=Paper=
{{Paper
|id=None
|storemode=property
|title=Disco: Discover Your Processes
|pdfUrl=https://ceur-ws.org/Vol-940/paper8.pdf
|volume=Vol-940
|dblpUrl=https://dblp.org/rec/conf/bpm/GuntherR12
}}
==Disco: Discover Your Processes==
https://ceur-ws.org/Vol-940/paper8.pdf
Disco: Discover Your Processes
Christian W. Günther and Anne Rozinat
Fluxicon
Bomanshof 259, 5611 NS, Eindhoven, The Netherlands.
{christian,anne}@fluxicon.com
Abstract. Disco is a complete process mining toolkit from Fluxicon that makes
process mining fast, easy, and simply fun.
1 Why Disco?
As former process mining researchers, we started Fluxicon in 2009 to build professional
tools that help organizations to regain control over their processes.
Our first product Nitro addressed the pain of getting the original process data from
IT systems into a format that can be used for process mining. Today, Nitro is used all
over the world by practitioners and researchers to convert raw data into event logs that
can be analyzed with the leading academic process mining toolkit ProM.
While ProM is great and immensely powerful, we realized through our own process
mining consulting projects, and through many conversations with practitioners, that
process analysts in practice need a tool that—above all—makes process mining easy
and fast. And this is what Disco is all about.
2 Tour
The following tour gives you an overview about the main functionality of Disco.
2.1 Import
Every process mining project starts with the data that should be analyzed. Disco has
been designed to make the data import really easy by automatically detecting times-
tamps, remembering your configuration settings, and by loading data sets with high
speed.
One simply opens a CSV or Excel file and configures which columns hold the case
ID, timestamps, activity names, which other attributes should be included in the anal-
ysis, and the import can be started. Data sets are imported in a read-only mode, so the
original files cannot be modified (which is important, e.g., for auditors).
Disco is also fully compatible with the academic toolsets ProM 5 and ProM 6. By
importing and exporting the event log standard formats MXML and XES, advanced
users can seamlessly move back and forth between Disco and ProM if they want to
benefit from the new research technologies developed in academia.
Disco also features a short-cut import and data exchange for previously imported
data sets with up to 200x speed-up for very large data sets through the native FXL
Disco log file format.
2.2 Automated Process Discovery
The core functionality of process mining is the automated discovery of process maps
by interpreting the sequences of activities in the imported log file. After one presses the
Start import button the user is taken right into the Map view, where she can quickly and
objectively see how the process has been actually performed.
Disco uses an intuitively understandable and 100% truthful process map visualiza-
tion. The thickness of paths and coloring of activities show the main paths of the process
flows, and wasteful rework loops are quickly discovered.
The Disco miner is based on the Fuzzy miner, but has been further developed in
many ways. The Fuzzy Miner was the first mining algorithm to introduce the “map
metaphor” to process mining, including advanced features like seamless process sim-
plification and highlighting of frequent activities and paths. For Disco, we have used the
approach of the Fuzzy Miner and combined it with experience from our own practice
and user testing.
The result is a mining algorithm that, while providing reliable and trustworthy re-
sults for data sets of arbitrary complexity, can be operated and understood efficiently by
domain experts with no prior experience in process mining. Although the Disco miner
is based on the framework of the Fuzzy Miner, we have developed a completely new
set of process metrics and modeling strategies, effectively making the Disco miner a
next-generation Fuzzy Miner.
Our design priorities are what sets the Disco miner apart from other solutions:
1. Usability: Our goal was to have a miner that can be operated and understood by do-
main experts, with an adequate learning curve to also accommodate process mining
experts. We also have put great effort into making our visualizations information-
dense, while avoiding information overload. For Disco, we have used state-of-the-
art UX and visualization research, user testing, and lots of development time to
make sure our models are nice to read and quick to understand.
2. Fidelity: Creating a truthful model from a simple, well-structured process model is
easy. When faced with complex data, though, most commercial approaches resort
to drastically limiting the data used (only using the mainstream variants) to keep
model complexity in check. We wanted a miner that can intelligently extract the
most important parts of the process from the full set of data, and create a useful
process model from data of arbitrary complexity.
3. Performance: Almost all process mining tools want to be used in a procedural fash-
ion: You give them the data, and some parameters, they create a process model,
done. We see process mining as an explorative and highly interactive task, where
the domain expert learns to understand the data by looking at the process from
multiple perspectives in quick succession. For this approach to work, we need our
miner to work very fast.
The Disco miner is considerably faster than any other approaches we are aware of,
while delivering superior model quality. We think there is inherent value in having a
good approximation of complex behavior in a few seconds, versus a perfect model in
three hours (which is what you get with, e.g., genetic approaches). By intensively opti-
mizing the whole stack, down from the log storage layer up to the graph visualization,
we have created a miner that fosters truly interactive usage which, ultimately, leads to
better and more meaningful analysis results.
2.3 Process Statistics
Next to the process maps one can also inspect statistics about the process. For this, one
simply changes to the Statistics tab in the toolbar. The user will get overview informa-
tion about the number of cases and events in the data set, the time frame covered, and
performance charts like, for example, about the case duration.
Further statistics views provide frequency and performance information for all ac-
tivities and resources in the process. Furthermore, there are statistics for any additional
data attribute column that was included in the data set. These additional data attributes
are usually very important for the process analysis, because they hold relevant context
information such as:
– Which product a service call was about,
– Which type of category a change request in an IT Service process falls in,
– The channel through which a lead in a sales process came in,
– Domain-specific characteristics such as warranty vs. out-of-warranty repairs in a
service process,
– By which department the activity was handled,
– In which country the process was performed,
– The value of an order, which is relevant for many purchasing processes, because
depending on the amount of money that is involved different anti-fraud rules will
apply, etc.
In our projects, we often get data sets with up to 40 or 60 additional data attributes
that are relevant and can be used in the analysis. Disco shows the users these attribute
statistics, but also lets them use them to drill down and focus their analysis, and to split
out and compare processes with respect to these categories.
2.4 Variants and Individual Cases
The third data set view is the Cases tab. While the Map view gives an understanding
about the process flows, and the Statistics view provides detailed performance metrics
about the process, the Cases view actually goes down to the individual case level and
shows the raw data.
To be able to inspect individual cases is important, because one will need to verify
the findings and see concrete examples particularly for “strange” behavior that will
most likely be discovered in the process analysis. Almost always users find things that
are hard to believe until they have drilled down to an individual example case, noted
down the case number, and verified that this is indeed what happened in the operational
system.
Furthermore, looking at individual cases with their history and all their attributes can
give additional context (like a comment field) that sometimes explains why something
happened. Finally, being able to drill down to individual cases is important to be able to
act on the analysis. For example, if one has found deviations from the described process,
or violations of an important business rule, one may want to get a list of these cases and
talk to the people involved in them to provide additional training.
In addition to a complete list of all cases in the data set, the user also gets direct
access to the variants in the process. Variants are an integral part of the process analysis.
In Disco, a variant is a specific sequence of activities. It can be seen as one path from the
beginning to the very end of the process. In the process map, an overview of the process
flow between activities is shown for all cases together. A variant is then one “run”
through this process from the start to the stop symbol, where also loops are unfolded.
Usually, a large portion of cases in the data set are following just a few variants, and it
is useful to know which are the most frequent ones.
Furthermore, a live full text search across case names and all activity, resource, and
data columns lets the user find specific cases based on the words or word fragments she
is looking for.
2.5 Filtering
Disco offers powerful, non-destructive filtering capabilities for explorative drill-down,
and for focusing the analysis. These filters are quickly accessible from any view and
easy to configure.
In total, there are six powerful filter types available in Disco, and they can be com-
bined and stacked in any order:
– The Timeframe filter with intuitive calendar controls to select cases and events
based on a time window. It can be used, for example, to compare the processes
before and after a process change.
– The Variation filter that allows one to focus the analysis on either the mainstream
behavior or precisely the exceptional cases by making use of the variants from the
Cases view.
– The Performance filter to focus on cases based on a variety of different performance
metrics like, for example, the case duration.
– The Endpoints filter to select cases based on their start and end activities. For ex-
ample, one can filter incomplete cases, or trim cases to cut out a part of the process.
– The Attribute filter to focus on (or exclude) certain activities, resources or process
categories based on data attributes.
– The Follower filter for powerful process pattern-oriented filtering, including a 4-
Eyes filter option that can be used to check for segregation of duty violations.
Together with the three analysis views, these filtering capabilities enable Disco users
to quickly and interactively explore their process into multiple directions, and to answer
concrete questions about the process. Because filtering, and Disco in general, are so
fast, one can also hold interactive process workshops, where the analyst and a group
of other process stakeholders get together to do an As-Is analysis and generate process
improvement ideas along the way.
2.6 Performance Highlighting
In addition to the frequency-based process map, one can also analyze the time that is
spent in the process. The average durations of the activities and the inactive (waiting)
times between activities are automatically extracted from the timestamps in the data set
and visually projected onto the process map.
An alternative Total durations performance highlighting option shows these high-
impact areas at one glance by summing up the durations for each activity and path for
the complete data set.
2.7 Animation
Animation is a way to visualize the process flow over time right in the discovered pro-
cess map (a bit like showing a “movie” of the process). Animation should not be con-
fused with simulation. Rather than simulating, the real events from the log are replayed
in the discovered process map as they took place.
Animation can be very useful to communicate analysis results to process managers
or other people who are no process analysis experts. By showing how the cases in the
data set move through the process (at their relative, actual speed), the process is literally
“brought to life”.
2.8 Project Management
One of the advantages of Disco is that it supports project work through the management
of multiple data sets in one project view. In a typical process mining project, one will
import log files in different ways, filter them, and make copies to save intermediate
results. This results in many different versions and views of the data sets and can easily
get out of hand.
The project view in Disco is there to help the users keep an overview. It keeps all
their work in one place and lets them make notes about what they found out, or what
they still want to check. Complete projects can be exported and shared with other people
who can start right where they left off.
Disco features a sandbox project that we prepared for new users to get started
quickly after the installation of Disco.
3 Links
A 6-min screencast has been recorded for this demo. You can watch this screencast in
two parts, Part I at http://screenr.com/F1n8, and Part II at http://screenr.com/q1n8.
Furthermore, you can view the Disco product page and download a free demo ver-
sion at http://fluxicon.com/disco/. You can also read a tour including screenshots and
examples in our launch blog post here: http://fluxicon.com/blog/2012/05/say-hello-to-
disco/.
Note that we provide free academic licenses for Disco in our Academic Initiative
for Process Mining Research and Education (see http://fluxicon.com/academic/).