Authors Aladdin Shikhizada Fabrizio Maria Maggi Francesco Riva Ivan Donadello
License CC-BY-4.0
Declare4Py: A Python Library for Declarative
Process Mining
Ivan Donadello1,∗ , Ph.D., Francesco Riva1 , M.S., Fabrizio Maria Maggi1 , Ph.D. and
Aladdin Shikhizada2 , M.S.
1
Free University of Bozen-Bolzano, Bolzano, Italy
2
Visioncraft OÜ, Tallin, Estonia
Abstract
In process mining, procedural process models can be difficult to manage when the process is unpredictable
and characterized by many possible exceptions since they can easily become unreadable. Declarative
languages, instead, model the process by imposing logical constraints on the process behavior and are
suitable to represent variable processes in a compact way. Declare is the reference declarative language
in the BPM community. Although several Java tools are available for process analysis based on Declare,
a library implementing process mining tasks with Declare in Python is still missing. Therefore, in this
paper, we present Declare4Py, the first Python package that offers support for declarative process mining.
Declare4Py includes methods for conformance checking, process discovery and query checking.
Keywords
Declare, Conformance Checking, Process Discovery, Query Checking, Python API, Declarative Process
Mining
1. Introduction
Process mining [1] focuses on the analysis of business processes based on event logs that contain
information about the process executions. A key component in process mining is a process model
that is a formal representation of the process in a standard format. Procedural models require
to define the whole control-flow of the process step-by-step thus making procedural process
mining not suitable for processes with a high number of different branches and exceptions.
Declarative process models are, instead, easier to manage as they just encode a set of constraints
that the process should follow. While procedural models can be designed and analyzed using
several available commercial and academic process mining tools12345 , this variety is lacking for
The BPM 2022 Demos & Resources Forum. 20th Business Process Management Conference, Münster, Germany, September
11-16, 2022
∗
Corresponding author.
Envelope-Open ivan.donadello@unibz.it (I. Donadello); Francesco.Riva@unibz.it (F. Riva); maggi@inf.unibz.it (F. M. Maggi);
aladdin.shikhizada@gmail.com (A. Shikhizada)
Orcid 0000-0002-0701-5729 (I. Donadello); 0000-0002-9089-6896 (F. M. Maggi)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
CEUR Workshop Proceedings (CEUR-WS.org)
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
apromore.com/
2
celonis.com/
3
fluxicon.com/disco/
4
processmining.org
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pm4py.fit.fraunhofer.de/
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declarative models where only few (Java-based) tools and libraries are available6 [2, 3].
In this paper, we present Declare4Py, a novel and easy-to-use Python library that implements a
set of APIs covering the main declarative process mining tasks based on Declare [4]. In particular,
the language employed is MP-Declare [5], the multi-perspective extension of Declare that
supports data- and time-aware constraints along with control-flow constraints. Our contribution
to the BPM community is the first Python library with APIs for conformance checking, process
discovery and query checking based on Declare models. Being a Python library, Declare4Py can
be easily integrated with the main Machine Learning frameworks such as SKlearn, Tensorflow
and Pytorch, and, as a library, can be easily invoked via code to conduct large experimentations.
We also stress the fact that the query checking functionality provided by Declare4Py is novel and
not available in the existing tools for declarative process mining. We compared the Declare4Py
performance with RuM [2], a Java-based tool for declarative process mining on the core task of
conformance checking, achieving better computational times.
This first release of Declare4Py is online in a GitHub repository available at https://github.
com/francxx96/declare4py. The repository contains the code and some tutorials in Jupyter
notebooks (https://github.com/francxx96/declare4py/tree/main/tutorials) showing how to use
Declare4Py using the well-known Sepsis cases log.7 A video that overviews the package is
available at https://youtu.be/hJhgoqFLM7s.
2. Overview of the Declare4Py Features
Declare4Py has been designed to analyze event logs using declarative, constraint-based process
models. It relies on well-known standards for input and output files, such as XES [6] for event
logs and d e c l [7] for the Declare models. This ensures its interoperability with other libraries
and tools.
We briefly recall here some preliminary definitions. A trace 𝜎 is an execution of a business
process. A trace contains a sequence of events where each event is related to the execution of
an activity 𝑎 ∈ 𝐴 (with 𝐴 the set of all possible activities), performed at time 𝑡 with a (possible)
set of other attributes a.k.a. the payload of the event. A Declare model ℳ = {𝜑1 , 𝜑2 , …} is a
set of Declare constraints instantiation of parameterized templates [4]. We indicate the set of
Declare templates with 𝒜. A trace satisfies a Declare model (𝜎 ⊧ ℳ), when the trace satisfies
each constraint 𝜑 ∈ ℳ, i.e., ∀𝜑 ∈ ℳ, 𝜎 ⊧ 𝜑. A log 𝐿 is a multi-set of traces.
Conformance Checking. Given a log 𝐿 of traces 𝜎𝑖 and an MP-Declare model ℳ, the con-
formance checking task checks, for all the traces 𝜎𝑖 ∈ 𝐿, whether, for all constraints 𝜑 ∈ ℳ, 𝜎𝑖 ⊧ 𝜑
holds. Declare4Py implements the conformance checking task using the approach presented
in [5] that takes an MP-Declare model and a log as inputs and returns the number of activations,
fulfillments, and violations for each constraint in the input model and for each trace in the input
log. These results are listed in a Python data structure indexed by trace identifier. Therefore,
the user can easily query such data structure to retrieve or aggregate information.
6
rulemining.org
7
https://data.4tu.nl/articles/dataset/Sepsis_Cases_-_Event_Log/12707639
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Process Discovery. Given a log 𝐿 of traces 𝜎𝑖 and a support threshold 𝑡ℎ𝑠 , the process dis-
covery task returns a Declare8 model ℳ of constraints satisfied by a percentage of traces in 𝐿
higher than or equal to 𝑡ℎ𝑠 . More formally:
ℳ = {𝜑 ∶ |{𝜎 ∈ 𝐿 ∶ 𝜎 ⊧ 𝜑}|/|𝐿| ≥ 𝑡ℎ𝑠 }. (1)
Declare4Py implements the approach presented in [8] that consists of two steps, i.e., (1) the
discovery of frequent (pairs of) activities from 𝐿; (2) the construction of ℳ from this set. The
set of frequent (pairs of) activities from 𝐿 is built with the Apriori algorithm [9] by computing
the frequent itemsets of activities of length 1 and 2. These itemsets are used to build a set of
candidate Declare constraints 𝒞 obtained by instantiating the templates in 𝒜 with the activities
belonging to each itemset. ℳ ⊆ 𝒞 is then computed by selecting the constraints 𝜑 ∈ 𝒞 such that
|{𝜎 ∈ 𝐿 ∶ 𝜎 ⊧ 𝜑}|/|𝐿| ≥ 𝑡ℎ𝑠 . The results are returned in a Python data structure containing, for
each constraint in ℳ, the traces that satisfy it. A Declare4Py function allows the user to filter
such data structure to retrieve the most relevant (i.e., the most frequently satisfied) constraints.
The discovered model can be exported as a d e c l file.
Query Checking. This task takes as input a log 𝐿 of traces 𝜎𝑖 , a support threshold 𝑡ℎ𝑠 , and
an MP-Declare query 𝑞, i.e., an MP-Declare constraint in which the activation and/or the
target activity are unspecified. For example, constraint Response(?𝐴, ER Triage)9 contains a
placeholder for the activation activity, whereas Response(?𝐴, ?𝑇) contains placeholders for both
activation and target. Let 𝑉 𝑎𝑟𝑠 be the set of placeholders of a Declare query and 𝜆 ∶ 𝑉 𝑎𝑟𝑠 → 𝐴
be an assignment function that assigns placeholders to activities. The query checking task
returns the set of assignments Λ = {𝜆1 , 𝜆2 , …} such that the input query 𝑞 instantiated using the
assignments in Λ is satisfied by a percentage of traces in 𝐿 higher than or equal to 𝑡ℎ𝑠 . More
formally:
Λ = {𝜆𝑖 ∶ |{𝜎 ∈ 𝐿 ∶ 𝜎 ⊧ 𝑞[𝜆𝑖 ]}|/|𝐿| ≥ 𝑡ℎ𝑠 }. (2)
Declare4Py returns a data structure containing the assignments.
3. Performance
We tested the computational time performance of Declare4Py on the above tasks under different
conditions using the Sepsis cases log7 and the log provided for the annual Business Process
Intelligence Challenge (BPIC) in 202010 . The core task is conformance checking as process
discovery and query checking are built on top of it. Therefore, we compared the conformance
checking task (based on [5]) implemented both in Declare4Py and in RuM [2], increasing the
number of Declare constraints in the input model. The performance of the discovery and
the query checking tasks is, instead, computed for different support values ranging in the set
8
The process discovery functionality, differently from the conformance checking and the query checking tasks, is
data-agnostic.
9
For simplicity, we do not define data and time conditions in this example. However, fully defined data and time
conditions can be specified in the query.
10
http://icpmconference.org/2020/wp-content/uploads/sites/4/2020/03/InternationalDeclarations.xes.gz
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Conformance checking - Sepsis log Model discovery - Sepsis log Query checking - Sepsis log
4 RuM 1 variable
30 2.0 2 variables
Declare4Py
3
25 1.5
Time [s]
Time [s]
Time [s]
2 1.0
20
1 0.5
15
0.0
20 30 40 50 60 70
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Number of model constraints
Itemset support Declare constraint support
Conformance checking - BPIC 2020 log Model discovery - BPIC 2020 log Query checking - BPIC 2020 log
50 1 variable
RuM 180
Declare4Py 2 variables
80
40
160
60 30
140
Time [s]
Time [s]
Time [s]
40 120 20
100
20 10
80
0 0
20 30 40 50 60 70
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Number of model constraints
Itemset support Declare constraint support
Figure 1: Declare4Py shows better performance than RuM for conformance checking for both the
Sepsis log (above) and the BPIC 2020 log (below).
{0.2, 0.4, 0.6, 0.8}.11 For query checking, we used the Chain Response template and performed
two tests. In the first one, we fixed the activation activity leaving the target unspecified; in the
second test, we left both the activation and the target activities unspecified.
The results of our experiments are reported in Figure 1. Declare4Py presents slightly lower
computational times with respect to RuM for conformance checking on small models. However,
as the model grows in the number of constraints, the computational times diverge. This is
particularly evident in the BPIC 2020 case. The computational time for the discovery and the
query checking tasks decreases when the support increases, since a higher support implies less
candidates to check. The computational time for the query checking task is obviously higher
when two placeholders have to be assigned.
4. Maturity and Future Remarks
Declare4Py has been used for deviance mining in [11] and as a tool for a new feature encoding
for business process analysis using Machine Learning methods [12]. This first release can be
improved both in terms of performance and number of functionalities. As future work, we
plan to increase the Declare4Py performance by implementing optimization techniques, such
as multi-threading, and by using the Numba library,12 which translates, at runtime, Python
code into optimized machine code by using the industry-standard LLVM [13]. The Declare4Py
functionalities will be improved by including state-of-the-art process mining algorithms. For
11
In this case, a comparison with RuM would not be fair as this tool implements the optimization technique based
on multi-threading, presented in [10], which is currently not developed in Declare4Py.
12
https://numba.pydata.org/
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conformance checking, we plan to include the techniques presented in [14] and in [15], while,
for process discovery, we will implement the techniques introduced in [16] and [17].
Acknowledgments
The work of Francesco Riva is supported by the UNIBZ project PRISMA.
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