Process mining is gaining more and more attention both in industry and practice. As such, the number of process mining products is steadily increasing. However, none of these products allow for composing and executing analysis work ows consisting of multiple process mining algorithms. As a result, the analyst needs to perform repetitive process mining tasks manually and scienti c process experiments are extremely labor intensive. To this end, we have RapidMiner 5, which allows for the de nition and execution of analysis work ows, connected with the process mining framework ProM 6. As such any discovery, conformance, or extension algorithm of ProM can be used within a RapidMiner analysis process thus supporting process mining work ows.
During last decade, process mining has become a mature technique for analyzing
all kinds of business processes based on a so-called event log [
Within the scienti c work ow domain, special kinds of work ow management systems exist which are designed to compose and execute a series of computational or data manipulation steps (e.g. RapidMiner, KNIME, and Taverna). When applying scienti c work ow concepts in the process mining eld, several advantages can be realized. For example, comparable process mining analyses can be repeated by just one-click of a button and scienti c experiments can be executed in an automated fashion. Furthermore, within several scienti c work ow management systems many data and machine learning techniques are readily available. As such, di erent techniques can be easily combined for an end-to-end analysis.
To this end, we have integrated the process mining framework ProM 6 [
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We rst provide a general introduction to RapidMiner. Then we present the extension which supports process mining analysis work ows.
RapidMiner is a software product allowing for advanced analytics, i.e.
sophisticated quantitative methods (for example, statistics, descriptive and predictive
data mining, simulation and optimization) to produce insights that traditional
approaches to Business Intelligence (BI) are unlikely to discover [
ProM operators
Looking at advanced analytics in general, one drawback is that processes are not made explicit. As a result, it is useful to extend RapidMiner with process mining capabilities. In the sequel, we will focus on the process mining capabilities that are available within the extension. In Figure 1 a screenshot of RapidMiner is provided thereby showing a process mining analysis within RapidMiner.
First, within the \Operators" panel at the left side all the operators that are available can be selected. There is a special \ProM6" folder in which all the process mining operators can be found. A description can be found in Table 1. Most of the operators correspond to existing ProM plug-ins.
In the \Process" panel in Figure 1 some of the available operators can be seen together with a visualization of the obtained results. First an event log is read (Read Log operator). Afterwards, a Petri net is discovered using the ILP miner (ILP Miner operator) and a dotted chart is created showing events in a graphical way such that a \helicopter overview" of the process is obtained (Analyse using Dotted chart operator). Finally, timing information is projected on the Petri net so that bottlenecks can be identi ed within the process (Replay a Log on Petri Net for Performance / Conformance Analysis operator).
A wide variety of work ows can be made. Some examples are: { By using the Loop Parameters operator it is possible to iterate over a selection of operators for a set of parameter combinations. For example, the ILP Miner is repeated for each di erent option of its \Variant" parameter. { For each item in a collection of objects, the same work ow can be executed.
For example, the Guide Tree Miner operator provides a collection of logs.
Subsequently, for each log the corresponding Petri net can be discovered. { Within RapidMiner many data mining algorithms are available which can be used after converting the log into a feature set. For example, using the Case Data Extractor operator the log is converted into a feature set and subsequently a decision tree is obtained using the Decision Tree operator. { Within RapidMiner also many statistical techniques are available. These can be used for evaluation of process mining experiments. For example, by using the Loop Attributes operator and the Replay a Log on Petri Net for Performance / Conformance Analysis operator, for a varying number of maximal states, the tness between a log and a Petri net can be calculated. Afterwards, using the Linear Regression operator, the strength of the relationship between the tness and the number of states can be determined. 3
In this section, we elaborate on the architecture of ProM 6 and RapidMiner and how both are connected. Furthermore, we focus on some implementation details.
To this end, in Figure 2 an architectural overview of RapidMiner and ProM 6 is given and how they are connected. First, the most important part of ProM 6 is the framework which roughly spoken contains all the necessary functionalities in order that process mining algorithms can be executed. The algorithms itself are provided by means of packages. A package may contain one or more plug-ins and a collection of provided objects that are needed or produced by the plugin. Furthermore, a plug-in needs a context to run in. Depending on the type of context, the plug-in communicates in a di erent way with a user. Here, it is important that there is a clear separation between the actual process mining algorithm and the visualization of its results. Also, there is a clear separation between the plug-in and the parameter settings it needs. At the moment, two types of contexts are available: a GUI-aware context, called UITopia, and a headless context. So, a plug-in that is running in the UITopia context may communicate with the user through dialogs and/or wizards, whereas for a plugin running in a headless context this is not obliged. So, the latter plug-ins can be ran using a client. In case such a plug-in requires parameter settings, these can be provided via its own input parameter object. Moreover, for the object that has been obtained, a visualization can be obtained by running the associated visualization plug-in.
Extensions RapidMiner
ProM 6 extension
Context Client User
Extensions
ProM Framework
Files Packages Fig. 2: Architectural overview of ProM and RapidMiner and how they are connected.
The headless context of ProM 6 is used in order that ProM plug-ins can be
executed within RapidMiner. As can be seen in Figure 2, RapidMiner consists of
a core in order that operators can be executed. New operators can be added by
means of an extension together with the objects that are needed by the operators
[
Currently, over 40 operators are available covering a selection of the plug-ins that are available within ProM 6. In case a plug-in runs in a headless context it can easily be added to the ProM extension. In order to speed up this process, we have developed a speci c ProM plugin, called RapidMiner Code Generator Plug-in, that generates code for adding the plug-in.
The ProM extension has been tested for many scenarios. Furthermore, students following the \Advanced Process Mining" course at TU/e are using the software in order to do the assignments. In the end, we want to achieve that the extension becomes robust and mature in order that it can be successfully used by many people both from industry and science. Currently, the extension has been downloaded over 850 times at the RapidMiner marketplace1. 4
For the ProM extension of RapidMiner, a dedicated website is available: http: //www.rapidprom.org. Amongst others, this website contains instructions for installing the extension, instructions for using it, it describes several use cases, and several screencasts.
Acknowledgements This research is supported by the Dutch Technology Foundation STW, applied science division of NWO and the Technology Program of the Ministry of Economic A airs. 1 http://marketplace.rapid-i.com/