=Paper=
{{Paper
|id=Vol-1920/BPM_2017_paper_161
|storemode=property
|title=Improving Interactivity in Multidimensional Process Mining: The Interactive PMCube Explorer Tool
|pdfUrl=https://ceur-ws.org/Vol-1920/BPM_2017_paper_161.pdf
|volume=Vol-1920
|authors=Thomas Vogelgesang
|dblpUrl=https://dblp.org/rec/conf/bpm/Vogelgesang17
}}
==Improving Interactivity in Multidimensional Process Mining: The Interactive PMCube Explorer Tool==
https://ceur-ws.org/Vol-1920/BPM_2017_paper_161.pdf
Improving Interactivity in Multidimensional
Process Mining: The Interactive PMCube
Explorer Tool
Thomas Vogelgesang
Department of Computer Science
University of Oldenburg, Germany
thomas.vogelgesang@uni-oldenburg.de
Abstract. Process mining is a set of techniques that analyze event logs
in order to discover and enhance process models or to check their con-
formance 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, different 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
workflow by forcing the users to follow a predefined sequence of anal-
ysis 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
definition of variation points in the generic view model allows the user to
dynamically activate or deactivate different perspectives on the process
models and to directly interact with them.
1 Introduction
Process mining is a set of techniques to automatically analyze (business) pro-
cesses using event data which is recorded during process execution and stored in
so-called event logs. Most process mining approaches aim to discover a descrip-
tive 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 exe-
cution from a normative process model or to measure the fitness 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 con-
cept that adopts the concepts of data cubes and OLAP [2] to the field of process
mining [3] in order to analyze the processes from multiple perspectives. By using
OLAP operators like roll-up and slice, it is possible to change the granularity
of the event data or to filter it. One major goal of MPM is the comparison of
different processes or variants process of the same process which are represented
by the cells of the data cube.
There are different approaches for MPM described in literature like Process
Cubes [4,1] and PMCube [6,5]. Both approaches use OLAP operations to par-
tition the event data into subsets (sublogs) which are independently analyzed
using arbitrary process mining algorithms. The main differences are the way
the data cube is organized and the definition of OLAP operators. Furthermore,
PMCube introduces also additional concepts to support the comparison of the
extracted models (e.g., difference visualization). However, both approaches are
similar in their general work-flow.
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 composi-
tions 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
filter) can make the analysis more intuitive.
2. Dynamic analysis workflow: Current tools for MPM force users to follow a
certain workflow step by step. Changes to previously performed steps re-
quire the users to repeat the subsequent steps. Consequently, even minor
adjustments of the OLAP query require a lot of effort 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 workflow. Therefore,
performance is crucial for interactivity, even though MPM is not a time-
critical 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 Architecture
The implementation of our tool is based on the PMCube Explorer prototype [5].
Besides several improvements of the software architecture, it provides a newly de-
signed user interface and workflow to improve the interactivity of MPM. There-
for, it manages the different analysis steps of the MPM workflow by a novel
operator framework which defines a stack of operation levels. Each level repre-
sents particular analysis steps of the MPM workflow (e.g., OLAP query, process
discovery, process enhancement). Interactions with user interface are mapped
onto operations of the related level. The framework individually manages these
operations for each level. This allows the user to undo/redo operations of a par-
ticular analysis step without affecting other levels. Also changes on lower levels
are propagated to the levels above to automatically update the process mining
results. This ensures that previously defined analysis steps do not need to be
redefined after executing new operations on lower levels. E.g., the user does not
need to configure and apply a process discovery algorithm again after changing
the underlying OLAP query. For a more detailed description of the underlying
concept, we refer to [7].
1 Operation Manager
Level n+m
2
... 3
Level n
...
User interaction Level 1
View model Update
4
event
10 B 10
A D 18 E 6
Visualization Manager
8 C 8
Styling
View model 5
B Translation
A D E
C
Fig. 1. General concept of the Interactive PMCube Explorer tool
Figure 1 shows the general concept of the operator framework. When the user
interacts with the user interface, e.g. by applying a filter to the data (cf. item
1), the framework creates a new operation and adds it to the list of operations
of the respective operation level (cf. item 2). Then, all operations of this and the
subsequent levels are executed to propagate the changes (cf. item 3). After that,
an update event triggers the visualization manager to update the currently dis-
played process models (cf. item 4). Therefor, the Visualization Manager creates
a new view model and applies all activated styling operations to it (cf. item 5).
Finally, the updated view model is shown in the user interface (cf. item 6).
3 Implementation
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 discov-
ery, conformance checking, process enhancement, consolidation, model difference
calculation), process models, view models, database drivers, styling operations,
and other operations like filters are integrated as plug-ins which are loaded dur-
ing run-time. Currently, there are more than 70 plug-ins provided. Even though
the tool is a prototypical implementation, it covers the entire MPM workflow.
To show the feasibility of the operator framework, it provides several operations
for almost every operation level. However, some features (e.g., interactive filters
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 .
Fig. 2. Screenshot of the Interactive PMCube Explorer tool
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 different 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 configuration 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 configuration 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 Demo Scenario
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 different
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 benefit from a seamless and intuitive interaction with the user.
References
1. Alfredo Bolt and Wil M.P. van der Aalst. Multidimensional Process Mining Using
Process Cubes. In K. Gaaloul et al., editors, Enterprise, Business-Process and
Information Systems Modeling, volume 214 of Lecture Notes in Business Information
Processing, pages 102–116. Springer International Publishing, 2015.
2. Matteo Golfarelli and Stefano Rizzi. Data Warehouse Design: Modern Principles
and Methodologies. McGraw-Hill, Inc., New York, NY, USA, 1 edition, 2009.
3. IEEE Task Force on Process Mining. Process Mining Manifesto. In F. Daniel,
K. Barkaoui, and S. Dustdar, editors, Business Process Management Workshops
(1), volume 99 of LNBIP, pages 169–194. Springer, 2011.
4. Wil M. P. van der Aalst. Process Cubes: Slicing, Dicing, Rolling Up and Drilling
Down Event Data for Process Mining. In M. Song, M. Wynn, and J. Liu, editors,
Asia Pacific Business Process Management, volume 159 of Lecture Notes in Business
Information Processing, pages 1–22. Springer International Publishing, 2013.
5. Thomas Vogelgesang and H.-Jürgen Appelrath. Multidimensional Process Mining
with PMCube Explorer. In F. Daniel and S. Zugal, editors, BPM Demo 2015,
volume 1418 of CEUR Workshop Proceedings, pages 90–94. CEUR-WS.org, 2015.
6. Thomas Vogelgesang and H.-Jürgen Appelrath. PMCube: A Data-Warehouse-Based
Approach for Multidimensional Process Mining. In M. Reichert and H. Reijers,
editors, BPM Workshops 2015, volume 256 of LNBIP, pages 167–178. Springer,
2015.
7. Thomas Vogelgesang, Stefanie Rinderle-Ma, and H.-Jürgen Appelrath. A Frame-
work for Interactive Multidimensional Process Mining. In M. Dumas and M. Fanti-
nato, editors, BPM Workshops 2016, pages 23–35. Springer, 2017.
2
https://youtu.be/5ItNtByDetQ
3
http://dx.doi.org/10.4121/uuid:5f3067df-f10b-45da-b98b-86ae4c7a310b