Generalized Conformance Checking

Recently, we submitted a work to the BPM conference with the title “In Log and Model We Trust? – A Generalized Conformance Checking Framework”.
The work is dealing with a problem, where we are given a process log and a process model, but both can have errors. That is, we might have different degrees of trust in the correctness of each of the artifacts. Imagine a process model created by a novice, or not capturing the process in it’s entirety. In this situation we might want to pull the model closer to the behavior reflected in the log. Conversely, the log can also have anomalies, outliers, noise or missing entries. Then, we might have a lower degree of trust in the log and want to pull it more towards the modeled behavior.

The proposed approach solves both problems in one go, when it tries to find a model-log pair that is well-fitting each other, and also not too far away from the input model and input log (in terms of edit distance). We applied this general framework to the formalism of process trees as model representation and event logs.

The attached screencast shows the GeneralizedConformance plugin in Prom in action. Currently, it requires two inputs: a Process Tree model, and an Event Log. If you only have an Event Log, you can simply first mine a process tree, as shown in the screencast.

Final call for Papers: IEEE EDOC conference 2016

This is the final call for papers for the IEEE EDOC conference 2016.
The deadline for full papers has been extended to April 7th! Thus, there is one week left to prepare your papers for the main conference! Don’t miss the opportunity to submit your paper(s)!

Also check out the webpage for updates and news:

More information:
IEEE EDOC 2016 – The 20th IEEE International EDOC Conference

EDOC 2016 – Vienna, Austria
September 05-09, 2016

IEEE EDOC 2016 is the twentieth conference in a series that provides the key forum for researchers and practitioners in the field of enterprise computing. EDOC conferences address the full range of models, methodologies, and engineering technologies contributing to intra- and inter-enterprise application systems. Since 1997, EDOC has brought together leading computer scientists, IT decision makers, enterprise architects, solution designers, and practitioners to discuss enterprise computing challenges, models and solutions from the perspectives of academia, industry, and government. The EDOC conference series emphasizes a holistic view on enterprise applications engineering and management, fostering integrated approaches that address and relate business models, business processes, people and technology.

EDOC 2016 welcomes high quality scientific submissions as well as experience papers on enterprise computing from industry. The main theme of EDOC 2016 is ”Enabling innovative business models in the enterprise of the future” and seeks to explore innovative approaches synthesizing concepts of (1) data science, (2) enterprise computing and (3) social computing.

Expert panel discussions and keynotes will address current topics and issues in this domain.


The EDOC 2016 conference seeks high-quality contributions addressing the domains, life-cycle issues, and realization technologies involved in building, deploying and operating enterprise computing systems. Suggested areas include, but are not limited to:

Enterprise Architecture and Enterprise Application Architecture
* Enterprise architecture frameworks
* Enterprise architecture analysis, assessment and prediction
* Cloud computing and the evolution of enterprise architectures
* Enterprise ontologies
* Innovative approaches to architecture management

Model-based Approaches
* Model-driven architectures and model-driven software development
* Modeling based on domain specific languages (DSL)
* Approaches based on reference architectures
* Collaborative development and cooperative engineering issues

Service-oriented Architectures (SOA) and Enterprise Service Architectures (ESA)
* Service engineering and evolution of related specifications
* Semantics-based service engineering
* Service composition, orchestration and choreography
* Governance in Service-oriented ArchitecturesService policies, contract definition and enforcement
* Security/privacy policy interoperability

Business process management (BPM)
* Business process modeling, verification, configuration and implementation
* Process-aware information systems (PAIS), Human-centric PAIS, Social BPM
* Managing business process variability, adaptation and evolution in PAIS
* Process mining and its application in business analytics
* Distributed and cross-organizational business processes
* Data-intensive processes
* Cloud impact on BPM, business processes in the cloud
* Adaptive case management and data-driven processes

Business analytics
* Modeling and predictive analytics for enterprise computing
* Data-driven enterprise strategy
* Collaboration enterprise analytic platforms
* Business process intelligence (e.g., process performance management)
* Continuous, online analytics for big data in the enterprise
* Natural language processing in enterprise systems

Business rules
* Business rule languages and engines
* Relation between business rules and business processes
* Business rules and service computing
* Business rules and compliance management, business process compliance

Information integration and interoperability
* Business object modeling methodologies and approaches
* Taxonomies, ontologies and business knowledge integration
* Master data management, data mining and (real-time) data warehousing
* Flexible information models and systems (e.g., object-driven processes)
* Data quality and trustworthiness
* Complex event processing and event-driven architectures

Networked Enterprise Solutions
* Enterprise interoperability, collaboration and its architecture
* Virtual organizations, including multi-agent system support
* Cross-enterprise collaboration in a world of cloud, social and big data
* Digital platforms and ecosystems
* Trust management

Enterprise applications deployment and governance
* Performance and operational risk prediction and measurement
* Quality of service (QoS) and cost of service (CoS)
* Management and maintenance of enterprise computing systems
* Information assurance
* Human and social organizational factors in enterprise computing

Emerging trends in distributed enterprise applications
* Social information and innovation networks, social media impact on the enterprise
* People-centric collaboration systems, people-centric services
* Private and public cloud computing Infrastructures
* Idea management and crowdsourcing
* Enterprise 2.0, Web 2.0 and beyond
* Mobile enterprise services
* Industry specific solutions (e.g. for aerospace, automotive, finance, logistics, medicine and telecommunications)
* Research and public sector collaboration (e.g. in e-health, e-government, e-science)

Important Dates:

Conference full paper submission due: April 7, 2016
Conference paper acceptance notifications: May 30, 2016
Conference camera ready papers due: July 1, 2016

Workshop Proposal Submission: January 15, 2016
Workshop Proposal Notification: February 1, 2016
Workshop Paper Submission: April 15, 2016
Workshop Paper Notification: June 13, 2016
Workshop Camera Ready: July 1, 2016

Workshops: September 05-06, 2016
Conference: September 05-09, 2016

Conference Committees

General Chair
Stefanie Rinderle-Ma, University of Vienna, Austria

PC Chairs
Florian Matthes, TU Munich, Germany
Jan Mendling, WU Vienna, Austria

Workshop Chairs
Remco Dijkman, TU Eindhoven, The Netherlands
Luís Ferreira Pires, University of Twente, The Netherlands

Demo Chairs
Walid Fdhila, University of Vienna, Austria
Stefan Schulte, Technical University of Vienna, Austria

Publicity Chair
Andreas Rogge-Solti, WU Vienna, Austria

Local Organization Chair
Monika Hofer-Mozelt, University of Vienna, Austria

Web Chairs
Georg Kaes, University of Vienna, Austria
Manuel Gall, University of Vienna, Austria

Information Systems paper

My paper Prediction of business process durations using non-Markovian stochastic Petri nets with Mathias Weske was accepted for publication in the Information Systems journal of Elsevier.

You can find the preprint version in the publications area.

Or get a free author’s copy now!


Companies need to efficiently manage their business processes to deliver products and services in time. Therefore, they monitor the progress of individual cases to be able to timely detect undesired deviations and to react accordingly. For example, companies can decide to speed up process execution by raising alerts or by using additional resources, which increases the chance that a certain deadline or service level agreement can be met. Central to such process control is accurate prediction of the remaining time of a case and the estimation of the risk of missing a deadline.

To achieve this goal, we use a specific kind of stochastic Petri nets that can capture arbitrary duration distributions. Thereby, we are able to achieve higher prediction accuracy than related approaches. Further, we evaluate the approach in comparison to state of the art approaches and show the potential of exploiting a so far untapped source of information: the elapsed time since the last observed event. Real-world case studies in the financial and logistics domain serve to illustrate and evaluate the approach presented.



BPM paper accepted

My paper with Gjergji Kasneci on Temporal Anomaly Detection in Business Processes has been accepted to BPM 2014.

The paper describes an approach to detect anomalies in the timestamps of events that happen during process execution. The approach is based on historical observations of usual behavior and uses a stochastic model to find outliers in a non-parametric way. It helps process analysts to identify and filter errors from process logs, and also to distinguish between measurement errors (where only one timestamp is the outlier) and real delays (where succeeding activities are also shifted).

Paper Abstract:

The analysis of business processes is often challenging not only because of intricate dependencies between process activities but also because of various sources of faults within the activities. The automated detection of potential business process anomalies could immensely help business analysts and other process participants detect and understand the causes of process errors.
This work focuses on temporal anomalies, i.e., anomalies concerning the runtime of activities within a process. To detect such anomalies, we propose a Bayesian model that can be directly inferred form the Petri net representation of a business process. Probabilistic inference on the above model allows the detection of non-obvious and interdependent temporal anomalies.