=Paper=
{{Paper
|id=Vol-2952/paper_296a
|storemode=property
|title=Rule Mining with RuM (Extended Abstract)
|pdfUrl=https://ceur-ws.org/Vol-2952/paper_296a.pdf
|volume=Vol-2952
|authors=Anti Alman,Claudio Di Ciccio,Fabrizio Maria Maggi
|dblpUrl=https://dblp.org/rec/conf/bpm/AlmanCM21
}}
==Rule Mining with RuM (Extended Abstract)==
https://ceur-ws.org/Vol-2952/paper_296a.pdf
Rule Mining with RuM (Extended Abstract)∗
Anti Alman1, Claudio Di Ciccio2, and Fabrizio Maria Maggi3
1
University of Tartu, Tartu, Estonia
anti.alman@ut.ee
2
Sapienza University of Rome, Rome, Italy
claudio.diciccio@uniroma1.it
3
Free University of Bozen-Bolzano, Bolzano, Italy
maggi@inf.unibz.it
Abstract. Declarative process modeling languages are especially suitable
to model loosely-structured, flexible business processes. One of the most
prominent of these languages is Declare. The Declare language can be
used for all process mining branches and a plethora of techniques have been
implemented to support process mining with Declare. The process mining
application RuM integrates multiple Declare-based process mining methods
into a single application and is developed to be the starting point for the use of
Declare both in industry and academia. RuM has been evaluated by conduct-
ing a qualitative user evaluation, the results of which have been used as input
for further development. In this paper, we give a short overview of the current
functionalities of RuM, including the main improvements made thus far.
Keywords: Process analytics · Declarative modeling · Process mining ·
Process discovery · Conformance checking
1 Introduction
One of the obstacles for the adoption of declarative process mining techniques, espe-
cially in the context of the Declare language [23], has been a lack of comprehensive,
easy-to-use process mining toolkits [22, RC7]. However, this shortcoming has been
recently addressed with the introduction of RuM [3,2], which implements various
process mining techniques based on Declare and its multi-perspective extension
MP-Declare [6]. RuM is specifically developed to be the starting point for the use
of Declare both in industry and academia.
To provide an intuition about the nature and expressiveness of Declare models,
consider the example given in Fig. 1. This model is inspired by the analysis of a
real-world event log of the process of handling patients affected by sepsis [18]. The
process begins with the emergency room (ER) registration. After that, if the condition
on the Systemic Inflammatory Response Syndrome (SIRS) criteria attribute holds
true, then intravenous (IV) antibiotics have to be administered. The administration
of IV antibiotics must follow the ER triage. The ER sepsis triage has to be executed
∗
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2 A. Alman et al.
[ SIRSCriteria2OrMore ↦ ⊤ ] Init
ER registration [ org:group is different ] [ time:timestamp in [0,3]ℎ ]
Existence
IV antibiotics ER triage ER sepsis triage Lactic acid
Fig. 1. An MP-Declare map
immediately after ER triage, but with the condition that the actors carrying out the
two activities differ (condition on org:group). Finally, The analysis of the presence
of lactic acid requires that ER sepsis triage has occurred at most 3 hours before.
In this paper, we provide a short overview of the functionalities available in
RuM to operate with process models similar to the one described above (including
additional functionalities not covered in [3,2]). RuM is open-source and publicly
available at https://rulemining.org.
2 Functional Overview
RuM integrates and improves multiple existing prototypes, while also providing novel
features that can be found in RuM only. Interoperability of all modules is ensured
by relying on existing standards, namely XES [12] for the event logs and decl [21]
for the models. The main functionalities of RuM are: process discovery, conformance
checking, log generation, model editor, and monitoring. Furthermore, RuM features an
inventory system allowing for process models and event logs to be reused easily across
the application. In the following, we describe the functionalities of RuM (including the
inventory) in more detail. We remark that the conformance results and monitoring
views, the model editor, and the inventory panel were designed and developed from
ground-up specifically for RuM.
Discovery. Process discovery is implemented using Declare Miner [15] and MIN-
ERful [10], both of which can be further augmented by discovering data conditions
(based on [14]) and time conditions (developed specifically for RuM). As depicted in
Fig. 2(a), the discovered model can be explored by using three complementary views:4
Declare view: it employs the standard graphical notation of Declare. Activities
are represented as rectangles, while constraints are represented either above the
activities (unary constraints) or as lines between the activities (both with support
percentages).
Textual view: it describes the model using natural language sentences that are easy
to understand without any prior knowledge of Declare. This representation
was developed specifically for RuM and meant for users who are less familiar
with the standard graphical notation.
Automaton view: it displays the discovered process model as a finite-state automaton
based on the a conjunction of the LTLf formulas of each discovered constraint.
4
All three views enable filtering based on activity support and constraint support.
Rule Mining with RuM (Extended Abstract) 3
(a) Process discovery (b) Conformance checking
(c) MP-Declare editor (d) Log generation
(e) Monitoring (f) Inventory
Fig. 2. Main functionalities of RuM
Conformance checking. The conformance checking functionality is based on three
techniques: Declare Analyzer [6] detects activations, violations, and fulfillments;
Declare Replayer [8] and Data-Aware Declare Replayer5 report on trace alignments.
As shown in Fig. 2(b), the results are presented in groups, each displaying the outcome
for a specific trace or a specific constraint. For every group, its name is displayed
along with general descriptive statistics. The user can freely switch among groups
and toggle an extended view to read more details. In this way, users can explore the
results at a high level of detail while also keeping the user interface relatively compact.
MP-Declare editing. RuM features the first fully MP-Declare compliant model
editor, depicted in Fig. 2(c). Models are imported and exported in the decl file for-
5
https://github.com/Clyvv/DataAwareDeclareReplayer
4 A. Alman et al.
mat [21], which allows for activity definitions, attribute definitions, activity-attribute
bindings, Declare constraints, and data and time conditions of MP-Declare. The
visualization of the entire model is updated on-the-fly and the same views (with minor
modifications) are provided as in Section 2. Furthermore, the user can add constraints
and data conditions using natural language sentences, which can be provided both
via voice and text through a simple chatbot [1].
Log generation. To generate logs, RuM resorts to the AlloyLogGenerator [21] and
MINERful Log Generator [7], the former of which can also account for the data
conditions in the input process model. As illustrated in Fig. 2(d), the user can specify
the percentage of vacuous traces (i.e., traces fulfilling the constraints because the
constraints are never activated), the number of negative traces (i.e., traces violating
at least one constraint), and the case attributes of the generated event log.
Monitoring. Figure 2(e) illustrates the monitoring panel, through which the user can
analyze the state of each constraint for every event sequentially. The states follow the
four-truth-values introduced in [17]: temporarily satisfied (satisfied given the events
thus far, but can be violated in the future: green), temporarily violated (violated,
but can be satisfied in the future: yellow), permanently satisfied (blue), permanently
violated (red). Additionally, orange indicates that some constraints are conflicting,
i.e., there is no possible sequence of future events that could satisfy all the conflicting
constraints. The user can replay the whole trace automatically, manually process the
events in the trace one by one, or jump to a specific event in the trace. This functionality
is implemented using MP-Declare with Alloy6 and MobuconLTL [16], the former of
which accounts for the definition of data conditions in the input process model.
Inventory. The inventory is a novel artifact aimed at easing the management of
the event logs and process models in use during the process analysis with RuM.
As depicted in Fig. 2(f), all the files imported from the file system are retrievable
from the inventory and intermediate results can be stored directly in the inventory
as snapshots. Next to each snapshot, the inventory offers action buttons that can
activate a related functionality. For example, event logs can be directly re-routed as
inputs for discovery, process models can be sent to log generation, and so on.
3 User Evaluation
To assess the feasibility of RuM, we conducted a qualitative user evaluation. Table 1
shows the results of the post-study survey. The post-survey included the System
Usability Scale (SUS) [5] and scales covering satisfaction, expectation confirmation,
future use intentions, and usefulness [4]. The study involved eight participants: four
BPM experts with little to no knowledge of Declare and four Declare experts.
Overall, RuM was rated high on all scales. For example, RuM scored 81.875 on the
6
https://github.com/b26140/Rule-mining-tool-with-monitor-extension
Rule Mining with RuM (Extended Abstract) 5
Table 1. Survey results represented as averages for both groups and overall. The SUS score
ranges between 0 and 100, while the other scales range between 1 and 5.
Overall Declare experts BPM experts
SUS 81.875 78.75 85
Satisfaction 4.5 4.5 4.5
Expectation 4.56 4.33 4.78
Future intentions 4.167 3.833 4.5
Usefulness 4.3125 4.25 4.375
SUS scale (69.69 is considered the average, while a score above 80 is considered to
be good or excellent [13]). However, there was a significant difference between BPM
and Declare experts. RuM was rated higher by BPM experts on all scales except
satisfaction, which was rated as 4.5 by both groups. The largest differences between
the two groups are SUS score (85 to 78.75) and future use intentions (4.5 to 3.833).
This discrepancy can point towards Declare experts potentially being less sensitive
to the improvements in the ease of use of Declare constraints. For a more detailed
discussion on the study, we refer the interested reader to [3].
4 Conclusion
In this paper, we presented an overview of the functionalities currently available in
RuM alongside the results of the post-study survey from a qualitative user evaluation.
The results of the post-study survey show that RuM is well-usable for both novice and
expert users. Furthermore, we believe that RuM provides all the essential and many
additional functionalities necessary for process mining when using the Declare
language and its extension MP-Declare.
For future work, we plan to explore the feasibility of developing a visual editor
for MP-Declare models, so that elements can be added, deleted and modified in
the graphical view directly. Another avenue for future work is to extend the current
capabilities to support discovery of branched-Declare constraints [9]. Furthermore,
we will investigate the integration of functionalities provided by other declarative
frameworks for process analysis such as DCR Graphs [19], DPIL [20] and WoMan [11].
We will also continue working based on the feedback received during the user evalua-
tion. While many improvements have already been made, we have not yet exhausted
all of the ideas provided by the participants of the evaluation.
Acknowledgements. The work of A. Alman was supported by the Estonian Re-
search Council (project PRG1226) and ERDF via the IT Academy Program. The
work of C. Di Ciccio was partly supported by the Italian MUR under grant “Dipar-
timenti di eccellenza 2018-2022” of the Department of Computer Science at Sapienza,
and by the “SPECTRA” Sapienza research project.
6 A. Alman et al.
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