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ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance and scalability, ELKI offers many data index structures such as the R*-tree that can provide major performance gains. ELKI is designed to be easy to extend for researchers and students in this domain, and welcomes contributions in particular of new methods. ELKI aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms.

SEPIA provides implementations for various types of Petri nets. Along Place/Transition-nets, it supports Petri nets with distinguishable token colors and defines coloured workflow nets, where coloured tokens are interpreted as data elements used during process execution. To support information flow analysis of processes, SEPIA defines so-called IF-Nets, tailored for security-oriented workflow modeling which enable users to assign security-levels (HIGH, LOW) to transitions, data elements and persons/agents participating in the process execution.