network analysis.     

Log-based transactional workflow mining

A continuous evolution of business process parameters, constraints and needs, hardly foreseeable initially, requires a continuous design from the business process management systems. In this article we are interested in developing a reactive design through process log analysis ensuring process re-engineering and execution reliability. We propose to analyse workflow logs to discover workflow transactional behaviour and to subsequently improve and correct related recovery mechanisms. Our approach starts by collecting workflow logs. Then, we build, by statistical analysis techniques, an intermediate representation specifying elementary dependencies between activities. These dependencies are refined to mine the transactional workflow model. The analysis of the discrepancies between the discovered model and the initially designed model enables us to detect design gaps, concerning particularly the recovery mechanisms. Thus, based on this mining step, we apply a set of rules on the initially designed workflow to improve workflow reliability. The work presented in this paper was partially supported by the EU under the SUPER project (FP6-026850) and by the Lion project supported by Science Foundation Ireland under Grant No. SFI/02/CE1/I131.     

Formal Modeling and Discovery of Multi-instance Business Processes: A Cloud Resource Management Case Study

Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years; however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model (MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets (MPNs) that are an extension of Petri nets with distinguishable tokens. Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multi-instantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used. The proposed discovery approach is properly implemented as plugins in the ProM toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-the-art process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.      

Business alignment: using process mining as a tool for Delta analysis and conformance testing

Increasingly, business processes are being controlled and/or monitored by information systems. As a result, many business processes leave their “footprints” in transactional information systems, i.e., business events are recorded in so-called event logs. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs, i.e., the basic idea of process mining is to diagnose business processes by mining event logs for knowledge. In this paper we focus on the potential use of process mining for measuring business alignment, i.e., comparing the real behavior of an information system or its users with the intended or expected behavior. We identify two ways to create and/or maintain the fit between business processes and supporting information systems: Delta analysis and conformance testing. Delta analysis compares the discovered model (i.e., an abstraction derived from the actual process) with some predefined processes model (e.g., the workflow model or reference model used to configure the system). Conformance testing attempts to quantify the “fit” between the event log and some predefined processes model. In this paper, we show that Delta analysis and conformance testing can be used to analyze business alignment as long as the actual events are logged and users have some control over the process.

Discovery, visualization and performance analysis of enterprise workflow

This work was motivated by a recent experience where we needed to develop enterprise operational reports when the underlying business process is not entirely known, a common situation for large companies with sophisticated IT systems. We learned that instead of relying on human knowledge or business documentation, it is much more reliable to learn from the flow structure of event sequences recorded for work items. An example of work items are product alarms detected and reported to a technical center through a remote monitoring system; the corresponding event sequence of a work item is an alarm history, i.e. the alarm handling process. We call the flow of event sequences recorded for work items, workflow. In this paper, we developed an algorithm to discover and visualize workflows for data from a remote technical support center, and argue that workflow discovery is a prerequisite for rigorous performance analysis. We also carried out a detailed performance analysis based on the discovered workflow. Among other things, we find that service time (e.g. the time necessary for handling a product alarm) fits the profile of a log-mixture distribution. It takes at least two parameters to describe such a distribution, which leads to the proposed method of using two metrics for service time reporting.     

A Workflow Process Mining Algorithm Based on Synchro-Net

Sometimes historic information about workflow execution is needed to analyze business processes. Process mining aims at extracting information from event logs for capturing a business process in execution. In this paper a process mining algorithm is proposed based on Synchro-Net which is a synchronization-based model of workflow logic and workflow semantics. With this mining algorithm based on the model, problems such as invisible tasks and short-loops can be dealt with at ease. A process mining example is presented to illustrate the algorithm, and the evaluation is also given.     

Workflow simulation for operational decision support

Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for operational decision making and continuous improvement. Here we describe a simulation system for operational decision support in the context of workflow management. To do this we exploit not only the workflow’s design, but also use logged data describing the system’s observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.     

BPS工作流平台在协同办公系统中的应用

针对企业内部管理协作高效需求的日益凸显,介绍了普元工作流平台的参考模型。基于普元BPS工作流平台,构建了企业协同办公系统功能框架,并以企业核心业务发文处理流程为例,探讨了基于BPS工作流系统的流程设计过程,对构建信息共享和业务协同的高效企业具有重要意义。     

国产民机数字化运营流程管理及仿真系统

数字化运营支持对国际民机领先制造商提高自身市场竞争力、扩大经济效益起到了越来越重要的作用。深入调研国内外数字化运营支持先进技术、经验和发展趋势,针对现有机型ARJ21和C919,梳理了国产数字化运营支持的八大业务场景,重构适合国产民机发展规律的数字化运营支持业务流程。充分考虑数字化、信息化技术发展以及运营支持需求的变化,以及成果的实用性,基于BPMN2.0业务流程建模符号标准和Activiti5流程引擎,搭建业务流程管理及仿真系统,对国产民机数字化运营支持业务流程进行电子化管理,并对典型的维修任务分析作业方法业务流程进行仿真。     

VIVACE: A framework for the systematic evaluation of variability support in process-aware information systems

ContextThe increasing adoption of process-aware information systems (PAISs) such as workflow management systems, enterprise resource planning systems, or case management systems, together with the high variability in business processes (e.g., sales processes may vary depending on the respective products and countries), has resulted in large industrial process model repositories. To cope with this business process variability, the proper management of process variants along the entire process lifecycle becomes crucial.ObjectiveThe goal of this paper is to develop a fundamental understanding of business process variability. In particular, the paper will provide a framework for assessing and comparing process variability approaches and the support they provide for the different phases of the business process lifecycle (i.e., process analysis and design, configuration, enactment, diagnosis, and evolution).MethodWe conducted a systematic literature review (SLR) in order to discover how process variability is supported by existing approaches.ResultsThe SLR resulted in 63 primary studies which were deeply analyzed. Based on this analysis, we derived the VIVACE framework. VIVACE allows assessing the expressiveness of a process modeling language regarding the explicit specification of process variability. Furthermore, the support provided by a process-aware information system to properly deal with process model variants can be assessed with VIVACE as well.ConclusionsVIVACE provides an empirically-grounded framework for process engineers that enables them to evaluate existing process variability approaches as well as to select that variability approach meeting their requirements best. Finally, it helps process engineers in implementing PAISs supporting process variability along the entire process lifecycle.     

Reinforcement learning based resource allocation in business process management

Efficient resource allocation is a complex and dynamic task in business process management. Although a wide variety of mechanisms are emerging to support resource allocation in business process execution, these approaches do not consider performance optimization. This paper introduces a mechanism in which the resource allocation optimization problem is modeled as Markov decision processes and solved using reinforcement learning. The proposed mechanism observes its environment to learn appropriate policies which optimize resource allocation in business process execution. The experimental results indicate that the proposed approach outperforms well known heuristic or hand-coded strategies, and may improve the current state of business process management.     

Introducing decision-aware business processes

Business processes are designed to smoothly operate under multiple contexts (or business situations). Each context technically implies taking a different course of action. Be that as it may, going for the most appropriate action is still left up to the business process participant without any kind of assistance. Such a situation demonstrates that there is a lack of a context-aware decision-making feature. This paper addresses the issue of enabling a context-aware decision-making within the frame of business processes. We combine the concepts of business process, context-awareness and decision-making to introduce a new concept of Decision-Aware Business Processes in which decision partitions are the cornerstones. A decision partition reacts to the collected contextual parameters by selecting or recommending the most appropriate decision(s). In fact, the focus of this research is to introduce a new formalism for designing these partitions by means of patterns. Throughout our approach, each proposed pattern leads to building decision partitions in a straight-forward fashion. An overall example is proposed to illustrate our approach. It is inspired from the banking industry and introduces a decision-aware business process that handles loan applications. To sum up, whether seasoned, novice or in-between, business process participants will be able to save time in taking action(s). Moreover, the workflow becomes no longer stagnant across the business process. Instead, it dynamically adapts itself to each new set of business requirements imposed by the collected contextual input(s).     

An overview of workflow management: From process modeling to workflow automation infrastructure

Today's business enterprises must deal with global competition, reduce the cost of doing business, and rapidly develop new services and products. To address these requirements enterprises must constantly reconsider and optimize the way they do business and change their information systems and applications to support evolving business processes. Workflow technology facilitates these by providing methodologies and software to support (i) business process modeling to capture business processes as workflow specifications, (ii) business process reengineering to optimize specified processes, and (iii) workflow automation to generate workflow implementations from workflow specifications. This paper provides a high-level overview of the current workflow management methodologies and software products. In addition, we discuss the infrastructure technologies that can address the limitations of current commercial workflow technology and extend the scope and mission of workflow management systems to support increased workflow automation in complex real-world environments involving heterogeneous, autonomous, and distributed information systems. In particular, we discuss how distributed object management and customized transaction management can support further advances in the commercial state of the art in this area. Recomended by: Omran Bukhres and e. Kühn     

Efficient Deviation Detection Between a Process Model and Event Logs

Business processes described by formal or semi-formal models are realized via information systems. Event logs generated from these systems are probably not consistent with the existing models due to insufficient design of the information system or the system upgrade. By comparing an existing process model with event logs, we can detect inconsistencies called deviations, verify and extend the business process model, and accordingly improve the business process. In this paper, some abnormal activities in business processes are formally defined based on Petri nets. An efficient approach to detect deviations between the process model and event logs is proposed. Then, business process models are revised when abnormal activities exist. A clinical process in a healthcare information system is used as a case study to illustrate our work. Experimental results show the effectiveness and efficiency of the proposed approach.      

Version management for business process schema evolution

The current business environment changes rapidly, dictated by user requirements and market opportunities. Organisations are therefore driven to continuously adapt their business processes to new conditions. Thus, management of business process schema evolution, particularly process version control, is in great demand to capture the dynamics of business process schema changes. This paper aims to facilitate version control for business process schema evolution, with an emphasis on version compatibility, co-existence of multiple versions and dynamic version shifts. A multi-level versioning approach is established to specify dependency between business process schema evolutions, and a novel version preserving graph model is proposed to record business process schema evolutions. A set of business process schema updating operations is devised to support the entire set of process change patterns. By maintaining sufficient and necessary schema and version information, our approach provides comprehensive support for navigating process instance executions of different and changing versions, and deriving the process schema of a certain version. A prototype is also implemented for the proof-of-concept purpose.     

Extended event–condition–action rules and fuzzy Petri nets based exception handling for workflow management

Exception handling plays a key role in dynamic workflow management that enables streamlined business processes. Handling application-specific exceptions is a knowledge-intensive process involving different decision-making strategies and a variety of knowledge, especially much fuzzy knowledge. Current efforts in workflow exception management are not adequate to support the knowledge-based exception handling. This paper proposes a hybrid exception handling approach based on two extended knowledge models, i.e., generalized fuzzy event–condition–action (GFECA) rule and typed fuzzy Petri net extended by process knowledge (TFPN-PK). The approach realizes integrated representation and reasoning of fuzzy and non-fuzzy knowledge as well as specific application domain knowledge and workflow process knowledge. In addition, it supports two handling strategies, i.e., direct decision and analysis-based decision, during exception management. The approach fills in the gaps in existing related researches, i.e., only providing the capability of direct exception handling and neglecting fuzzy knowledge. Based on TFPN-PK, a weighted fuzzy reasoning algorithm is designed to address the reasoning problem of uncertain goal propositions and known goal concepts by combining forward reasoning with backward reasoning and therefore to facilitate cause analysis and handling of workflow exceptions. A prototype system is developed to implement the proposed approach.     

多Agent流程协同建模研究

流程建模是企业变化管理的基础，企业的网络化，分布化导致流程的复杂化，这给传统的企业建模提出了挑战，基于网络的群组流程建模是有效的解决方法，本质上讲群组建模是群决策的过程，分布式群体决策支持系统的发展为群组建模提供了技术保障，该文给出了一种流程的协同建模系统，为辅助建模小组一致地交流，系统采用了agent技术协调。     

工作流重构技术研究

先进的工作流技术与传统的企业管理信息系统相结合,日益成为提高企业信息化的一个重要手段。目前的工作流是基于模型驱动的,定义一个完整的模型是相当复杂和费时的;而且,实际业务流程同流程模型之间必然存在差异。本文介绍了工作流网,工作流日志的概念;提出了一种基于日志包含的信息来重构业务流程模型的算法,该算法还能处理日志中的干扰信息和有效地度量流程模型和实际业务流程之间的差异。