LODFlow

Workflow management systems (WfMSs) are being increasingly deployed to deliver e-business transactions across organizational boundaries. To ensure a high service quality in such transactions, exception-handling schemes for conflict resolution are needed. The conflicts primarily arise due to failure of a task in workflow execution because of underlying application, or controlling WfMS component failures or insufficient user input. So far, little progress has been reported in addressing conflict resolution in cross-organizational business processes, though its importance has been recognized. In this paper, we identify the exception handling techniques that support conflict resolution in cross-organizational settings. In particular, we propose a novel, “bundled” exception-handling approach, which supports (1) exception knowledge sharing--sharing exception specifications and handling experiences, (2) coordinated exception handling, and (3) intelligent problem solving--using case based reasoning to reuse exception handing experiences. A prototype of this exception handling mechanism is developed and integrated as a part of the METEOR Workflow Management System. An evaluation of our approach is also presented through some sample workflow applications.

This paper proposes a novel architectural framework handling effective unexpected exceptions in workflow management systems (WfMS). Effective unexpected exceptions are events for which the organizations lack handling strategies. Unstructured human interventions are necessary to overcome these situations, but clash with the type of model control currently exercised by WfMS. The proposed framework uses the notion of map guidance to orchestrate these human interventions. Map guidance empowers users with contextual information about the WfMS and environment, enables the interruption of model control on the affected instances, supports collaborative exception handling and facilitates regaining model control after the exception has been resolved. The framework implementation in the Open Symphony open source platform is also described.

Along with rapid development of workflow technology, on the market, there are a lot of workflow products. Workflow exception handling study in the exception of workflow, through the appropriate method to deal effectively with the exception to ensure the implementation of the workflow. Most existing research on workflow management systems (WFMS) has been focused on workflow based on well structured and well-behaved business processes. But it is devoid of the advisement of flexibility and uncertainty. This paper proposes an Agents group based approach for exception handling. Agents group is dispatched to find out the status of running processes in the system to keep track and troubleshoot them when necessary. This model describes the classification of workflow exception, and the corresponding design of different type of exception handing agents and their cooperation.

Exception handling in workflow management systems (WFMSs) is a very important problem since it is not possible to specify all possible outcomes and alternatives. Effective reuse of existing exception handlers can greatly help in dealing with workflow exceptions. On the other hand, cooperative support for user-driven resolution of unexpected exceptions and workflow evolution at runtime is vital for an adaptive WFMS. We have developed ADOME-WFMS via a meta-modeling approach as a comprehensive framework in which the problem of workflow exception handling can be adequately addressed. In this chapter, we present an overview of exception handling in ADOME-WFMS with procedures for supporting the following: reuse of exception handlers, thorough and automated resolution of expected exceptions, effective management of Problem Solving Agents, cooperative exception handling, user-driven computer supported resolution of unexpected exceptions, and workflow evolution.

MOdeling DAta (MODA) is a template for the standardized description of materials models. It is meant to guide users toward a complete high-level documentation of modeling workflows, starting from the end-user case down to the computational details. While a MODA workflow outlines the sequence of models and data interactions, it is not executable by itself and requires implementation. Traditionally, this involves custom coding to link together solvers and data sources or using an integration framework like the Multi-Physics Integration Framework (MuPIF). Both options demand software development expertise. To simplify this process, the MUSICODE H2020 project introduced in its Open Innovation Platform a low-code approach that enables easier transformation of MODA workflows into executable multi-scale workflows. This bridges the space between MODA workflow definition and its executable encoding, merging them into a single entity while ensuring FAIR compliance. In this paper, we illustrate the implementation of this approach based on the Business Process Model and Notation (BPMN) standard, demonstrating how MODA workflows can be converted into executable workflows through examples and a real-world use case.

New technological advancements in synchrotron light sources enable data acquisitions at unprecedented levels. This emergent trend affects not only the size of the generated data but also the need for larger computational resources. Although beamline scientists and users have access to local computational resources, these are typically limited and can result in extended execution times. Applications that are based on iterative processing as in tomographic reconstruction methods require high-performance compute clusters for timely analysis of data. Here, time-sensitive analysis and processing of Advanced Photon Source data on geographically distributed resources are focused on. Two main challenges are considered: (i) modeling of the performance of tomographic reconstruction workflows and (ii) transparent execution of these workflows on distributed resources. For the former, three main stages are considered: (i) data transfer between storage and computational resources, (i) wait/queue time of reconstruction jobs at compute resources, and (iii) computation of reconstruction tasks. These performance models allow evaluation and estimation of the execution time of any given iterative tomographic reconstruction workflow that runs on geographically distributed resources. For the latter challenge, a workflow management system is built, which can automate the execution of workflows and minimize the user interaction with the underlying infrastructure. The system utilizes Globus to perform secure and efficient data transfer operations. The proposed models and the workflow management system are evaluated by using three high-performance computing and two storage resources, all of which are geographically distributed. Workflows were created with different computational requirements using two compute-intensive tomographic reconstruction algorithms. Experimental evaluation shows that the proposed models and system can be used for selecting the optimum resources, which in turn can provide up to 3.13× speedup (on experimented resources). Moreover, the error rates of the models range between 2.1 and 23.3% (considering workflow execution times), where the accuracy of the model estimations increases with higher computational demands in reconstruction tasks.

When solving problems such as enterprise information integration and interoperation across heterogeneous repositories, Web services and workflow are extensively used. Due to the nature of modularity, openness and encapsulation, traditional exception handling strategies can hardly meet the demands in the Web service setting. In this paper, we put forward a strategy based on constraint rules of Web services and the position of service nodes. We also design a framework to support exception handling dynamically during the execution of workflow.

Characterizing workflow-based activity on a production e-infrastructure using provenance data

BP plans world-scale PTA plant in Belgium

Edge node placement optimization has been an emerging research area that has drawn extraordinary attention from the disciplines of distributed and services computing. Existing studies, nevertheless, barely focus on overall deployment cost minimization with edge node site selection and server amount optimization, while bearing users’ delay tolerance. In this paper, we focus on investigating feasible user delay tolerance-aware edge node site selection and server placement optimization strategies adaptive for real-world large-scale use cases, with the objective of deployment cost minimization. A Coverage First Search method is proposed to address this problem in polynomial time. The experiments conducted on a real-world dataset demonstrate the effectiveness of our method.

Workflow management systems (WfMS) offer a promising technology for the realization of process-centered application systems. A deficiency of existing WfMS is their inadequate support for dealing with exceptional deviations from the standard procedure. In the ADEPT project, therefore, we have developed advanced concepts for workflow modeling and execution, which aim at the increase of flexibility in WfMS. On the one hand we allow workflow designers to model exceptional execution paths already at buildtime provided that these deviations are known in advance. On the other hand authorized users may dynamically deviate from the pre-modeled workflow at runtime as well in order to deal with unforeseen events. In this paper, we focus on forward and backward jumps needed in this context. We describe sophisticated modeling concepts for capturing deviations in workflow models already at buildtime, and we show how forward and backward jumps (of different semantics) can be correctly applied in an ad-hoc manner during runtime as well. We work out basic requirements, facilities, and limitations arising in this context. Our experiences with applications from different domains have shown that the developed concepts will form a key part of process flexibility in process-centered information systems.

Cross-company business processes are common in today’s networked economy and are nowadays often supported by process support systems that integrate the information systems of the different partners based on web service technology. In contrast to earlier Workflow Management Systems, which were often deployed in the controlled environment of a single company, the distributed nature of modern solutions make company-spanning web service processes more susceptible to failures. Therefore, it is desirable to augment the process models already at design time with error-handling behavior such that disrupted process instances can for instance be rolled back or completed on an alternative execution path, if, e.g., an individual service is not reachable.In this chapter, we will give an overview of past and current approaches as well as potential future works to exception handling in web service processes. We start with the concepts that were developed in the area of Workflow Management Systems, continue with error-handling techniques in state-of-the-art process modeling languages and finally give an outlook on future automated approaches to error recovery and repair.KeywordsException HandlingBusiness Process Modeling NotationRepair PlanBPEL ProcessFault HandlerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Arrowhead is an IIoT (Industrial IoT) framework that dynamically and flexibly supports automated manufacturing processes following Industry 4.0 expectations. This technical paper describes how its workflow management system, i.e., the Workflow Choreographer supports automated production. The motivation behind this work is to make production workflow management and execution more flexible by applying the SOA (Service-Oriented Architecture) principles that help information exchange between various, loosely coupled systems through late binding and lookup of their services. This paper further details the functions of the Workflow Choreographer system and shows how it applies the identified workflow levels in practice to provide a solution for the given challenge. To validate its feasibility, the paper presents simple use cases where the Workflow Choreographer manages typical production processes under laboratory conditions.

Scientific applications are frequently modeled as a workflow that is executed under the control of a workflow management system. One crucial requirement during the execution is the validation of the generated results and the traceability of the experiment execution path. The automated tracking and storage of provenance information during workflow execution could satisfy this requirement. To collect provenance data using the Grid-enabled scientific workflow management system WS-VLAM, experimentation was made with two different implementations of the provenance concepts. The first one adopts the Open Provenance Model (OPM) as basis to represent, store, and share scientific experiments metadata using the Provenance Layer Infrastructure for e-Science Resources (PLIER). The second one is the history-tracing XML (HisT) which was developed for e-Business provenance. HisT provides a specific model to store provenance data within layered XML documents, whereby each layer is related to one individual workflow task. This paper explores these two provenance models by using an example workflow application and describes how they are integrated into WS-VLAM including implementation details of the provenance architecture. It finally gives a comparison of the two different approaches with a special regard to provenance for human actors.

Nowadays workflow management systems (WfMS) are widely used for improving business processes and providing better quality of services. The analysis of workflow can facilitate locating problems in business processes and prevent repeated errors during workflow execution. A temporal workflow is described with the min and max execution time intervals for each process and modeled by control structures (Sequence, AND, XOR, Loop). These time intervals are essential factors for the analysis and anomaly detection in a workflow. In our previous work, we provided redefined definitions for anomalous behaviors and algorithms to detect anomalies within a temporal workflow. However, an essential factor, a loop structure and its temporal effects are not discussed yet. In this paper, our work extends the analysis to the workflows containing loop structure. To simplify the anomaly detection inside the loop, we first transform the loop into an XOR structure branch, perform a series of analyses and then design an algorithm to find the anomalies more efficiently. We also make an analysis for the contribution due to the algorithms.