The tool IntHealth helps identify the executed process in ER, through data mining and process mining techniques, and include a series of rules to predict the discharge hospitalization of patients based on historical information stored in event logs. It enables ER experts to improve their knowledge of the process and help them make better and faster decisions. Including data mining rules for predicting hospitalization and process mining techniques in IntHealth, can reduce waiting times by discharging patients for hospitalization faster and releasing ER boxes.
IntHealth is available in here.
ResRec, a novel Multi-factor Criteria tool that can be used to recommend and allocate resources dynamically. ResRec is a decision maker-oriented approach that provides the feature of solving individual requests (On-demand), or requests made in blocks (Batch) through a recommender system developed in ProM.
ResRec is available in here.
Process Mining UC is collaborative part of the ProM initiative. ProM is an extensible framework that supports a wide variety of Process Mining techniques in the form of plug-ins. It is platform independent as it is implemented in Java, and can be downloaded free of charge. The process mining tools result of our research are available as plug-ins in ProM (mostly under the nightly-builds version). We welcome and support practical applications of those plug-ins, and we invite researchers and developers to contribute to ProM in the form of new plug-ins.
ProM is available in www.promtools.org
Process Mining UC is collaborative part of the PMLAB initiative. PMLAB is an scripting environment for Process Mining in Python. On top of IPython, it allows to perform exploratory process-oriented computing and/or research in a process-oriented language.In this language, logs, models and many other high-level objects/tasks are first-class citizens, meaning that one can compute (interactively or not) on the basis of these elements. Importantly, there can be different granularities on the view of these high-level elements, e.g., a log can be simply passed to a discovery algorithm (coarse-level view), or analyzed to derive the most frequent cases (introspective view).
PMLAB is available in here