Looking Back: Process Mining at BPM 2014

Our Process Mining Party was awesome! (photo taken by Sander Leemans)

A Happy New Year everyone! We start the year by looking back to 2014 for our annual Process Mining at BPM post1.

In 2014, there was an insane amount of process mining papers at the BPM conference. As always, we have looked through all the main conference and workshop papers to find the ones that are related to process mining and contacted the authors of the papers that were not yet publicly available.

You can find full-paper links to the publications below and we will keep adding new links from authors who have not responded yet. If we missed something please let us know.

Main Conference

The BPM conference is a very competitive conference with hundreds of papers being submitted to the main track and just around 20+ of them are accepted. It’s incredible that 14 of them fall into the process mining research area. Below you find the links to the papers and the slides, along with a short summary:

Discovering Target-Branched Declare Constraints by Claudio Di Ciccio, Fabrizio Maria Maggi, and Jan Mendling from Vienna University of Business and Economics, Austria, and University of Tartu, Estonia (download slides)

An alternative to discovering process models is to discover a set of declarative rules, restricting the allowed behavior “from the outside” rather than explicitly outlining the paths that are possible. However, a challenge for complex processes is that the discovery of declarative processes often also results in hundreds of constraints (another encounter of the so-called “spaghetti” problem). The work of Claudio and his colleagues addresses the explosion of branching constraints by mining Target-Branched constraints.

Crowd-Based Mining of Reusable Process Model Patterns by Carlos Rodrguez, Florian Daniel, and Fabio Casati from the University of Trento, Italy (download slides)

Rather than discovering process models from data, Carlos and his colleagues investigate the discovery of model patterns, for example to provide recommendations during process modeling. While there are automated methods such as frequent sub-graph mining, they explore an approach where the pattern identification is implemented through humans in a crowdsourcing environment. The approach is tested to discover data flow-based mashup models.

A Recommender System for Process Discovery by Joel Ribeiro, Josep Carmona, Mustafa Msr, and Michele Sebag from Universitat Politcnica de Catalunya, Spain and TAO, INRIA Saclay - CNRS - LRI, Universite Paris Sud XI, Orsay, France (download slides)

There are dozens of different process mining algorithms with different strengths and weaknesses, and even built on different formalisms (e.g., Petri nets, BPMN, EPC, Causal nets). So, selecting the right one and using it correctly is a daunting task. Joel and his colleagues have worked out a recommender system to find the best discovery algorithm for the data at hand. This way, the users can get a recommendation for which algorithm to use. Log features such as the average trace length and measures such as fitness and precision are the basis for the recommendation.

Beyond Tasks and Gateways: Discovering BPMN Models with Subprocesses, Boundary Events and Activity Markers by Raffaele Conforti, Marlon Dumas, Luciano Garca-Bauelos, and Marcello La Rosa from Queensland University of Technology, Australia, and University of Tartu, Estonia (download slides)

Existing process mining techniques generally produce flat process models. The authors developed a technique for automatically discovering BPMN models containing subprocesses (based on a set of attributes that includes keys to identify (sub)process instances, and foreign keys to identify relations between parent and child processes), interrupting and non-interrupting boundary events, and activity markers. The discovered process models are more modular, but also more accurate and less complex than those obtained with flat process discovery methods.

A Genetic Algorithm for Process Discovery Guided by Completeness, Precision and Simplicity by Borja Vzquez-Barreiros, Manuel Mucientes, and Manuel Lama from the University of Santiago de Compostela, Spain (download slides)

The authors present a new genetic process discovery algorithm with a hierarchical fitness function that takes into account completeness, precision and simplicity. The algorithm has been tested with 21 different logs and was compared with two state of the art algorithms.

Constructs Competition Miner: Process Control-Flow Discovery of BP-Domain Constructs by David Redlich, Thomas Molka, Wasif Gilani, Gordon Blair, and Awais Rashid from SAP Research Center Belfast, Lancaster University, and University of Manchester, United Kingdom (download slides)

A new process discovery algorithm is proposed that follows a top-down approach to directly mine a process model which consists of common business process constructs (in a language familiar to the business analyst rather than Petri nets or other languages preferred by academic scholars). The discovered process model represents the main behaviour and is based on a competition of the supported constructs.

Mining Resource Scheduling Protocols by Arik Senderovich, Matthias Weidlich, Avigdor Gal, and Avishai Mandelbaum from Technion, Israel and Imperial College London, United Kingdom (download slides)

Their contribution fits under the umbrella of operational process mining, similar to other techniques aiming to predict wait times and case completion times. The paper focuses on service processes, where performance analysis is particularly important, and does not only take the load information into account but also the order of activities that a service provider follows when serving customers. A data mining technique and one based on queueing heuristics are tested based on a large real-live data set from the telecom sector.

Temporal Anomaly Detection in Business Processes by Andreas Rogge-Solti from Vienna University of Economics and Business, Austria and Gjergji Kasneci from Hasso Plattner Institute, University of Potsdam, Germany (download slides)

This paper focuses on temporal aspects of anomalies in business processes. The goal is to detect temporal outliers in activity durations for groups of interdependent activities automatically from event traces. To detect such anomalies, the authors propose a Bayesian model that can be automatically inferred form the Petri net representation of a business process.

A General Framework for Correlating Business Process Characteristics by Massimiliano de Leoni, Wil van der Aalst, and Marcus Dees from the University of Padua, Italy, Eindhoven University of Technology, The Netherlands, and Uitvoeringsinstituut Werknemersverzekeringen (UWV), The Netherlands (download slides)

The authors provide a general framework for deriving and correlating process characteristics and therewith unify existing ad-hoc solutions for specific process questions. First, they show how the desired process characteristics can be derived and linked to events. Then, they show that we can derive the selected dependent characteristic from a set of independent characteristics for a selected set of events.

The Automated Discovery of Hybrid Processes by Fabrizio Maria Maggi from University of Tartu, Estonia, Tijs Slaats from IT University of Copenhagen, Denmark, and Hajo A. Reijers from Eindhoven University of Technology, The Netherlands (download slides)

This paper presents an automated discovery technique for hybrid process models: Less-structured process parts with a high level of variability can be described in a more compact way using a declarative language. Procedural process modeling languages seem more suitable to describe structured and stable processes. The proposed technique discovers a hybrid process model, where each of its sub-processes may be specified in a declarative or procedural fashion, leading to overall more compact models.

Declarative Process Mining: Reducing Discovered Models Complexity by Pre-Processing Event Logs by Pedro H. Piccoli Richetti, Fernanda Araujo Baio, and Flvia Maria Santoro from the Federal University of the State of Rio de Janeiro, Brazil (download slides)

The authors present a new discovery approach for declarative models that aims to address the problem that existing declarative mining approaches still produce models that are hard to understand, both due to their size and to the high number of restrictions of the process activities. Their approach reduces declarative model complexity by aggregating activities according to inclusion and hierarchy semantic relations.

SECPI: Searching for Explanations for Clustered Process Instances by Jochen De Weerdt and Seppe vanden Broucke from KU Leuven, Belgium (download slides)

Trace clustering is an approach to group process instances in similar groups, however usually does not provide insight into on which basis these groups were formed. This paper presents a technique that assists users with understanding a trace clustering solution by finding a minimal set of control-flow characteristics whose absence would prevent a process instance from remaining in its current cluster.

Business Monitoring Framework for Process Discovery with Real-Life Logs by Mari Abe and Michiharu Kudo from IBM Research, Tokyo, Japan (download slides)

This paper proposes a monitoring framework for process discovery that simultaneously extracts the process instances and metrics in a single pass through the event log. Instances of monitoring contexts are linked at runtime, which allows to build process models from different metrics without reading huge logs again.

Predictive Task Monitoring for Business Processes by Cristina Cabanillas, Claudio Di Ciccio, and Jan Mendling from Institute for Information Business at Vienna, and Anne Baumgrass from Hasso Plattner Institute at the University of Potsdam, Germany (download slides)

Event logs of running processes can be used as input for predictions around business processes. The authors extend this idea by also including misbehaviour patterns on the level of singular tasks associated with external events such as from GPS or RFID systems and demonstrate the use case based on a scenario from the smart logistics area.

Workshops

The workshops always take place the day before the main conference starts, are smaller, have a specific theme, and also provide the space to explore and discuss new ideas. Normally, mostly the BPI workshop is the main target for process mining papers but last year the theme runs like a read thread through almost all of the workshops:

The 10th International Workshop on Business Process Intelligence (BPI), as always, had lots of process mining contributions:

The 7th Workshop on Business Process Management and Social Software (BPMS2) focused on social software as a new paradigm and had one process mining paper:

The 3rd Workshop on Data- & Artifact-centric BPM (DAB) specializes on data-centric processes and also had a contribution in the process mining area:

The 2nd International Workshop on Decision Mining & Modeling for Business Processes (DeMiMoP) looked specifically into decisions in relation to processes and had three process mining papers:

The 3rd Workshop on Security in Business Processes (SBP) featured two process mining contributions plus a practitioner keynote on the topic:

Finally, the 3rd International Workshop on Theory and Applications of Process Visualization (TaProViz) also had a practitioner keynote on process mining and two more papers in this area:

More Process Mining

There was actually even more process mining going on than we can cover here. Andrea Burattin received the Best Process Mining PhD thesis award. There were demos. CKM Advisors won the BPI Challenge (the team of Gabriele Cacciola from the Universiy of Calabria won the student challenge). The annual IEEE Task force meeting took place. And we had an awesome process mining party.

What you can see from all the new contributions above is that process mining is as active a research area as never before. It’s an exciting area to work in and there are still so many topics that have not been addressed yet.

This year’s BPM conference takes place in Innsbruck. If you are a researcher, you should mark the deadlines and try to be there!


  1. You can find earlier editions for 2012 and 2013. ↩︎

Anne Rozinat

Anne Rozinat

Market, customers, and everything else

Anne knows how to mine a process like no other. She has conducted a large number of process mining projects with companies such as Philips Healthcare, Océ, ASML, Philips Consumer Lifestyle, and many others.