Our Research Projects ===================== ### ActiTraC ActiTraC is a novel trace clustering approach based on active learning which significantly outperforms other clustering techniques when applied on complex, large event logs. [→ Read More](/actitrac) ### ActiTraC-MO ActiTraC-MO is an adapted version of ActiTraC that constructs traces clusters in parallel rather than sequential, allowing for a multi-objective approach to trace clustering. [→ Read More](/multiobjective) ### AGNEsMiner AGNEsMiner is a declarative machine learning technique that represents the problem of process discovery as a multi-relational classification problem on event logs supplemented with Artificially Generated Negative Events (AGNEs). [→ Read More](/agnesminer) ### CoBeFra CoBeFra is a comprehensive benchmarking suite to set up large scale conformance checking experiments and is used and loved by many researchers in the field. [→ Read More](/cobefra) ### Context-aware Petri nets Context-aware Petri nets (CAPN) enable the straightforward use of ontologies in Petri nets. [→ Read More](/capn) ### Declare Execution Environment This tool allows users to enact Declare models that are annotated with hidden dependency information. [→ Read More](/declareexecutionenvironment) ### Determining the Number of Trace Clusters: a Stability-based Approach Determining the appropriate number of trace clusters in an event log based on the stability of the clustering. [→ Read More](/clusterstability) ### Expert Knowledge in Trace Clustering ActiTraC-SemiSupervised is an adapted version of ActiTraC that incorporates expert knowledge into its clustering approach. [→ Read More](/expertdriventraceclustering) ### Fodina Fodina is a cutting-edge robust, flexible, and scalable heuristic dependency-based process discovery technique. [→ Read More](/fodina) ### Fusion Miner Fusion Miner is the first process discovery approach for mixed-paradigm process models with intertwined state spaces. [→ Read More](/fusionminer) ### FusionMINERFul FusionMINERFul is a fast mixed-paradigm process mining algorithm with self-learning flexibility analysis and model checker. [→ Read More](/fusionminerful) ### Location-Aware Process Modeling and Execution Integrating location-aware aspects with process flow concerns using Coloured Petri nets. [→ Read More](/locationaware) ### Negative Event Based Conformance Checking Blazingly fast and precise conformance checking using negative events. For recall, precision, and generalization assessments. [→ Read More](/neconformance) ### Petri net Event Log Generator An easy-to-use Petri net driven event log generator with timing, resource, and flow options. Great to set up experiments with synthetic models. [→ Read More](/loggenerator) ### P-MInD P-MInD is a framework for discovering holistic decision models from event logs. [→ Read More](/PMInD) ### Representation Learning for Business Processes Embeddings applied in the world of business processes: act2vec, trace2vec, log2vec and model2vec. [→ Read More](/replearn) ### Real-time Decomposed Conformance Checking Step one: decompose a model. Step two: apply negative event based conformance checking. Step three: visualize and monitor conformance issues and deviations in real time. [→ Read More](/rtconformance) ### Rozinat's Conformance Framework Port for ProM 6 Dr. Anne Rozinat's conformance checker framework remains one of the most well-known and seminal conformance checking frameworks in the process mining community. Sadly, its implementation—a ProM 5 plugin—has never been ported to ProM 6. Until now. [→ Read More](/rozinatconformance) ### Rozinat's Decision Mining Framework Port for ProM 6 Dr. Anne Rozinat's decision mining framework port for ProM 6. [→ Read More](/rozinatdecmin) ### SECPI Explaining why process instances are put in a particular cluster based on machine learning on features generated from control flow aspects. [→ Read More](/svmexplainer) ### A Stability-assessment Framework for Process Discovery Techniques Assessing the stability of process discovery techniques based on log-perturbation and process model similarity. [→ Read More](/PDStability)