ORCID Profile
0000-0002-9247-7476
Current Organisations
University of Tartu
,
Apromore
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Information Systems | Interorganisational Information Systems | Information Systems Development Methodologies | Business Information Systems (Incl. Data Processing) | Information Systems Development Methodologies | Information Systems Management | Global Information Systems | Systems Theory | Conceptual Modelling | Artificial Intelligence and Image Processing | Sales And Distribution | Information Engineering and Theory | Information Systems Management | Pattern Recognition and Data Mining | Simulation and Modelling | Business and Management | Bioprocessing, Bioproduction and Bioproducts | Decision Support And Group Support Systems
ANZSRC Socio-Economic Objective (SEO)
Information processing services | Application tools and system utilities | Computer Software and Services not elsewhere classified | Expanding Knowledge in the Information and Computing Sciences | Public services management | Application packages | Human Biological Preventatives (e.g. Vaccines) | Computer software and services not elsewhere classified | Human Pharmaceutical Treatments (e.g. Antibiotics) |
Related Links
Publications
Simulation-Based Evaluation of Workflow Escalation Strategies
Publisher: IEEE
Date: 2009
Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction
Publisher: IEEE
Date: 31-10-2021
Mine Me but Don’t Single Me Out: Differentially Private Event Logs for Process Mining
Publisher: IEEE
Date: 31-10-2021
Conformance Checking in Process Mining
Publisher: Elsevier BV
Date: 12-2021
Mining Business Process Stages from Event Logs
Publisher: Springer International Publishing
Date: 2017
Business process analytics: From insights to predictions
Publisher: Springer International Publishing
Date: 2018
Modeling Software Processes Using BPMN: When and When Not?
Publisher: Springer International Publishing
Date: 2016
Predictive Business Process Monitoring with LSTM Neural Networks
Publisher: Springer International Publishing
Date: 2017
Conformance checking of service behavior
Publisher: Association for Computing Machinery (ACM)
Date: 05-2008
Abstract: A service-oriented system is composed of independent software units, namely services, that interact with one another exclusively through message exchanges. The proper functioning of such system depends on whether or not each in idual service behaves as the other services expect it to behave. Since services may be developed and operated independently, it is unrealistic to assume that this is always the case. This article addresses the problem of checking and quantifying how much the actual behavior of a service, as recorded in message logs, conforms to the expected behavior as specified in a process model. We consider the case where the expected behavior is defined using the BPEL industry standard (Business Process Execution Language for Web Services). BPEL process definitions are translated into Petri nets and Petri net-based conformance checking techniques are applied to derive two complementary indicators of conformance: fitness and appropriateness . The approach has been implemented in a toolset for business process analysis and mining, namely ProM, and has been tested in an environment comprising multiple Oracle BPEL servers.
Squeezing Out the Cloud via Profit-Maximizing Resource Allocation Policies
Publisher: IEEE
Date: 08-2012
A formal approach to protocols and strategies for (legal) negotiation
Publisher: ACM
Date: 05-2001
PhDOOS 2000: The 10th Ph.D. Workshop on Object-Oriented Systems
Publisher: Springer Berlin Heidelberg
Date: 2000
Multi-perspective Comparison of Business Process Variants Based on Event Logs
Publisher: Springer International Publishing
Date: 2018
Discovering business process simulation models in the presence of multitasking and availability constraints
Publisher: Elsevier BV
Date: 07-2021
Redundancy detection in service-oriented systems
Publisher: ACM
Date: 26-04-2010
Alarm-Based Prescriptive Process Monitoring
Publisher: Springer International Publishing
Date: 2018
Behavioral Comparison of Process Models Based on Canonically Reduced Event Structures
Publisher: Springer International Publishing
Date: 2014
Predicting Coding Effort in Projects Containing XML
Publisher: IEEE
Date: 03-2012
DOI: 10.1109/CSMR.2012.29
Secure Multi-party Computation for Inter-organizational Process Mining
Publisher: Springer International Publishing
Date: 2020
Deadline-based escalation in process-aware information systems
Publisher: Elsevier BV
Date: 03-2007
Web service composition languages: old wine in New bottles?
Publisher: IEEE
Date: 2003
Structure and Evolution of Package Dependency Networks
Publisher: IEEE
Date: 05-2017
DOI: 10.1109/MSR.2017.55
Aggregate Quality of Service Computation for Composite Services
Publisher: Springer Berlin Heidelberg
Date: 2010
Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring
Publisher: Association for Computing Machinery (ACM)
Date: 18-07-2019
DOI: 10.1145/3331449
Abstract: Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity, or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g., shifting resources from one case onto another to ensure the latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures, and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 17 real-life datasets originating from different industry domains.
UML Activity Diagrams as a Workflow Specification Language
Publisher: Springer Berlin Heidelberg
Date: 2001
Temporal stability in predictive process monitoring
Publisher: Springer Science and Business Media LLC
Date: 29-06-2018
Heuristics for composite Web service decentralization
Publisher: Springer Science and Business Media LLC
Date: 14-08-2012
From BPMN Process Models to BPEL Web Services
Publisher: IEEE
Date: 2006
DOI: 10.1109/ICWS.2006.67
Building payment classification models from rules and crowdsourced labels: A case study
Publisher: Springer International Publishing
Date: 2018
Automated Discovery of Process Models from Event Logs: Review and Benchmark
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
On the expressive power of behavioral profiles
Publisher: Association for Computing Machinery (ACM)
Date: 07-2016
DOI: 10.1007/S00165-016-0372-4
Abstract: Behavioral profiles have been proposed as a behavioral abstraction of dynamic systems, specifically in the context of business process modeling. A behavioral profile can be seen as a complete graph over a set of task labels, where each edge is annotated with one relation from a given set of binary behavioral relations. Since their introduction, behavioral profiles were argued to provide a convenient way for comparing pairs of process models with respect to their behavior or computing behavioral similarity between process models. Still, as of today, there is little understanding of the expressive power of behavioral profiles. Via counter-ex les, several authors have shown that behavioral profiles over various sets of behavioral relations cannot distinguish certain systems up to trace equivalence, even for restricted classes of systems represented as safe workflow nets. This paper studies the expressive power of behavioral profiles from two angles. Firstly, the paper investigates the expressive power of behavioral profiles and systems captured as acyclic workflow nets. It is shown that for unlabeled acyclic workflow net systems, behavioral profiles over a simple set of behavioral relations are expressive up to configuration equivalence. When systems are labeled, this result does not hold for any of several previously proposed sets of behavioral relations. Secondly, the paper compares the expressive power of behavioral profiles and regular languages. It is shown that for any set of behavioral relations, behavioral profiles are strictly less expressive than regular languages, entailing that behavioral profiles cannot be used to decide trace equivalence of finite automata and thus Petri nets.
Correctness-preserving configuration of business process models
Publisher: Springer Berlin Heidelberg
Date: 2008
Business Process Performance Mining with Staged Process Flows
Publisher: Springer International Publishing
Date: 2016
Detecting approximate clones in business process model repositories
Publisher: Elsevier BV
Date: 04-2015
Updates and Application Migration Support in an ODMG Temporal Extension
Publisher: Springer Berlin Heidelberg
Date: 1999
Minimizing Overprocessing Waste in Business Processes via Predictive Activity Ordering
Publisher: Springer International Publishing
Date: 2016
Evaluation of trade-offs between workflow escalation strategies
Publisher: SAGE Publications
Date: 29-12-2013
Abstract: Workflows in the service industry are subject to exceptional circumstances that affect the ability to complete work in a timely manner. For instance, workflows may need to deal with sudden spikes in customer demand due to a variety of events such as promotional deals, product launches, major news, or natural disasters. Escalation strategies can be incorporated into the design of a workflow so that it can cope with sudden spikes in the number of service requests while mitigating the effects of missed deadlines. In this article, we propose a method for evaluating escalation strategies using simulation technology. The effectiveness of the proposed method is demonstrated on a workflow from an insurance company.
Factors affecting the sustained use of process models
Publisher: Springer International Publishing
Date: 2016
On the Suitability of Generalized Behavioral Profiles for Process Model Comparison
Publisher: Springer International Publishing
Date: 2016
A process-based methodology for designing event-based mobile composite applications
Publisher: Elsevier BV
Date: 04-2007
Fast detection of exact clones in business process model repositories
Publisher: Elsevier BV
Date: 06-2013
Interpreted Execution of Business Process Models on Blockchain
Publisher: IEEE
Date: 10-2019
An architecture for assembling agents that participate in alternative heterogeneous auctions
Publisher: IEEE Comput. Soc
Date: 2002
The Rise of the Estonian Start-Up Sphere
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
DOI: 10.1109/MITP.2014.62
Predictive Monitoring of Business Processes
Publisher: Springer International Publishing
Date: 2014
The biggest business process management problems to solve before we die
Publisher: Elsevier BV
Date: 04-2023
Modelling Flexible Processes with Business Objects
Publisher: IEEE
Date: 07-2009
DOI: 10.1109/CEC.2009.39
A Petri Nets based Generic Genetic Algorithm framework for resource optimization in business processes
Publisher: Elsevier BV
Date: 08-2018
Bursty egocentric network evolution in Skype
Publisher: Springer Science and Business Media LLC
Date: 02-07-2013
Process-Oriented Assessment of Web Services
Publisher: IGI Global
Date: 2006
Abstract: Though Web services offer unique opportunities for the design of new business processes, the assessment of the potential impact of Web services is often reduced to technical aspects. This paper proposes a four-phase methodology which facilitates the evaluation of the potential use of Web services in e-business systems both from a technical and from a strategic viewpoint. It is based on business process models, which are used to frame the adoption of Web services and to assess their impact on existing business processes. The application of this methodology is described using a procurement scenario.
Detecting behavioural incompatibilities between pairs of services
Publisher: Springer Berlin Heidelberg
Date: 2009
Service-oriented processes: An introduction to BPEL
Publisher: IGI Global
Date: 2007
DOI: 10.4018/978-1-59904-045-5.CH008
Abstract: The Business Process Execution Language for Web Services (BPEL) is an emerging standard for specifying the behaviour of Web services at different levels of details using business process modeling constructs. It represents a convergence between Web services and business process technology. This chapter introduces the main concepts and constructs of BPEL and illustrates them by means of a comprehensive ex le. In addition, the chapter reviews some perceived limitations of BPEL and discusses proposals to address these limitations. The chapter also considers the possibility of applying formal methods and Semantic Web technology to support the rigorous development of service-oriented processes using BPEL.
Scalable alignment of process models and event logs: An approach based on automata and S-components
Publisher: Elsevier BV
Date: 12-2020
Toward web-scale workflows for film production
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2008
DOI: 10.1109/MIC.2008.115
Graph Matching Algorithms for Business Process Model Similarity Search
Publisher: Springer Berlin Heidelberg
Date: 2009
Towards a Formalization of Contracts for Service Substitution
Publisher: IEEE
Date: 07-2010
Caterpillar: A business process execution engine on the Ethereum blockchain
Publisher: Wiley
Date: 09-05-2019
DOI: 10.1002/SPE.2702
Browserbite: cross‐browser testing via image processing
Publisher: Wiley
Date: 22-12-2015
DOI: 10.1002/SPE.2387
Strategies in supply chain management for the Trading Agent Competition
Publisher: Elsevier BV
Date: 12-2007
Automated Discovery of Process Models with True Concurrency and Inclusive Choices
Publisher: Springer International Publishing
Date: 2021
Adapting the CRISP-DM Data Mining Process: A Case Study in the Financial Services Domain
Publisher: Springer International Publishing
Date: 2021
Semantic DMN: Formalizing Decision Models with Domain Knowledge
Publisher: Springer International Publishing
Date: 2017
Adaptations of data mining methodologies: a systematic literature review
Publisher: PeerJ
Date: 25-05-2020
DOI: 10.7717/PEERJ-CS.267
Abstract: The use of end-to-end data mining methodologies such as CRISP-DM, KDD process, and SEMMA has grown substantially over the past decade. However, little is known as to how these methodologies are used in practice. In particular, the question of whether data mining methodologies are used ‘as-is’ or adapted for specific purposes, has not been thoroughly investigated. This article addresses this gap via a systematic literature review focused on the context in which data mining methodologies are used and the adaptations they undergo. The literature review covers 207 peer-reviewed and ‘grey’ publications. We find that data mining methodologies are primarily applied ‘as-is’. At the same time, we also identify various adaptations of data mining methodologies and we note that their number is growing rapidly. The dominant adaptations pattern is related to methodology adjustments at a granular level (modifications) followed by extensions of existing methodologies with additional elements. Further, we identify two recurrent purposes for adaptation: (1) adaptations to handle Big Data technologies, tools and environments (technological adaptations) and (2) adaptations for context-awareness and for integrating data mining solutions into business processes and IT systems (organizational adaptations). The study suggests that standard data mining methodologies do not pay sufficient attention to deployment issues, which play a prominent role when turning data mining models into software products that are integrated into the IT architectures and business processes of organizations. We conclude that refinements of existing methodologies aimed at combining data, technological, and organizational aspects, could help to mitigate these gaps.
Fire now, fire later: alarm-based systems for prescriptive process monitoring
Publisher: Springer Science and Business Media LLC
Date: 30-12-2021
DOI: 10.1007/S10115-021-01633-W
Abstract: Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood that it will lead to an undesired outcome. These techniques, however, focus on generating predictions and do not prescribe when and how process workers should intervene to decrease the cost of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive monitoring with the ability to generate alarms that trigger interventions to prevent an undesired outcome or mitigate its effect. The framework incorporates a parameterized cost model to assess the cost–benefit trade-off of generating alarms. We show how to optimize the generation of alarms given an event log of past process executions and a set of cost model parameters. The proposed approaches are empirically evaluated using a range of real-life event logs. The experimental results show that the net cost of undesired outcomes can be minimized by changing the threshold for generating alarms, as the process instance progresses. Moreover, introducing delays for triggering alarms, instead of triggering them as soon as the probability of an undesired outcome exceeds a threshold, leads to lower net costs.
Identifying and Classifying Variations in Business Processes
Publisher: Springer Berlin Heidelberg
Date: 2012
Artifact Lifecycle Discovery
Publisher: World Scientific Pub Co Pte Ltd
Date: 03-2015
DOI: 10.1142/S021884301550001X
Abstract: Artifact-centric modeling is an approach for capturing business processes in terms of so-called business artifacts — key entities driving a company's operations and whose lifecycles and interactions define an overall business process. This approach has been shown to be especially suitable in the context of processes where one-to-many or many-to-many relations exist between the entities involved in the process. As a contribution towards building up a body of methods to support artifact-centric modeling, this article presents a method for automated discovery of artifact-centric process models starting from logs consisting of flat collections of event records. We decompose the problem in such a way that a wide range of existing (non-artifact-centric) automated process discovery methods can be reused in a flexible manner. The presented methods are implemented as a package for ProM, a generic open-source framework for process mining. The methods have been applied to reverse-engineer an artifact-centric process model starting from logs of a real-life business process.
Business Process Simulation for Operational Decision Support
Publisher: Springer Berlin Heidelberg
Date: 2008
Decomposition Driven Consolidation of Process Models
Publisher: Springer International Publishing
Date: 2013
Outcome-Oriented Predictive Process Monitoring
Publisher: Association for Computing Machinery (ACM)
Date: 13-03-2019
DOI: 10.1145/3301300
Abstract: Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces. Motivated by the increasingly pervasive availability of fine-grained event data about business process executions, the problem of predictive process monitoring has received substantial attention in the past years. In particular, a considerable number of methods have been put forward to address the problem of outcome-oriented predictive process monitoring, which refers to classifying each ongoing case of a process according to a given set of possible categorical outcomes—e.g., Will the customer complain or not? Will an order be delivered, canceled, or withdrawn? Unfortunately, different authors have used different datasets, experimental settings, evaluation measures, and baselines to assess their proposals, resulting in poor comparability and an unclear picture of the relative merits and applicability of different methods. To address this gap, this article presents a systematic review and taxonomy of outcome-oriented predictive process monitoring methods, and a comparative experimental evaluation of eleven representative methods using a benchmark covering 24 predictive process monitoring tasks based on nine real-life event logs.
Robotic Process Mining
Publisher: Springer International Publishing
Date: 2022
DOI: 10.1007/978-3-031-08848-3_16
Abstract: User interaction logs allow us to analyze the execution of tasks in a business process at a finer level of granularity than event logs extracted from enterprise systems. The fine-grained nature of user interaction logs open up a number of use cases. For ex le, by analyzing such logs, we can identify best practices for executing a given task in a process, or we can elicit differences in performance between workers or between teams. Furthermore, user interaction logs allow us to discover repetitive and automatable routines that occur during the execution of one or more tasks in a process. Along this line, this chapter introduces a family of techniques, called Robotic Process Mining (RPM), which allow us to discover repetitive routines that can be automated using robotic process automation technology. The chapter presents a structured landscape of concepts and techniques for RPM, including techniques for user interaction log preprocessing, techniques for discovering frequent routines, notions of routine automatability, as well as techniques for synthesizing executable routine specifications for robotic process automation.
Service interaction modeling: Bridging global and local views
Publisher: IEEE
Date: 10-2006
DOI: 10.1109/EDOC.2006.50
Interactive and Incremental Business Process Model Repair
Publisher: Springer International Publishing
Date: 2017
A configurable matchmaking framework for electronic marketplaces
Publisher: Elsevier BV
Date: 03-2004
Dynamic Role Binding in Blockchain-Based Collaborative Business Processes
Publisher: Springer International Publishing
Date: 2019
Code churn estimation using organisational and code metrics: An experimental comparison
Publisher: Elsevier BV
Date: 02-2012
Formal semantics and analysis of control flow in WS-BPEL
Publisher: Elsevier BV
Date: 07-2007
Facilitating the rapid development and scalable orchestration of composite web services
Publisher: Springer Science and Business Media LLC
Date: 2005
Reserved or On-Demand Instances? A Revenue Maximization Model for Cloud Providers
Publisher: IEEE
Date: 07-2011
Questionnaire-based variability modeling for system configuration
Publisher: Springer Science and Business Media LLC
Date: 06-06-2009
Multi-level privacy analysis of business processes: the Pleak toolset
Publisher: Springer Science and Business Media LLC
Date: 21-07-2021
Orchestrating interrelated trading activities
Publisher: Inderscience Publishers
Date: 2005
Specification and execution of composite trading activities
Publisher: Springer Science and Business Media LLC
Date: 24-10-2007
QoS-aware middleware for Web services composition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2004
DOI: 10.1109/TSE.2004.11
White-box prediction of process performance indicators via flow analysis
Publisher: ACM
Date: 05-07-2017
Einführung in das Geschäftsprozessmanagement
Publisher: Springer Berlin Heidelberg
Date: 2021
Fundamentals of Business Process Management
Publisher: Springer Berlin Heidelberg
Date: 2018
Prozessidentifikation
Publisher: Springer Berlin Heidelberg
Date: 2021
Grundlagen der Geschäftsprozessmodellierung
Publisher: Springer Berlin Heidelberg
Date: 2021
Similarity of business process models: Metrics and evaluation
Publisher: Elsevier BV
Date: 04-2011
On the convergence of data and process engineering
Publisher: Springer Berlin Heidelberg
Date: 2011
Fortgeschrittene Prozessmodellierung
Publisher: Springer Berlin Heidelberg
Date: 2021
Prozesserhebung
Publisher: Springer Berlin Heidelberg
Date: 2021
Qualitative Prozessanalyse
Publisher: Springer Berlin Heidelberg
Date: 2021
Quantitative Prozessanalyse
Publisher: Springer Berlin Heidelberg
Date: 2021
Measuring Fitness and Precision of Automatically Discovered Process Models: A Principled and Scalable Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Declarative composition and peer-to-peer provisioning of dynamic Web services
Publisher: IEEE Comput. Soc
Date: 2002
Modeling Extraneous Activity Delays in Business Process Simulation
Publisher: arXiv
Date: 2022
Prozessverbesserung
Publisher: Springer Berlin Heidelberg
Date: 2021
A formal approach to negotiating agents development
Publisher: Elsevier BV
Date: 06-2002
Handling Transactional Properties in Web Service Composition
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11581062_21
Prozessorientierte Informationssysteme
Publisher: Springer Berlin Heidelberg
Date: 2021
Discovering Business Process Simulation Models in the Presence of Multitasking
Publisher: Springer International Publishing
Date: 2020
BPM als Unternehmensfähigkeit
Publisher: Springer Berlin Heidelberg
Date: 2021
Prozessüberwachung
Publisher: Springer Berlin Heidelberg
Date: 2021
Approximate Clone Detection in Repositories of Business Process Models
Publisher: Springer Berlin Heidelberg
Date: 2012
Prozessimplementierung mit ausführbaren Modellen
Publisher: Springer Berlin Heidelberg
Date: 2021
Browserbite: Accurate Cross-Browser Testing via Machine Learning over Image Features
Publisher: IEEE
Date: 09-2013
DOI: 10.1109/ICSM.2013.88
Pointwise Temporal Object Database Browsing
Publisher: Springer Berlin Heidelberg
Date: 2001
Discovering Causal Factors Explaining Business Process Performance Variation
Publisher: Springer International Publishing
Date: 2017
Dimensions of coupling in middleware
Publisher: Wiley
Date: 11-02-2009
DOI: 10.1002/CPE.1414
Libra: High-Utility Anonymization of Event Logs for Process Mining via Subsampling
Publisher: arXiv
Date: 2022
Towards an assessment model for balancing process model production and use
Publisher: ACM
Date: 24-03-2014
Predictive Business Process Monitoring Framework with Hyperparameter Optimization
Publisher: Springer International Publishing
Date: 2016
SERVICE-ORIENTED DESIGN: A MULTI-VIEWPOINT APPROACH
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2004
DOI: 10.1142/S0218843004001012
Abstract: As the technology associated with the "Web Services" trend gains significant adoption, the need for a corresponding design approach becomes increasingly important. This paper introduces a foundational model for designing (composite) services. The innovation of this model lies in the identification of four interrelated viewpoints (interface behaviour, provider behaviour, choreography, and orchestration) and their formalization from a control-flow perspective in terms of Petri nets. By formally capturing the interrelationships between these viewpoints, the proposal enables the static verification of the consistency of composite services designed in a cooperative and incremental manner. A proof-of-concept simulation and verification tool has been developed to test the possibilities of the proposed model.
Predictive process monitoring in apromore
Publisher: Springer International Publishing
Date: 2018
Identifying Candidate Routines for Robotic Process Automation from Unsegmented UI Logs
Publisher: IEEE
Date: 10-2020
Differential Privacy Analysis of Data Processing Workflows
Publisher: Springer International Publishing
Date: 2016
Designing Maintainable XML Transformations
Publisher: IEEE
Date: 03-2010
DOI: 10.1109/CSMR.2010.24
Designing a data mining process for the financial services domain
Publisher: Informa UK Limited
Date: 07-07-2022
Modelling families of business process variants: A decomposition driven method
Publisher: Elsevier BV
Date: 03-2016
Process-oriented assessment of Web services
Publisher: IGI Global
Date: 2008
DOI: 10.4018/978-1-60566-086-8.CH014
Abstract: Though Web services offer unique opportunities for the design of new business processes, the assessment of the potential impact of Web services is often reduced to technical aspects. This paper proposes a four-phase methodology which facilitates the evaluation of the potential use of Web services in e-business systems both from a technical and from a strategic viewpoint. It is based on business process models, which are used to frame the adoption of Web services and to assess their impact on existing business processes. The application of this methodology is described using a procurement scenario.
Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs
Publisher: IEEE
Date: 10-2020
When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints
Publisher: arXiv
Date: 2022
Diagnosing behavioral differences between business process models: An approach based on event structures
Publisher: Elsevier BV
Date: 03-2016
Special issue: BPM 2020 Selected Papers in Foundations and Engineering
Publisher: Elsevier BV
Date: 11-2022
Semantics, Analysis and Simplification of DMN Decision Tables
Publisher: Elsevier BV
Date: 11-2018
Clustering-Based Predictive Process Monitoring
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure
Publisher: Springer International Publishing
Date: 2016
Achieving performance and availability guarantees with spot instances
Publisher: IEEE
Date: 09-2011
DOI: 10.1109/HPCC.2011.46
Evaluating choreographies in BPMN 2.0 using an extended quality framework
Publisher: Springer Berlin Heidelberg
Date: 2011
Discovering process maps from event streams
Publisher: ACM
Date: 26-05-2018
Genetic algorithms for hyperparameter optimization in predictive business process monitoring
Publisher: Elsevier BV
Date: 05-2018
Modelling business process variability for design-time configuration
Publisher: IGI Global
Date: 2009
DOI: 10.4018/978-1-60566-288-6.CH009
Abstract: A reference process model represents multiple variants of a common business process in an integrated and reusable manner. It is intended to be in idualized in order to fit the requirements of a specific organization or project. This practice of in idualizing reference process models provides an attractive alternative with respect to designing process models from scratch in particular, it enables the reuse of proven practices. This chapter introduces techniques for representing variability in the context of reference process models, as well as techniques that facilitate the in idualization of reference process models with respect to a given set of requirements.
Semantic DMN: Formalizing and Reasoning About Decisions in the Presence of Background Knowledge
Publisher: Cambridge University Press (CUP)
Date: 18-01-2019
DOI: 10.1017/S1471068418000479
Abstract: The Decision Model and Notation (DMN) is a recent Object Management Group standard for the elicitation and representation of decision models and for managing their interconnection with business processes. DMN builds on the notion of decision tables and their combination into more complex decision requirements graphs (DRGs), which bridge between business process models and decision logic models. DRGs may rely on additional, external business knowledge models, whose functioning is not part of the standard. In this work, we consider one of the most important types of business knowledge, namely, background knowledge that conceptually accounts for the structural aspects of the domain of interest, and propose decision knowledge bases (DKBs), which semantically combine DRGs modeled in DMN, and domain knowledge captured by means of first-order logic with datatypes. We provide a logic-based semantics for such an integration, and formalize different DMN reasoning tasks for DKBs. We then consider background knowledge formulated as a description logic (DL) ontology with datatypes, and show how the main verification tasks for DMN in this enriched setting can be formalized as standard DL reasoning services and actually carried out in ExpTime. We discuss the effectiveness of our framework on a case study in maritime security.
Business process model merging: An approach to business process consolidation
Publisher: Association for Computing Machinery (ACM)
Date: 03-2013
Abstract: This article addresses the problem of constructing consolidated business process models out of collections of process models that share common fragments. The article considers the construction of unions of multiple models (called merged models ) as well as intersections (called digests ). Merged models are intended for analysts who wish to create a model that subsumes a collection of process models -- typically representing variants of the same underlying process -- with the aim of replacing the variants with the merged model. Digests, on the other hand, are intended for analysts who wish to identify the most recurring fragments across a collection of process models, so that they can focus their efforts on optimizing these fragments. The article presents an algorithm for computing merged models and an algorithm for extracting digests from a merged model. The merging and digest extraction algorithms have been implemented and tested against collections of process models taken from multiple application domains. The tests show that the merging algorithm produces compact models and scales up to process models containing hundreds of nodes. Furthermore, a case study conducted in a large insurance company has demonstrated the usefulness of the merging and digest extraction operators in a practical setting.
Unraveling Unstructured Process Models
Publisher: Springer Berlin Heidelberg
Date: 2010
Efficient edge filtering of directly-follows graphs for process mining
Publisher: Elsevier BV
Date: 09-2022
Discovering Automatable Routines from User Interaction Logs
Publisher: Springer International Publishing
Date: 2019
Programming and Compiling Web Services in GPSL
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11596141_41
Business Process Privacy Analysis in Pleak
Publisher: Springer International Publishing
Date: 2019
Preserving correctness during business process model configuration
Publisher: Association for Computing Machinery (ACM)
Date: 05-2010
DOI: 10.1007/S00165-009-0112-0
Abstract: A configurable process model captures a family of related process models in a single artifact. Such models are intended to be configured to fit the requirements of specific organizations or projects, leading to in idualized process models that are subsequently used for domain analysis or solution design. This article proposes a formal foundation for in idualizing configurable process models incrementally, while preserving correctness, both with respect to syntax and behavioral semantics. Specifically, assuming the configurable process model is behaviorally sound, the in idualized process models are guaranteed to be sound. The theory is first developed in the context of Petri nets and then extended to a process modeling notation widely used in practice, namely Event-driven Process Chains.
Consolidated management of business process variants
Publisher: Springer Berlin Heidelberg
Date: 2012
Probabilistic Automated Bidding in Multiple Auctions
Publisher: Springer Science and Business Media LLC
Date: 2005
Improving Web Service Survivability via Gracefully Degraded Substitution
Publisher: IEEE
Date: 08-2010
Specification of Composite Trading Activities in Supply Chain Management
Publisher: IEEE
Date: 2005
DOI: 10.1109/EEE.2005.125
TEMPOS: a platform for developing temporal applications on top of object DBMS
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2004
Verification of Privacy-Enhanced Collaborations
Publisher: ACM
Date: 07-10-2020
Metaheuristic Optimization for Automated Business Process Discovery
Publisher: Springer International Publishing
Date: 2019
Learning Accurate LSTM Models of Business Processes
Publisher: Springer International Publishing
Date: 2019
Generalized aggregate Quality of Service computation for composite services
Publisher: Elsevier BV
Date: 08-2012
Split miner: automated discovery of accurate and simple business process models from event logs
Publisher: Springer Science and Business Media LLC
Date: 15-05-2018
Scalable conformance checking of business processes
Publisher: Springer International Publishing
Date: 2017
Detecting Sudden and Gradual Drifts in Business Processes from Execution Traces
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
WofBPEL: A Tool for Automated Analysis of BPEL Processes
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11596141_37
AI-Augmented Business Process Management Systems: A Research Manifesto
Publisher: arXiv
Date: 2022
Data-Driven Analysis of Batch Processing Inefficiencies in Business Processes
Publisher: Springer International Publishing
Date: 2022
DOI: 10.1007/978-3-031-05760-1_14
Abstract: Batch processing reduces processing time in a business process at the expense of increasing waiting time. If this trade-off between processing and waiting time is not analyzed, batch processing can, over time, evolve into a source of waste in a business process. Therefore, it is valuable to analyze batch processing activities to identify waiting time wastes. Identifying and analyzing such wastes present the analyst with improvement opportunities that, if addressed, can improve the cycle time efficiency (CTE) of a business process. In this paper, we propose an approach that, given a process execution event log, (1) identifies batch processing activities, (2) analyzes their inefficiencies caused by different types of waiting times to provide analysts with information on how to improve batch processing activities. More specifically, we conceptualize different waiting times caused by batch processing patterns and identify improvement opportunities based on the impact of each waiting time type on the CTE. Finally, we demonstrate the applicability of our approach to a real-life event log.
Event Structures as a Foundation for Process Model Differencing, Part 1: Acyclic processes
Publisher: Springer Berlin Heidelberg
Date: 2013
Grundlagen des Geschäftsprozessmanagements
Publisher: Springer Berlin Heidelberg
Date: 2021
Structuring Acyclic Process Models
Publisher: Springer Berlin Heidelberg
Date: 2010
Linking domain models and process models for reference model configuration
Publisher: Springer Berlin Heidelberg
Date: 2008
Guest editorial: special issue on data and artifact-centric business processes
Publisher: Springer Science and Business Media LLC
Date: 09-02-2016
Controlled flexibility in blockchain-based collaborative business processes
Publisher: Elsevier BV
Date: 02-2022
A flexible, object-centric approach for business process modelling
Publisher: Springer Science and Business Media LLC
Date: 20-06-2010
Blockchains for Business Process Management - Challenges and Opportunities
Publisher: Association for Computing Machinery (ACM)
Date: 26-02-2018
DOI: 10.1145/3183367
Abstract: Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.
A probabilistic approach to automated bidding in alternative auctions
Publisher: ACM
Date: 07-05-2002
Translating Standard Process Models to BPEL
Publisher: Springer International Publishing
Date: 2006
DOI: 10.1007/11767138_28
Business process variant analysis: Survey and classification
Publisher: Elsevier BV
Date: 2021
Generating Business Process Models from Object Behavior Models
Publisher: Informa UK Limited
Date: 09-10-2008
Scaling Dynamic Web Content Provision Using Elapsed-Time-Based Content Degradation
Publisher: Springer Berlin Heidelberg
Date: 2004
Predicting process performance: A white‐box approach based on process models
Publisher: Wiley
Date: 27-03-2019
DOI: 10.1002/SMR.2170
Differentially private release of event logs for process mining
Publisher: Elsevier BV
Date: 05-2023
Data Mining Methodologies in the Banking Domain: A Systematic Literature Review
Publisher: Springer International Publishing
Date: 2019
Slice, Mine and Dice: Complexity-Aware Automated Discovery of Business Process Models
Publisher: Springer Berlin Heidelberg
Date: 2013
Business Process Privacy Analysis in Pleak
Publisher: Springer Science and Business Media LLC
Date: 10-2019
Predictive business process monitoring with structured and unstructured data
Publisher: Springer International Publishing
Date: 2016
Discovering Data-Aware Declarative Process Models from Event Logs
Publisher: Springer Berlin Heidelberg
Date: 2013
Discovering Branching Conditions from Business Process Execution Logs
Publisher: Springer Berlin Heidelberg
Date: 2013
Discovering data transfer routines from user interaction logs
Publisher: Elsevier BV
Date: 07-2022
Clone Detection in Repositories of Business Process Models
Publisher: Springer Berlin Heidelberg
Date: 2011
Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach
Publisher: Springer International Publishing
Date: 2022
DOI: 10.1007/978-3-030-98581-3_14
Abstract: Prescriptive process monitoring is a family of techniques to optimize the performance of a business process by triggering interventions at runtime. Existing prescriptive process monitoring techniques assume that the number of interventions that may be triggered is unbounded. In practice, though, interventions consume resources with finite capacity. For ex le, in a loan origination process, an intervention may consist of preparing an alternative loan offer to increase the applicant’s chances of taking a loan. This intervention requires time from a credit officer. Thus, it is not possible to trigger this intervention in all cases. This paper proposes a prescriptive monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints. The technique relies on predictive modeling to identify cases that are likely to lead to a negative outcome, in combination with causal inference to estimate the effect of an intervention on a case’s outcome. These estimates are used to allocate resources to interventions to maximize a cost function. A preliminary evaluation suggests that the approach produces a higher net gain than a purely predictive (non-causal) baseline.
Peer-to-Peer Traced Execution of Composite Services
Publisher: Springer Berlin Heidelberg
Date: 2001
From business process models to service interfaces
Publisher: Springer Berlin Heidelberg
Date: 11-04-2015
Blockchain Support for Collaborative Business Processes
Publisher: Springer Science and Business Media LLC
Date: 22-05-2019
DOI: 10.1007/S00287-019-01178-X
Abstract: Blockchain technology provides basic building blocks to support the execution of collaborative business processes involving mutually untrusted parties in a decentralized environment. Several research proposals have demonstrated the feasibility of designing blockchain-based collaborative business processes using a high-level notation, such as the Business Process Model and Notation (BPMN), and thereon automatically generating the code artifacts required to execute these processes on a blockchain platform. In this paper, we present the conceptual foundations of model-driven approaches for blockchain-based collaborative process execution and we compare two concrete approaches, namely Caterpillar and Lorikeet.
Correlating Activation and Target Conditions in Data-Aware Declarative Process Discovery
Publisher: Springer International Publishing
Date: 2018
Business Process Simulation with Differentiated Resources: Does it Make a Difference?
Publisher: arXiv
Date: 2022
Abstract-and-compare: A family of scalable precision measures for automated process discovery
Publisher: Springer International Publishing
Date: 2018
Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs
Publisher: IEEE
Date: 11-2017
DOI: 10.1109/ICDM.2017.9
Silhouetting the Cost-Time Front: Multi-objective Resource Optimization in Business Processes
Publisher: Springer International Publishing
Date: 2021
DOI: 10.1007/978-3-030-85440-9_6
Abstract: The allocation of resources in a business process determines the trade-off between cycle time and resource cost. A higher resource utilization leads to lower cost and higher cycle time, while a lower resource utilization leads to higher cost and lower waiting time. In this setting, this paper presents a multi-objective optimization approach to compute a set of Pareto-optimal resource allocations for a given process concerning cost and cycle time. The approach heuristically searches through the space of possible resource allocations using a simulation model to evaluate each allocation. Given the high number of possible allocations, it is imperative to prune the search space. Accordingly, the approach incorporates a method that selectively perturbs a resource utilization to derive new candidates that are likely to Pareto-dominate the already explored ones. The perturbation method relies on two indicators: resource utilization and resource impact, the latter being the contribution of a resource to the cost or cycle time of the process. Additionally, the approach incorporates a ranking method to accelerate convergence by guiding the search towards the resource allocations closer to the current Pareto front. The perturbation and ranking methods are embedded into two search meta-heuristics, namely hill-climbing and tabu-search. Experiments show that the proposed approach explores fewer resource allocations to compute Pareto fronts comparable to those produced by a well-known genetic algorithm for multi-objective optimization, namely NSGA-II.
Cognitive Relay Networks With Multiple Primary Transceivers Under Spectrum-Sharing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2012
Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring
Publisher: Springer International Publishing
Date: 2016
Evidence-based business process management
Publisher: Elsevier
Date: 2014
Fast fully dynamic landmark-based estimation of shortest path distances in very large graphs
Publisher: ACM
Date: 24-10-2011
Criteria and Heuristics for Business Process Model Decomposition
Publisher: Springer Science and Business Media LLC
Date: 08-12-2015
Service Design, Implementation and Description (Tutorial)
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11581062_83
Predicting the maintainability of XSL transformations
Publisher: Elsevier BV
Date: 12-2011
Automated discovery of declarative process models with correlated data conditions
Publisher: Elsevier BV
Date: 03-2020
Patterns of Process Modeling
Publisher: Wiley
Date: 02-09-2005
On the Notion of Coupling in Communication Middleware
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11575801_6
Automated discovery of structured process models from event logs: The discover-and-structure approach
Publisher: Elsevier BV
Date: 09-2018
Optimization framework for DFG-based automated process discovery approaches
Publisher: Springer Science and Business Media LLC
Date: 27-02-2021
DOI: 10.1007/S10270-020-00846-X
Abstract: The problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by means of metaheuristic optimization techniques. However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation. This article presents a metaheuristic optimization framework for automated process discovery. The key idea of the framework is to construct a directly-follows graph (DFG) from the event log, to perturb this DFG so as to generate new candidate solutions, and to apply a DFG-based automated process discovery approach in order to derive a process model from each DFG. The framework can be instantiated by linking it to an automated process discovery approach, an optimization metaheuristic, and the quality measure to be optimized (e.g., fitness, precision, F-score). The article considers several instantiations of the framework corresponding to four optimization metaheuristics, three automated process discovery approaches (Inductive Miner—directly-follows, Fodina, and Split Miner), and one accuracy measure (Markovian F-score). These framework instances are compared using a set of 20 real-life event logs. The evaluation shows that metaheuristic optimization consistently yields visible improvements in F-score for all the three automated process discovery approaches, at the cost of execution times in the order of minutes, versus seconds for the baseline approaches.
Disclosure Analysis of SQL Workflows
Publisher: Springer International Publishing
Date: 2019
Controlled automated discovery of collections of business process models
Publisher: Elsevier BV
Date: 12-2014
Log Delta Analysis: Interpretable Differencing of Business Process Event Logs
Publisher: Springer International Publishing
Date: 2015
Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning
Publisher: Springer International Publishing
Date: 2022
DOI: 10.1007/978-3-031-07472-1_4
Abstract: Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures – a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods leverage process mining techniques to learn process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to accurately capture the temporal dynamics of real-life processes. In contrast, generative Deep Learning (DL) models are better able to capture such temporal dynamics. The drawback of DL models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) process model is extracted via DDS techniques, and then combined with a DL model to generate timest ed event sequences. An experimental evaluation shows that the resulting hybrid simulation models match the temporal accuracy of pure DL models, while partially retaining the what-if analysis capability of DDS approaches.
Fast and Accurate Business Process Drift Detection
Publisher: Springer International Publishing
Date: 2015
Understanding business process models: The costs and benefits of structuredness
Publisher: Springer International Publishing
Date: 2012
Configurable SOAP proxy cache for data provisioning web services
Publisher: ACM
Date: 21-03-2011
Declarative Process Modeling in BPMN
Publisher: Springer International Publishing
Date: 2015
APROMORE: An advanced process model repository
Publisher: Elsevier BV
Date: 06-2011
Using dynamic and contextual features to predict issue lifetime in GitHub projects
Publisher: ACM
Date: 14-05-2016
Process‐Aware Information Systems
Publisher: Wiley
Date: 02-09-2005
DOI: 10.1002/0471741442
Managing Process Model Collections with AProMoRe
Publisher: Springer Berlin Heidelberg
Date: 2010
Using CEP technology to adapt messages exchanged by web services
Publisher: ACM
Date: 21-04-2008
Process Mining Reloaded: Event Structures as a Unified Representation of Process Models and Event Logs
Publisher: Springer International Publishing
Date: 2015
Encoding resource experience for predictive process monitoring
Publisher: Elsevier BV
Date: 02-2022
AI-augmented Business Process Management Systems: A Research Manifesto
Publisher: Association for Computing Machinery (ACM)
Date: 31-01-2023
DOI: 10.1145/3576047
Abstract: AI-augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for ABPMSs and discusses research challenges that need to be surmounted to realize this vision. To this end, we define the concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a set of challenges to realize systems with these characteristics.
Combining propensity and influence models for product adoption prediction
Publisher: ACM
Date: 25-08-2015
Transforming Object-Oriented Models to Process-Oriented Models
Publisher: Springer Berlin Heidelberg
Date: 2008
The Self-Serv environment for Web services composition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2003
Community-Based Prediction of Activity Change in Skype
Publisher: ACM
Date: 25-08-2015
Structuring acyclic process models
Publisher: Elsevier BV
Date: 09-2012
Homophilic network decomposition: a community-centric analysis of online social services
Publisher: Springer Science and Business Media LLC
Date: 31-10-2016
Cost-Effective Semantic Annotation of XML Schemas and Web Service Interfaces
Publisher: IEEE
Date: 2009
DOI: 10.1109/SCC.2009.17
Opportunities and Challenges for Process Mining in Organizations: Results of a Delphi Study
Publisher: Springer Science and Business Media LLC
Date: 10-2021
DOI: 10.1007/S12599-021-00720-0
Abstract: Process mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of process mining in organizations has been far more limited. In particular, there is limited understanding of the opportunities and challenges of using process mining in organizations. Such an understanding has the potential to guide research by highlighting barriers for process mining adoption and, thus, can contribute to successful process mining initiatives in practice. In this respect, the paper provides a holistic view of opportunities and challenges for process mining in organizations identified in a Delphi study with 40 international experts from academia and industry. Besides proposing a set of 30 opportunities and 32 challenges, the paper conveys insights into the comparative relevance of in idual items, as well as differences in the perceived relevance between academics and practitioners. Therefore, the study contributes to the future development of process mining, both as a research field and regarding its application in organizations.
Framework for monitoring and testing web application scalability on the cloud
Publisher: ACM
Date: 20-08-2012
Why Am I Waiting? Data-Driven Analysis of Waiting Times in Business Processes
Publisher: Springer Nature Switzerland
Date: 2023
DOI: 10.1007/978-3-031-34560-9_11
Abstract: Waiting times in a business process often arise when a case transitions from one activity to another. Accordingly, analyzing the causes of waiting times of activity transitions can help analysts to identify opportunities for reducing the cycle time of a process. This paper proposes a process mining approach to decompose the waiting time observed in each activity transition into multiple direct causes and to analyze the impact of each identified cause on the cycle time efficiency of the process. An empirical evaluation shows that the proposed approach is able to discover different direct causes of waiting times. The applicability of the proposed approach is demonstrated in a real-life process.
Community-centric analysis of user engagement in Skype social network
Publisher: ACM
Date: 25-08-2015
Semantics and analysis of DMN decision tables
Publisher: Springer International Publishing
Date: 2016
Merging business process models
Publisher: Springer Berlin Heidelberg
Date: 2010
Quality driven web services composition
Publisher: ACM Press
Date: 2003
A sequence-based object-oriented model for video databases
Publisher: Springer Science and Business Media LLC
Date: 2002
Guest editorial: Business process management
Publisher: Elsevier BV
Date: 09-2009
Automated discovery of business process simulation models from event logs
Publisher: Elsevier BV
Date: 07-2020
Middleware support for mobile applications
Publisher: Emerald
Date: 05-2005
DOI: 10.1108/17427370580000114
Abstract: Mobile devices have received much research interest in recent years. Mobility raises new issues such as more dynamic context, limited computing resources, and frequent disconnections. A middleware infrastructure for mobile computing must handle all these issues properly. In this project we propose a middleware, called 3DMA, to support mobile computing. We introduce three requirements, distribution, decoupling and decomposition as central issues for mobile middleware. 3DMA uses a space based middleware, which facilitates the implementation of decoupled behavior and support for disconnected operation and context awareness. This is done by defining a set of “workers” which are able to act on the users behalf either: to reduce load on the mobile device, and/or to support disconnected behavior. In order to demonstrate aspects of the middleware architecture we then consider the development of a commonly used mobile application.
Configurable multi-perspective business process models
Publisher: Elsevier BV
Date: 04-2011
Design and Evaluation of a User Interface Concept for Prescriptive Process Monitoring
Publisher: Springer Nature Switzerland
Date: 2023
DOI: 10.1007/978-3-031-34560-9_21
Abstract: Prescriptive process monitoring methods recommend interventions during the execution of a process to maximize its success rate. Current research in this field focuses on algorithms to learn intervention policies that maximize the expected payoff of the interventions under certain statistical assumptions. In contrast, there has been limited attention on how to aid process stakeholders in understanding the outputs of these algorithms. In this research, we set to develop an interface to provide end users with relevant information to guide the decision on where and when to trigger interventions in a process. We draw upon an analysis of existing solutions and a review of the literature to elicit information items for a user interface for prescriptive process monitoring. Thereon, we develop a user interface concept and evaluate it with experts. The evaluation confirms the informational needs covered by the user interface concept. In addition, the evaluation shows that different end-user groups (operational users, tactical managers, and process analysts) can benefit from the information items included in the interface.
When to Intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints
Publisher: Springer International Publishing
Date: 2022
DOI: 10.1007/978-3-031-16171-1_13
Abstract: Prescriptive process monitoring approaches leverage historical data to prescribe runtime interventions that will likely prevent negative case outcomes or improve a process’s performance. A centerpiece of a prescriptive process monitoring method is its intervention policy: a decision function determining if and when to trigger an intervention on an ongoing case. Previous proposals in this field rely on intervention policies that consider only the current state of a given case. These approaches do not consider the tradeoff between triggering an intervention in the current state, given the level of uncertainty of the underlying predictive models, versus delaying the intervention to a later state. Moreover, they assume that a resource is always available to perform an intervention (infinite capacity). This paper addresses these gaps by introducing a prescriptive process monitoring method that filters and ranks ongoing cases based on prediction scores, prediction uncertainty, and causal effect of the intervention, and triggers interventions to maximize a gain function, considering the available resources. The proposal is evaluated using a real-life event log. The results show that the proposed method outperforms existing baselines regarding total gain.
Business Process Variability Modeling
Publisher: Association for Computing Machinery (ACM)
Date: 10-03-2017
DOI: 10.1145/3041957
Abstract: It is common for organizations to maintain multiple variants of a given business process, such as multiple sales processes for different products or multiple bookkeeping processes for different countries. Conventional business process modeling languages do not explicitly support the representation of such families of process variants. This gap triggered significant research efforts over the past decade, leading to an array of approaches to business process variability modeling. In general, each of these approaches extends a conventional process modeling language with constructs to capture customizable process models. A customizable process model represents a family of process variants in a way that a model of each variant can be derived by adding or deleting fragments according to customization options or according to a domain model. This survey draws up a systematic inventory of approaches to customizable process modeling and provides a comparative evaluation with the aim of identifying common and differentiating modeling features, providing criteria for selecting among multiple approaches, and identifying gaps in the state of the art. The survey puts into evidence an abundance of customizable process-modeling languages, which contrasts with a relative scarcity of available tool support and empirical comparative evaluations.
Robotic Process Mining: Vision and Challenges
Publisher: Springer Science and Business Media LLC
Date: 24-03-2020
DOI: 10.1007/S12599-020-00641-4
Abstract: Robotic process automation (RPA) is an emerging technology that allows organizations automating repetitive clerical tasks by executing scripts that encode sequences of fine-grained interactions with Web and desktop applications. Ex les of clerical tasks include opening a file, selecting a field in a Web form or a cell in a spreadsheet, and copy-pasting data across fields or cells. Given that RPA can automate a wide range of routines, this raises the question of which routines should be automated in the first place. This paper presents a vision towards a family of techniques, termed robotic process mining (RPM), aimed at filling this gap. The core idea of RPM is that repetitive routines amenable for automation can be discovered from logs of interactions between workers and Web and desktop applications, also known as user interactions (UI) logs. The paper defines a set of basic concepts underpinning RPM and presents a pipeline of processing steps that would allow an RPM tool to generate RPA scripts from UI logs. The paper also discusses research challenges to realize the envisioned pipeline.
Stage-based discovery of business process models from event logs
Publisher: Elsevier BV
Date: 09-2019
BPMN Miner: Automated discovery of BPMN process models with hierarchical structure
Publisher: Elsevier BV
Date: 03-2016
Explaining International Migration in the Skype Network
Publisher: ACM
Date: 09-2015
Scalable and Imbalance-Resistant Machine Learning Models for Anti-money Laundering: A Two-Layered Approach
Publisher: Springer International Publishing
Date: 2020
Reverse-engineering conference rankings: what does it take to make a reputable conference?
Publisher: Springer Science and Business Media LLC
Date: 11-01-2013
Discovering generative models from event logs: data-driven simulation vs deep learning
Publisher: PeerJ
Date: 12-07-2021
DOI: 10.7717/PEERJ-CS.577
Abstract: A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business process models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently, and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation approach with multiple deep learning approaches for building generative business process models. The study sheds light on the relative strengths of these two approaches and raises the prospect of developing hybrid approaches that combine these strengths.
Business Process Simulation with Differentiated Resources: Does it Make a Difference?
Publisher: Springer International Publishing
Date: 2022
DOI: 10.1007/978-3-031-16103-2_24
Abstract: Business process simulation is a versatile technique to predict the impact of one or more changes on the performance of a process. Mainstream approaches in this space suffer from various limitations, some stemming from the fact that they treat resources as undifferentiated entities grouped into resource pools. These approaches assume that all resources in a pool have the same performance and share the same availability calendars. Previous studies have acknowledged these assumptions, without quantifying their impact on simulation model accuracy. This paper addresses this gap in the context of simulation models automatically discovered from event logs. The paper proposes a simulation approach and a method for discovering simulation models, wherein each resource is treated as an in idual entity, with its own performance and availability calendar. An evaluation shows that simulation models with differentiated resources more closely replicate the distributions of cycle times and the work rhythm in a process than models with undifferentiated resources.
Property propagation rules for prioritizing and synchronizing trading activities
Publisher: IEEE Comput. Soc
Date: 2003
From business process models to process-oriented software systems
Publisher: Association for Computing Machinery (ACM)
Date: 08-2009
Abstract: Several methods for enterprise systems analysis rely on flow-oriented representations of business operations, otherwise known as business process models. The Business Process Modeling Notation (BPMN) is a standard for capturing such models. BPMN models facilitate communication between domain experts and analysts and provide input to software development projects. Meanwhile, there is an emergence of methods for enterprise software development that rely on detailed process definitions that are executed by process engines. These process definitions refine their counterpart BPMN models by introducing data manipulation, application binding, and other implementation details. The de facto standard for defining executable processes is the Business Process Execution Language (BPEL). Accordingly, a standards-based method for developing process-oriented systems is to start with BPMN models and to translate these models into BPEL definitions for subsequent refinement. However, instrumenting this method is challenging because BPMN models and BPEL definitions are structurally very different. Existing techniques for translating BPMN to BPEL only work for limited classes of BPMN models. This article proposes a translation technique that does not impose structural restrictions on the source BPMN model. At the same time, the technique emphasizes the generation of readable (block-structured) BPEL code. An empirical evaluation conducted over a large collection of process models shows that the resulting BPEL definitions are largely block-structured. Beyond its direct relevance in the context of BPMN and BPEL, the technique presented in this article addresses issues that arise when translating from graph-oriented to block-structure flow definition languages.
Management and engineering of process aware information systems : introduction to the special issue
Publisher: Elsevier BV
Date: 04-2012
Aligning Business Process Models
Publisher: IEEE
Date: 09-2009
DOI: 10.1109/EDOC.2009.11
Issue Dynamics in Github Projects
Publisher: Springer International Publishing
Date: 2015
Analyzing Web Services Networks: Theory and Practice
Publisher: Springer New York
Date: 06-08-2013
Repairing Activity Start Times to Improve Business Process Simulation
Publisher: arXiv
Date: 2022
The rise of web service ecosystems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2006
Applying the CRISP-DM data mining process in the financial services industry: Elicitation of adaptation requirements
Publisher: Elsevier BV
Date: 05-2022
Bridging Global and Local Models of Service-Oriented Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2008
Semantics and analysis of business process models in BPMN
Publisher: Elsevier BV
Date: 11-2008
Local Concurrency Detection in Business Process Event Logs
Publisher: Association for Computing Machinery (ACM)
Date: 21-01-2019
DOI: 10.1145/3289181
Abstract: Process mining techniques aim at analyzing records generated during the execution of a business process in order to provide insights on the actual performance of the process. Detecting concurrency relations between events is a fundamental primitive underpinning a range of process mining techniques. Existing approaches to this problem identify concurrency relations at the level of event types under a global interpretation. If two event types are declared to be concurrent, every occurrence of one event type is deemed to be concurrent to one occurrence of the other. In practice, this interpretation is too coarse-grained and leads to over-generalization. This article proposes a finer-grained approach, whereby two event types may be deemed to be in a concurrency relation relative to one state of the process, but not relative to other states. In other words, the detected concurrency relation holds locally, relative to a set of states. Experimental results both with artificial and real-life logs show that the proposed local concurrency detection approach improves the accuracy of existing concurrency detection techniques.
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End Date: 2024
Funder: European Research Council
View Funded ActivityRapidly Locating Items In Distribution Networks With Process-Driven Nodes
Start Date: 2007
End Date: 2009
Funder: Australian Research Council
View Funded ActivityDiagnosis And Prediction Of Business Process Deviances
Start Date: 2018
End Date: 2020
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP0773012
Start Date: 2007
End Date: 12-2010
Amount: $290,000.00
Funder: Australian Research Council
View Funded ActivityLinkage - International - Grant ID: LX0348123
Start Date: 07-2003
End Date: 07-2004
Amount: $11,400.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP0669244
Start Date: 06-2007
End Date: 12-2010
Amount: $258,920.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP0451092
Start Date: 04-2004
End Date: 12-2006
Amount: $240,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP0665480
Start Date: 2006
End Date: 12-2009
Amount: $282,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP0455394
Start Date: 07-2004
End Date: 12-2008
Amount: $141,336.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Research Hubs - Grant ID: IH210100051
Start Date: 07-2022
End Date: 07-2027
Amount: $5,000,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP0562363
Start Date: 11-2005
End Date: 06-2009
Amount: $72,444.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP180102839
Start Date: 05-2018
End Date: 12-2023
Amount: $377,784.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP150103356
Start Date: 2015
End Date: 12-2019
Amount: $847,700.00
Funder: Australian Research Council
View Funded Activity