KWM: Knowledge-based Workflow Model for agile organization

The workflow management system (WFMS) in an agile organization should be highly adaptable to the frequent organizational changes. To increase the adaptability of contemporary WFMSs, a mechanism for managing changes within the organizational structure and changes in business rules needs to be reinforced. In this paper, a knowledge-based approach for workflow modeling is proposed, in which a workflow is defined as a set of business rules. Knowledge on the organizational structure and special workflow, such as role/actor mappings and complex routing rules, can be explicitly modeled in KWM (Knowledge-based Workflow Model). Using knowledge representation scheme and dependency management facility, a change propagation mechanism is provided to adapt to the frequent changes in the organizational structure, business rules, and procedures

A Logic Based Modeling Approach to Managing Workflow Policy Changes

Workflow management systems are becoming increasingly important in the automation of business processes. In order to ensure proper workflow execution, workflow policies must be specified with respect to users, roles, and tasks. In today’s dynamic business environment, successful organizations must be able to respond to new customer demands and market opportunities with flexibility and speed. However, without systematic management of workflow policies, changes in organizational structure and process models can lead to inconsistent workflow specifications. Thus far, research in the change management of workflow policies has been scant. In this paper, we propose a logic-based approach to address this problem. Our contribution is three-fold: 1) a modeling language based on predicate logic is proposed, which is succinct and expressive enough to represent process model, organization model, and workflow polices; 2) workflow policy consistency in a dynamic changing environment is formally defined and analyzed based on the proposed language. 3) two algorithms are developed to check and enforce the policy consistency. To the best of our knowledge, this is the first work focuses on the formal analysis of workflow policy change management

Knowledge based approach to flexible workflow management systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded the Korea Advanced Institute of Science and Technology (KAIST).Today's business environments are characterized by dynamic and uncertain environments. In order to effectively support business processes in such contexts, workflow management systems must be able to adapt themselves effectively. In this dissertation, the workflow is redefined in concept and represented with a set of business rules. Business rules play a central role in organizational workflows in context of cooperation among actors. To achieve business goals, they constrain the flow of works, use of resources, and responsibility mapping between tasks and actors using role concept. Business rules are explicitly modeled in the Knowledge-based Workflow Model (KWM) using frames. To increase the adaptability of workflow management system, KWM has several distinctive features. First, it increases expressiveness of workflow model so that exception handling rules and responsibility mapping rules between tasks and actors as well as task scheduling rules are explicitly modeled. Secondly, formal definition of KWM enables one to define and to analyze correctness of workflow schema. Knowledge-based approach enables more powerful analysis on workflow schema including checking consistency and compactness of routing rules as well as terminality of a workflow. Thirdly, providing change propagation mechanism which assures correctness of workflow after the modification of workflow schema increases adaptability. Change propagation rules for the modification primitives are provided to manage workflow evolution. On the other hand, metarules that control rules in KWM are used to handle exceptions that occur in a running workflow instance. Workflow participants can easily change workflow schema of a workflow instance with the support of extra rules and a metarule. Based on KWM, K-WFMS (Knowledge-based WorkFlow Management System) has been implemented in client/server architecture. Inference shell of knowledge-based systems is employed for enactment of business rules and integrated with database systems. From a real application based on the KWM architecture, it has been shown that system performance can increase notably by reducing the number of rules and facts that are used in the course of workflow enactment

Data Flow Correctness in Adaptive Workflow Systems

Enterprises must be able to quickly adapt their business processes to react to changes in their environment. Needed business agility is often hindered by the lacking flexibility of contemporary workflow systems. In response to this inflexibility, adaptive workflow systems have emerged, which enable the dynamic adaptation of running workflows. One of the most important challenges in this context is to avoid inconsistencies and errors. So far, approaches providing respective correctness criteria for dynamic workflow change have mainly focused on control flow correctness (e.g., avoidance of deadlocks). However, little attention has been paid to data flow correctness even though this is crucial for any application of dynamic workflow change in practice. Specifically, missing or inconsistent input data of workflow activities, for example, can lead to blocking or breakdown of the underlying workflow system. This paper deals with fundamental challenges related to data flow correctness. We revisit and discuss data flow correctness at different phases of the workflow life cycle (i.e., buildtime and runtime), and show how data flow correctness can be ensured in an efficient way when dynamically changing a workflow

CONVERSATIONAL ENGINE FOR BUILDING CUSTOMIZED WORKFLOWS

Customers who utilize a device for interacting with a printer may do so for different needs and situations and primary use cases for a device may vary from user to user. Based on the context, a user may want to perform multiple tasks using the same document source. Most of the solutions on market require users to perform tasks separately, without allowing continuation of the workflow. Other solutions require users to build a workflow recipe before the workflow is performed. Preferences in individual tasks are usually not stored in the workflow because doing so would require a lot of details up front. Another broad family of solutions allows users to perform the task and have the process recorded. Materials can be imported to the saved workflow in the future. While this solution works when new materials have close resemblance with the prior one, it lacks flexibility. Users cannot change individual tasks once the workflow is recorded. The user interface (UI) is also static while users are recording their workflow. The solution embodied in this document treats all tasks as processes of workflow creation. It guides users through each task, and the UI is dynamically changed to guide users for the next available task. Once a workflow is complete, it is automatically saved for future use. The new workflow also be amended when users utilize it in the future

Supporting flexible workflow processes with a progression model

Users require flexibility when interacting with information systems to contend with changing business processes and to support diverse workflow. Model-based user interface design can accommodate flexible business processes by integrating workflow modelling with other modelling approaches. We present a workflow model, the progression model, to help in developing systems that support flexible business processes. The progression model tracks a user’s interaction with an application as a set of data elements we refer to as a workflow transaction. The steps a user takes to create a workflow transaction and the state of the workflow transaction at each step is made explicit. By making the workflow status and workflow transaction state explicit, the user can change the order of the steps in a process, manage multiple workflow transactions, keep track of data as it is accumulated, and so on. The intent is to provide the user with a mechanism to deal with partial information, interrupted and concurrent workflow transaction entry, and the processing of multiple workflow transactions. This thesis describes the progression model, an XML-compliant notation to specify the progression model, and a prototype system

An LTL Semantics of Business Workflows with Recovery

We describe a business workflow case study with abnormal behavior management (i.e. recovery) and demonstrate how temporal logics and model checking can provide a methodology to iteratively revise the design and obtain a correct-by construction system. To do so we define a formal semantics by giving a compilation of generic workflow patterns into LTL and we use the bound model checker Zot to prove specific properties and requirements validity. The working assumption is that such a lightweight approach would easily fit into processes that are already in place without the need for a radical change of procedures, tools and people's attitudes. The complexity of formalisms and invasiveness of methods have been demonstrated to be one of the major drawback and obstacle for deployment of formal engineering techniques into mundane projects

Event-Oriented Dynamic Adaptation of Workflows: Model, Architecture and Implementation

Workflow management is widely accepted as a core technology to support long-term business processes in heterogeneous and distributed environments. However, conventional workflow management systems do not provide sufficient flexibility support to cope with the broad range of failure situations that may occur during workflow execution. In particular, most systems do not allow to dynamically adapt a workflow due to a failure situation, e.g., to dynamically drop or insert execution steps. As a contribution to overcome these limitations, this dissertation introduces the agent-based workflow management system AgentWork. AgentWork supports the definition, the execution and, as its main contribution, the event-oriented and semi-automated dynamic adaptation of workflows. Two strategies for automatic workflow adaptation are provided. Predictive adaptation adapts workflow parts affected by a failure in advance (predictively), typically as soon as the failure is detected. This is advantageous in many situations and gives enough time to meet organizational constraints for adapted workflow parts. Reactive adaptation is typically performed when predictive adaptation is not possible. In this case, adaptation is performed when the affected workflow part is to be executed, e.g., before an activity is executed it is checked whether it is subject to a workflow adaptation such as dropping, postponement or replacement. In particular, the following contributions are provided by AgentWork: A Formal Model for Workflow Definition, Execution, and Estimation: In this context, AgentWork first provides an object-oriented workflow definition language. This language allows for the definition of a workflow\u92s control and data flow. Furthermore, a workflow\u92s cooperation with other workflows or workflow systems can be specified. Second, AgentWork provides a precise workflow execution model. This is necessary, as a running workflow usually is a complex collection of concurrent activities and data flow processes, and as failure situations and dynamic adaptations affect running workflows. Furthermore, mechanisms for the estimation of a workflow\u92s future execution behavior are provided. These mechanisms are of particular importance for predictive adaptation. Mechanisms for Determining and Processing Failure Events and Failure Actions: AgentWork provides mechanisms to decide whether an event constitutes a failure situation and what has to be done to cope with this failure. This is formally achieved by evaluating event-condition-action rules where the event-condition part describes under which condition an event has to be viewed as a failure event. The action part represents the necessary actions needed to cope with the failure. To support the temporal dimension of events and actions, this dissertation provides a novel event-condition-action model based on a temporal object-oriented logic. Mechanisms for the Adaptation of Affected Workflows: In case of failure situations it has to be decided how an affected workflow has to be dynamically adapted on the node and edge level. AgentWork provides a novel approach that combines the two principal strategies reactive adaptation and predictive adaptation. Depending on the context of the failure, the appropriate strategy is selected. Furthermore, control flow adaptation operators are provided which translate failure actions into structural control flow adaptations. Data flow operators adapt the data flow after a control flow adaptation, if necessary. Mechanisms for the Handling of Inter-Workflow Implications of Failure Situations: AgentWork provides novel mechanisms to decide whether a failure situation occurring to a workflow affects other workflows that communicate and cooperate with this workflow. In particular, AgentWork derives the temporal implications of a dynamic adaptation by estimating the duration that will be needed to process the changed workflow definition (in comparison with the original definition). Furthermore, qualitative implications of the dynamic change are determined. For this purpose, so-called quality measuring objects are introduced. All mechanisms provided by AgentWork include that users may interact during the failure handling process. In particular, the user has the possibility to reject or modify suggested workflow adaptations. A Prototypical Implementation: Finally, a prototypical Corba-based implementation of AgentWork is described. This implementation supports the integration of AgentWork into the distributed and heterogeneous environments of real-world organizations such as hospitals or insurance business enterprises

A novel approach to user-steering in scientific workflows

From the scientist's perspective the workflow execution is like black boxes. The scientist submits the workflow and at the end, the result or a notification about failed completion is returned. Concerning long running experiments or when workflows are in experimental phase it may not be acceptable. Scientist may need to fine-tune and monitor their experiments. To support the scientist with special user interaction tool we introduced intervention points (iPoints) where the user takes over the control for a while and has the possibility to interfere, namely to change some parameters or data, or to stop, to restart the workflow or even to deviate from the original workflow model during enactment. We plan to implement our solution in IWIR \cite{plan2011} language which was targeted to provide interoperability between four existing well-known SWfMS within the framework of the SHIWA project