=Paper=
{{Paper
|id=Vol-2374/paper7
|storemode=property
|title=ProcessExplorer: Interactive Visual Exploration of Event Logs with Analysis Guidance
|pdfUrl=https://ceur-ws.org/Vol-2374/paper7.pdf
|volume=Vol-2374
|authors=Alexander Seeliger,Maximilian Ratzke,Timo Nolle,Max Mühlhäuser
}}
==ProcessExplorer: Interactive Visual Exploration of Event Logs with Analysis Guidance==
https://ceur-ws.org/Vol-2374/paper7.pdf
P ROCESS E XPLORER: Interactive Visual Exploration
of Event Logs with Analysis Guidance
Alexander Seeliger, Maximilian Ratzke, Timo Nolle, Max Mühlhäuser
Technische Universität Darmstadt
Telecooperation Lab
Darmstadt, Germany
Email: {seeliger, nolle, max}@tk.tu-darmstadt.de
Abstract—Process analysts use process mining techniques to organizations, visual exploration and analysis are getting more
obtain fact-based knowledge from event logs about how business and more challenging. Often the analyst is confronted with a
processes are actually executed in organizations. Often process spaghetti-like process map which by itself does not necessarily
discovery is the first step in their analytical workflow. However,
when working with large amount of data and complex processes, lead to useful insights. Without extensive knowledge about
exploring as-is process models to obtain interesting and insightful the underlying process, selecting the right set of cases to find
knowledge can be challenging. We propose P ROCESS E XPLORER, interesting and valuable insights or trends is non-trivial. In
an interactive visual recommendation system for process dis- current process mining tools, most of these analysis steps are
covery to facilitate event log exploration. P ROCESS E XPLORER performed manually, leading to a lot of repetitive work which
automatically analyzes the event log to obtain promising subsets
of cases, evaluates interesting process performance indicators, hampers efficient exploration and analysis.
and recommends those that are most interesting and insightful. P ROCESS E XPLORER extends the interactive visual explo-
Our system uses multi-perspective trace clustering to identify ration capabilities in today’s process mining tools by providing
candidate cases of interest and a deviation-based approach to automatic guidance to the analyst. Our tool integrates several
assess the interestingness of process performance indicators.
We implemented P ROCESS E XPLORER as a standalone desktop
recommendation suggestions in a user-friendly manner to
application that allows to explore any process and any event log. improve overall process discovery exploration:
Our demo shows how the workflow of analysts is supported by the 1) Subset Recommendation. P ROCESS E XPLORER recom-
system through suggesting subset and insights recommendations.
mends subsets of interesting cases to allow analysts
Index Terms—process discovery, variants analysis, log pre- quickly inspect the different process behaviors observed
processing, trace clustering, statistical hypothesis testing in the event log. Different from the manual filtering
that requires expert knowledge, subset recommendations
I. I NTRODUCTION are automatically derived by mining process behavior
patterns from the dataset to simplify subset selection.
Nowadays, information systems in organizations support
2) Insights Recommendation. After selecting a subset of
and automate the processing of business transactions. These
cases, P ROCESS E XPLORER automatically computes a
systems are typically integrated into companies’ business
range of relevant process performance indicators to
processes and record the activities that have been executed
show interesting deviations. Analysts are guided towards
in the form of an event log. Process mining aims at providing
interesting statistics that they usually would compute
an accurate view of how processes are actually executed in
manually.
organizations. In particular, process discovery reconstructs as-
3) Recommendation Ranking. In order to prevent the an-
is process models from event logs which can be used for
alyst from inspecting only a limited subset of cases,
further analysis. A wide range of process mining tools has
P ROCESS E XPLORER provides the analyst with the most
been established that implement process discovery and analy-
diversifying recommendations by applying diversifying
sis methods to support analysts to obtain valuable knowledge.
top-k ranking [1].
With this knowledge, process issues can be identified and
optimizations can be implemented. P ROCESS E XPLORER is agnostic to the process and event
In this paper, we introduce the P ROCESS E XPLORER system log that is being analyzed. Any process and any event log in
which provides recommendations to the analyst on how to the standardized IEEE XES format can be used. Furthermore,
select a subset of cases and what statistics may be interesting the analyst does not need to setup any configuration or specify
and insightful. Our system is inspired by the workflow that parameter values. Prior knowledge about the process or the
analysts typically perform when working with process mining event log is not required. P ROCESS E XPLORER obtains all the
tools. The visual inspection of the discovered process model necessary information from the event log itself.
is the initial starting point of any process mining project. We used P ROCESS E XPLORER in a case study on the BPI
Due to the massive growth of data, the increasing process Challenge 2019 event log collected from a large company
complexity, and the flexible execution of business processes in to investigate the procurement handling process [2]. The
rest of the paper is structured as follows. We provide a C. Ranking
walk-through of P ROCESS E XPLORER using this event log, Lastly, P ROCESS E XPLORER ranks the recommendations
showing the different types of recommendations provided by based on the interestingness score [4]. Each insights recom-
P ROCESS E XPLORER and highlight the maturity of the tool. mendation is assigned a score that is computed from how large
Then, we present the architecture of P ROCESS E XPLORER to the deviation is from the rest of the event log and the number
show extensibility. of cases that are covered. We use Cohen’s effect size [5] which
uses a comprehensive scale to determine the maturity of the
II. R ECOMMENDATION E NGINE deviation. Insights recommendations are then ranked by their
P ROCESS E XPLORER extends process mining tools by intro- assigned scores.
ducing a recommendation engine to support analysts selecting During our experiments, we found that certain insights
interesting subsets of cases and generating insightful statistics. co-occur with each other which unnecessarily increases the
In particular, our system allows to quickly scan unknown pro- number of insights recommendations. P ROCESS E XPLORER
cesses in event logs to obtain knowledge about how the process clusters similar insights recommendations using the Spear-
is actually executed and where potential issues can be found. man’s rank-order correlation.
P ROCESS E XPLORER provides two types of recommendations Subset recommendations are assigned a score based on the
and a ranking mechanism. insights scores and the number of cases that are contained
in the subset. We obtain the top-k subset recommendations
A. Subset Recommendations using the top-k diversifying ranking algorithm [1] to increase
the analysts perspective on the event log. Instead of show-
The first type of recommendation suggests subsets of cases
ing very similar subset recommendations on top of the list,
that contain interesting process behavior patterns. We are
P ROCESS E XPLORER suggests the most diversifying subsets
particularly interested in patterns that combine the control
which prevent the analyst from inspecting only a limited subset
flow and the data perspective. This is inspired by the manual
of cases. In P ROCESS E XPLORER, the top 10 most interesting
work of analysts who not only filter cases by the sequence
and diversifying subset recommendations are shown to the
of activities but also by attributes. This is often used to
user.
compare different departments, products, or company loca-
tions. To support analysts during the selection of appropriate III. T OOL
subsets of cases, P ROCESS E XPLORER automatically analyzes P ROCESS E XPLORER is a standalone interactive process
the given event log to find such patterns using trace clustering. mining tool to demonstrate the proposed guidance capabil-
Specifically, we apply multi-perspective trace clustering [3] to ities. As mentioned earlier, it allows importing any stan-
obtain subsets of cases that contain dependencies between the dardized IEEE XES event log and works without specifying
control flow and the case attributes. Resulting subsets of cases any additional parameter value. We give a walk-through of
with similar behavior lead to process maps that are typically P ROCESS E XPLORER by inspecting the procurement handling
less complex and easier to understand visually. process of the BPI Challenge 2019 event log [2]. Figure 1
shows the main screen of P ROCESS E XPLORER. The user
B. Insights Recommendations
interface consists of five different components:
Another typical task in process mining is to investigate and a) Process Map: The most prominent component in
compare a range of process performance indicators (PPIs), P ROCESS E XPLORER is the process map. It visualizes the
such as the number of activities, the total duration time, activities and transitions that have been observed in the event
the duration time between activities, the directly followed- log. Activities and transitions can be filtered by their relative
by relation, and the existence of activities. These are either occurrence using the slider at the bottom right. Figure 1 shows
directly visualized in the process map or separately displayed the process map of a selected subset recommendation.
in the form of statistical charts or single values. Existing b) Subset Recommendations: On the top right side, the
process mining tools provide assistance by offering the possi- ranked list of subset recommendations is shown. Subset rec-
bility to create dashboards with predefined PPIs which will ommendations can be modified and adjusted by the user,
update immediately if a different case selection is made. enabling to further refine the selection of cases interactively.
Still, each PPI needs to be investigated one after another to Users can add a happy path filter, a variant filter, a start and
identify deviations which is time-consuming and error-prone. end activity filter, and an activity occurrence filter. Figure 1
P ROCESS E XPLORER automatically computes these PPIs for a shows the 8 subset recommendations that are suggested for
selected subset and identifies those ones that may be interest- the currently selected subset of cases.
ing to the user by performing statistical significance testing. c) Subset Statistics: On the lower right side, basic statis-
Compared to dashboards that are static with respect to the tics of the selected subset recommendation are shown which
computed PPIs, P ROCESS E XPLORER reevaluates the PPIs for give an overview of the cases in the subset. The statistics show
each applied subset recommendation individually. Only PPIs how the subset selection compares to the original event log
that are significantly different from the rest of the cases in the and highlights the event distribution, the variant distribution,
event log are considered as an interesting insight [4]. and the number of selected cases. Based on the statistics, the
Fig. 1. User interface of P ROCESS E XPLORER showing the subset and insights recommendations, the process map of the selected subset, the stage view, and
the subset statistics. The screenshot shows a selected subset recommendation of the BPI Challenge 2019 event log.
user can decide which subset recommendation to apply. In the recommendations are computed, so recommendations can be
example, the selected subset recommendation selects 6 events, successively refined.
and 1 out of 4 variants.
d) Insights Recommendations: On the left-hand side, IV. A RCHITECTURE
P ROCESS E XPLORER shows the insights recommendations for
the current subset. Insights recommendations are automatically P ROCESS E XPLORER is built of three main components:
updated each time the subset of cases is modified. The the event log manager (XLogManager), the stage manager
system computes a range of basic PPIs which are typically (XStageManager), and the recommendation manager (Rec-
analyzed by users. We distinguish between case- and subset- ommendationManager). All three components are open for
based insights. Depending on the insight type, a different extension, such that other event log formats, stage management
visualization is shown to the user. Figure 1 shows a portion capabilities, subset and insights recommendation approaches
of the obtained insights recommendations. For instance, the can be integrated. Figure 2 shows the overall architecture of
first insight refers to the directly followed-by relation between P ROCESS E XPLORER.
the “Record Invoice Receipt” and “Remove Payment Block” Event logs are imported as an OpenXES XLog object and
activities, which occurs more often in the applied subset. stored in-memory using the XESlite extension. Each loaded
Furthermore, we can see that the activity “Receive Order log is stored in the XLogData object structure which links
Confirmation” is mostly executed by “user 029”. to the XLog object and stores the basic statistics of the
e) Stage Views: For easier navigation between the differ- log. The XStageManager is responsible for managing the
ent subset recommendations, P ROCESS E XPLORER introduces views of P ROCESS E XPLORER, storing a history of all stages
stage views. Each time the user decides to apply a subset visited by the user. For an active stage, the XStageManager
recommendation a new stage view is generated. A stage view retrieves the recommendations from the Recommendation-
stores the selected cases and the computed insight recom- Manager which returns a set of Recommendation objects.
mendations. Stages are organized as a hierarchical structure If the recommendations have not yet being computed, the
such that each refinement of a selection results in a new RecommendationManager calls the RecommendationFactory.
hierarchy level. For each stage view, subset and insights Each Recommendation refers to the subset recommendations
XLogManager
import event log
XES
Document XLogData
XLogData
XLogData
RecommendationFactory
XLogData
generated
active stage recommendations
selected stage RecommendationManager
selected log data
XStageViewer XLogData
XLogData
XStageManager Recommendation
XLogViewer
recommendation
XLogData RecommendationInfoViewer
XLogData active stage
selected
active stage
Recommendation
XStage
StageInfoViewer RecommendationInsightsViewer
XStage Insight
StageInsightsviewer
selected generated
recommendation recommendations
RecommendationSelector
accept/reject recommendation
Fig. 2. Overview of the architecture of the P ROCESS E XPLORER tool [6].
shown in P ROCESS E XPLORER which contain the Insight rec- analysts towards interesting subsets of cases as well as shows
ommendations. insightful statistics of relevant PPIs. Subset recommenda-
All visualization components, such as the XLogViewer, tions are computed using multi-perspective trace clustering
StageInfoViewer, StageInsightsViewer, RecommendationView- to obtain process behavior patterns that are interesting to
ers are separated from the actual recommendation engine. explore. Insights recommendations show interesting PPIs that
This architecture allows the exploration of different types of significantly differ for an investigated subset compared to the
visualizations, such as other types of charts, process model rest of the event log. Furthermore, P ROCESS E XPLORER gives
visualizations, etc., but keep the actual computation of the each recommendation a score based on interestingness and
recommendations. maturity. It applies top-k diversifying ranking to obtain the
In the current implementation of P ROCESS E XPLORER, we most different recommendations.
implemented a multi-perspective trace clustering recommenda-
ACKNOWLEDGMENT
tion engine for subset recommendations and a statistical sig-
nificance testing approach for obtaining insights recommenda- This work is funded by the German Federal Ministry of
tions. However, other implementations are easy to implement Education and Research (BMBF) Software Campus project
by extending the corresponding classes. “AI-PM” [01IS17050] and the research project “KI.RPA”
[01IS18022D].
V. D OWNLOAD , S CREENCAST, AND L INKS
R EFERENCES
The P ROCESS E XPLORER demo tool can be found at our
[1] L. Qin, J. X. Yu, and L. Chang, “Diversifying top-k results,” Proceedings
project page1 . On the project page, a demonstration video of the VLDB Endowment, vol. 5, no. 11, pp. 1124–1135, jul 2012.
including a screencast, a reduced event log derived from the [2] B. F. van Dongen, “Dataset BPI Challenge 2019,” 4TU.Centre for
BPI Challenge 2019, and additional screenshots are provided. Research Data, 2019.
[3] A. Seeliger, T. Nolle, and M. Mühlhäuser, “Finding Structure in the
The demo tool requires Oracle Java 8 and was tested on Unstructured: Hybrid Feature Set Clustering for Process Discovery,” in
Windows and Ubuntu. Proc. of the 16th BPM. Springer International Publishing, 2018, pp.
288–304.
VI. C ONCLUSION [4] M. Vartak, S. Rahman, S. Madden, A. Parameswaran, and N. Polyzotis,
“SeeDB,” in Proc. of the VLDB Endowment, vol. 8, no. 13, 2015, pp.
In this paper, we presented P ROCESS E XPLORER, an inter- 2182–2193.
active visual recommendation system for process discovery [5] J. Cohen, “Statistical Power Analysis,” Current Directions in Psycholog-
ical Science, vol. 1, no. 3, pp. 98–101, jun 1992.
inspired by the workflow typically performed by analysts. [6] M. Ratzke, “Intelligent and Systematic Browsing through Process Mining
Our system suggests two types of recommendations that guide Data,” 2019.
1 https://fileserver.tk.informatik.tu-darmstadt.de/AS/processexplorer