Mirjam Minor

Case-based adaptation of workflows

This paper presents on a Case-based Reasoning approach for automated workflow adaptation by reuse of experience. Agile workflow technology allows structural adaptations of workflow instances at build time or at run time. The approach supports the expert in performing such adaptations by an automated method. The method employs workflow adaptation cases that record adaptation episodes from the past. The recorded changes can be automatically transferred to a new workflow that is in a similar situation of change. First, the notion of workflow adaptation cases is introduced. The sample workflow modeling language CFCN is presented, which has been developed by the University of Trier as a part of the agile workflow management system Cake. Then, the retrieval of adaptation cases is briefly discussed. The case-based adaptation method is explained including the so-called anchor mapping algorithm which identifies the parts of the target workflow where to apply the changes. A formative evaluation in two application domains compares different variants of the anchor mapping algorithm by means of experts assessing the results of the automated adaptation.

Extracting and enriching workflows from text

This paper is on a workflow extraction framework which allows to derive a formal representation based on workflows from textual descriptions of instructions, for instance, of aircraft repair procedures from a maintenance manual. The framework applies a pipes-and-filters architecture and uses NLP (Natural Language Processing) tools to perform information extraction steps automatically. In detail, the paper presents on the step of anaphora resolution to enrich the workflow extracted so far. We introduce a lexical approach and two further approaches based on a set of association rules which are created during a statistical analysis of a corpus of workflows. The results of the approaches are compared to each other. For the evaluation, we use 37 workflows which have been created by a human expert.

Extracting control-flow from text

This paper is on control-flow extraction which is a part of workflow-extraction. Workflow extraction is the transformation of a textual process description into a formal workflow model. A textual process description is a form of representation for procedural knowledge. Procedural knowledge is know how to do it-knowledge. Workflow extraction opens the use of existing workflow retrieval methods for procedural knowledge which is expressed in natural language. In this paper we extended our existing workflow-extraction-framework with components for control-flow extraction. We present and evaluate two different approaches for control-flow extraction. For the evaluation of the control-flow we developed special evaluation functions which are based on the trace index of workflows. We show that our approaches for control-flow extraction are beneficiai compared with a sequential control-flow.

On the Transferability of Process-Oriented Cases

This paper studies the feasibility of using transfer learning for process-oriented case-based reasoning. The work introduces a novel approach to transfer workflow cases from a loosely related source domain to a target domain. The idea is to develop a representation mapper based on workflow generalization, workflow abstraction, and structural analogy between the domain vocabularies. The approach is illustrated by a pair of sample domains in two sub-fields of customer relationship management that have similar process objectives but different tasks and data to fulfill them. An experiment with expert ratings of transferred cases is conducted to test the feasibility of the approach with promising results for workflow modeling support.

Towards Process-Oriented Cloud Management with Case-Based Reasoning

The paper is on a novel cloud management model based on Case-based reasoning. Cloud resources are monitored and (re-)configured according to cloud management experience stored in a case-based system. We introduce a process-oriented, multi-tier cloud management model. We propose a case representation for cloud management cases, define similarity functions and sketch adaptation and revise issues. A proof-of-concept of this ongoing work is given by a sample application scenario from the field of video ingest.

On the Use of Anaphora Resolution for Workflow Extraction

In this chapter we present three anaphora resolution approaches for workflow extraction. We introduce a lexical approach and two further approaches based on a set of association rules which are created during a statistical analysis of a corpus of workflows. We implement these approaches in our generic workflow extraction framework. The workflow extraction framework allows to derive a formal representation based on workflows from textual descriptions of instructions, for instance, of aircraft repair procedures from a maintenance manual. The framework applies a pipes-and-filters architecture and uses Natural Language Processing (NLP) tools to perform information extraction steps automatically. We evaluate the anaphora resolution approaches in the cooking domain. Two different evaluation functions are used for the evaluation which compare the extraction result with a golden standard. The syntactic function is strictly limited to syntactical comparison. The semantic evaluation function can use an ontology to infer a semantic distance for the evaluation. The evaluation shows that the most advanced anaphora resolution approach performs best. In addition a comparison of the semantic and syntactic evaluation functions shows that the semantic evaluation function is better suited for the evaluation of the anaphora resolution approaches.

Real-Time Collaboration and Experience Reuse for Cloud-Based Workflow Management Systems

In this paper we explore the concept of a real-time workflow collaboration platform. The work presents how a cloud-based Workflow Management System (WfMS) combines the technologic features which are offered by the cloud computing paradigm with a developed resource model for collaboration and reuse of experiential knowledge in workflows. It is based on a prototypical generic software system for integrated process and knowledge management and addresses the concept for collaborative workflow modeling. The concept for the reuse of workflows combines the ability of the resource model to share workflows with Case-Based Reasoning, a specific field of Artificial Intelligence. In particular, a sample workflow of a process in the financial industry is discussed. By enabling collaborative workflow modeling and by providing expert knowledge to large group of users, we aim at the improvement of the quality of workflows. Consequently, workload can be reduced, thus facilitating the work of all process stakeholders.

Towards a Case-based Reasoning Approach for Cloud Provisioning

Resource provisioning is an important issue of cloud computing. Most of the recent cloud solutions implement n na simple way with static thresholds to provide resources. Some more sophisticated approaches consider the n ncloud provisioning problem a multi-dimensional optimization approach. However, the calculation effort for n nsolving optimization problems is significant. An intelligent resource provisioning with a reduced calculation n neffort requires smart cloud management methods. In this position paper, we propose a case-based reasoning n napproach for cloud management. A case records a problem situation in cloud management and its solution. n nWe introduce a case model and a retrieval method for previously solved problem cases with the aim to reuse n ntheir re-configuration actions for a recent problem situation. The case model uses the container notion correlated n nwith QoS problems. We present a novel, composite similarity function that allows to compare a recent n nproblem situation with the cases from the past. During retrieval, the similarity function creates a ranking of n nthe cases according to their relevance to the current problem situation. Further, we describe the prototypical n nimplementation of the core elements of our case based-reasoning concept. The plausiblility of the retrieval n napproach has been tested by means of sample cases with simulated data.

Task Placement in a Cloud with Case-based Reasoning

Moving workflow management to the cloud raises novel, exciting opportunities for rapid scalability of workflow execution. Instead of running a fixed number of workflow engines on an invariant cluster of physical machines both, physical and virtual resources, can be scaled rapidly. Further, the actual state of the resources gained from cloud monitoring tools can be used to schedule workload, migrate workload or conduct split and join operations for workload at run time. n nHowever, having so many options for distributing workload forms a computationally complex configuration problem which we call the {em task placement problem}. n nIn this paper, we present on a case-based framework addressing the task placement problem by interleaving workflow management and cloud management. In addition to traditional workflow and cloud management operations it provides a set of task internal operations for workload distribution.

Towards a Trace Index Based Workflow Similarity Function

In this paper we present first steps towards an index based workflow similarity function. Classical approaches on workflow similarity perform a pairwise comparison of workflows, thus the workflow similarity function presented in this paper can speedup the calculation as the comparison is performed on the index.