Process mining is a set of techniques that analyze event logs in order to discover and enhance process models or to check their conformance to the event logs. Multidimensional process mining (MPM) is an emerging concept that adopts the idea of data cubes and OLAP to process mining. In recent years, di erent approaches and tools for MPM have been proposed. Despite the explorative character of MPM, such tools are still quite limited in their interactivity. For example, they lack direct interaction with process models and restrict the dynamic analysis work ow by forcing the users to follow a prede ned sequence of analysis steps. In this demo, we present a novel tool for MPM that aims to overcome these limitations in order to provide seamless interaction. It is based on a multilevel operator framework which enables the user to perform, undo, and redo the analysis steps in an arbitrary order. The de nition of variation points in the generic view model allows the user to dynamically activate or deactivate di erent perspectives on the process models and to directly interact with them.
Process mining is a set of techniques to automatically analyze (business) processes using event data which is recorded during process execution and stored in so-called event logs. Most process mining approaches aim to discover a descriptive process model from an event log (process discovery). However, there is also a wide range of other techniques. Conformance checking, for example, compares an event log to a process model either to identify deviations of the process execution from a normative process model or to measure the tness of a discovered model. Process enhancement aims to analyze additional data stored in the event log in order to annotate the process model with additional information (e.g., waiting times) to provide further perspectives.
The notion of multidimensional process mining (MPM) is an emerging
concept that adopts the concepts of data cubes and OLAP [
There are di erent approaches for MPM described in literature like Process
Cubes [
Due to its explorative character, interactivity is vital for MPM. However, tools for MPM are typically limited in their interactivity to a certain extent. 1. Interaction with process models: Even though process models are compositions of nodes and edges, they are statically visualized similar to an image. Direct interaction with process models (e.g., clicking on a node to trigger a lter) can make the analysis more intuitive. 2. Dynamic analysis work ow: Current tools for MPM force users to follow a certain work ow step by step. Changes to previously performed steps require the users to repeat the subsequent steps. Consequently, even minor adjustments of the OLAP query require a lot of e ort for the analysts. 3. Undo/redo of analysis steps: Current tools for MPM do not provide undo/redo functionality. This may restrain users from exploring the processes because returning to a previous view on the process may be laborious. 4. Performance: Long processing times may disrupt the work ow. Therefore, performance is crucial for interactivity, even though MPM is not a timecritical application.
In this paper, we introduce Interactive PMCube Explorer, a tool which aims to provide seamless interactivity to MPM. In Section 2, we present the underlying concepts of our tool and in Section 3, we describe its implementation. Section 4 gives an overview of the tool demonstration. 2
The implementation of our tool is based on the PMCube Explorer prototype [
1 A
8 A View model
10 B 10 View model
B C C User interaction 8
D 18 E
6 D
E 2
Operation Manager
Level n+m The Interactive PMCube Explorer tool is designed as a generic framework and is highly extensible. Most of its components like algorithms (e.g., for process discovery, conformance checking, process enhancement, consolidation, model di erence calculation), process models, view models, database drivers, styling operations, and other operations like lters are integrated as plug-ins which are loaded during run-time. Currently, there are more than 70 plug-ins provided. Even though the tool is a prototypical implementation, it covers the entire MPM work ow. To show the feasibility of the operator framework, it provides several operations for almost every operation level. However, some features (e.g., interactive lters and styling) are currently only implemented as a proof of concept for particular process notations. Nonetheless, we plan to extend the tool by further plug-ins in the future. The tool and a manual containing a list of all plug-ins are available for download on our website1.
Figure 2 shows a screenshot of the Interactive PMCube Explorer tool. The process mining results { which form the major subject of the analysis { are presented at the center of the application (cf. item 1). As MPM typically creates multiple sublogs and process models, the results are organized in tabs in order to easily switch between di erent results. The result overview (showing statistics about each cell), the process model matrix (presenting all mined models in a grid), and the preview for OLAP queries (indicating the resulting cells and the estimated data distribution) are also presented in tabs.
All other parts of the user interface (e.g., dialogs for the con guration of mining algorithms or OLAP queries) are arranged around the mining results. Using a docking system allows the user to customize the user interface (e.g. hiding parts or changing their location on screen). The example in Figure 2 shows the con guration of OLAP queries (cf. item 2) and the available visualization options (cf. item 3) for the currently shown process model (cf. item 1). Additional 1 http://uol.de/pmcubeexplorer information for the selected edge are shown in an additional view (cf. item 4). The history view (cf. item 5) shows the sequence of performed operations separately for each operation level. For each level, an undo/redo of operations is available. 4
In the demo scenario, we give a walk-through of an example analysis using the Interactive PMCube Explorer tool. A screen-cast of our demo is available on the web2. It demonstrates the key features of the tool with a special focus on its interactivity. The intention is to give an impression of the concepts like the operator framework, the change propagation, the visualization operations and the direct interaction with process models. Therefore, the OLAP queries and analysis steps of the demonstration are selected with focus on the di erent features. For the demo scenario, we use the data set of the BPI Challenge 20173.
As target audience, we are addressing researchers as well as practitioners with an interest in multidimensional process mining. Besides, this demo might also be interesting for the process mining community in general as the operator framework can also be incorporated in traditional process mining tools which may also bene t from a seamless and intuitive interaction with the user.