=Paper=
{{Paper
|id=Vol-2428/paper15
|storemode=property
|title=Enabling process mining in aircraft manufactures: extracting event logs and discovering processes from complex data
|pdfUrl=https://ceur-ws.org/Vol-2428/paper15.pdf
|volume=Vol-2428
|authors=Alvaro Valencia-Parra,Belen Ramos-Gutierrez,Angel Jesus Varela-Vaca,Maria Teresa Gomez-Lopez,Antonio Garcia Bernal
|dblpUrl=https://dblp.org/rec/conf/bpm/Valencia-ParraR19
}}
==Enabling process mining in aircraft manufactures: extracting event logs and discovering processes from complex data==
https://ceur-ws.org/Vol-2428/paper15.pdf
Enabling Process Mining in Aircraft
Manufactures: Extracting Event Logs and
Discovering Processes from Complex Data
Álvaro Valencia-Parra1 , Belén Ramos-Gutiérrez1 , Ángel Jesús Varela-Vaca1 ,
María Teresa Gómez-López1 , and Antonio García Bernal2
1
IDEA Research Group, Dpto. Lenguajes y Sistemas Informáticos,
Universidad de Sevilla, Spain http://www.idea.us.es/
{avalencia,brgutierrez,ajvarela,maytegomez}@us.es
2
Airbus Defence & Space, http://www.airbus.com/
antonio.garcia.bernal@airbus.com
Abstract. Process mining is employed by organizations to completely
understand and improve their processes and to detect possible deviations
from expected behavior. Process discovery uses event logs as input data,
which describe the times of the actions that occur the traces. Currently,
Internet-of-Things environments generate massive distributed and not
always structured data, which brings about new complex scenarios since
data must first be transformed in order to be handled by process min-
ing tools. This paper shows the success case of application of a solution
that permits the transformation of complex semi-structured data of an
assembly-aircraft process in order to create event logs that can be man-
aged by the process mining paradigm. A Domain-Specific Language and
a prototype have been implemented to facilitate the extraction of data
into the unified traces of an event log. The implementation performed
has been applied within a project in the aeronautic industry, and promis-
ing results have been obtained of the log extraction for the discovery of
processes and the resulting improvement of the assembly-aircraft process.
Keywords: Process Mining · Event log · IoT · Complex data structure
· Domain-Specific Language
1 Introduction
Process mining facilitates the understanding of business processes of organiza-
tions. This technique usually involves [14] the analysis of process event logs and
the discovery of the process models behind them. There exist several approaches
to discover them [4–6].
One of the challenges in process mining is related to the ability to extract
suitable events [3]. Moreover, the way to obtain event logs produced by the
systems is becoming more and more complex. The systems tend to produce
massive, distributed, heterogeneous, and complex data in new Internet-of-Things
(IoT) environments, and derive chaotic combinations of elements [13,18] without
Copyright © 2019 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2 A. Valencia et al.
Aircraft
Raw Log Raw Log
WorkStation E
Raw Log
WorkStation A Event Log
Raw Log
WorkStation D
WorkStation B Process Mining
Raw Log Techniques
WorkStation C
Fig. 1. Example of IoT Scenario.
structured schema. It brings about an extra level of complexity to be managed
by the current process mining solutions that involve the use of event logs because
they are mainly focused on structured homogeneous data.
The main issue tackled in this paper concerns how complex data structures,
typically generated in IoT environments, can be transformed to create an event
log that could be managed by the existing tools in process mining. Figure 1 shows
a scenario of log extraction in the aeronautic industry, based on the data pro-
duced by workstations in an aircraft-assembly process. In this scenario, several
workstations perform tests and produce logs of their execution. The information,
which follows a complex data structure, is stored in a NoSQL database (i.e., Mon-
goDB). The challenges are thus aggregating such complex data and extracting
event logs in XES format from them. Then, process discovery techniques can be
applied.
Hence, the objective of this paper is to develop an approach that enables
the extraction of event logs in XES format from complex data. In addition, this
solution is integrated in a tool so that non-expert users can benefit from this
solution.
2 Situation faced
Challenging Scenario
Nowadays, IoT environments are becoming highly relevant for organizations [12]
due to the necessity of monitoring their own operations. The challenge is how to
manage and analyze the recovered information in order to be useful. The project
Clean Sky 2: A-24 One step beyond on automated testing technologies, developed
in collaboration with Airbus Space & Defence, inspired the desire to improve data
extraction in IoT scenarios for the creation of event logs. The example is based
on the assembly and testing process of aircraft as previously shown in Figure 1.
Enabling Process Mining in Internet-of-Things Environments 3
Raw Logs Event Log (XES)
Extraction
recipe
Event Log (XES)
Fig. 2. Scenario of data extraction.
This describes a complex process that is executed at several locations without
guidance from a Business Process Management System (BPMS) [8].
In this context, the main process in the organization is focused on the assem-
bly of the aircraft in accordance with the design of an assembly chain formed
of various workstations. Each workstation in the factory executes its own sepa-
rate tests depending on the equipment that it has available, as described in the
following:
1. An aircraft with an identifier (accode) is located at a workstation.
2. In each workstation, a set of test instructions must be carried out (gticode).
3. Each set of test instructions can be executed several times until it is validated
by a designated operator (executor), and it begins and finishes at a specific
moment (start_date, final_date) and successdate.
4. Each execution might either finish successfully or generate incidents. Those
include an incidentcode, incidenttype, start_date, resolution_date,
and label.
The workstations are physically distributed and generate complex data struc-
tures depending on the tests executed thereon. In order to generate logs that
can be consumed by process-mining techniques, an extraction from the structure
presented in Figure 2 must be defined. The left side of the figure represents a set
of datasets formed of complex structures (lists and nested structures). However,
the event logs must fit the schema presented in the right-hand side of the figure.
By using this data, several trace logs can be generated, which will depend
on the type of process that must be discovered, for example, the evolution of
the aircraft per workstation, the evolution of tests, or the life-cycle of incidents.
These different perspectives (points of view) of the data can be used as the input
of the process-discovery techniques to ascertain the actual process and to enable
the analysis and improvement of the processes of assembly and testing.
4 A. Valencia et al.
Problems to be faced
The main problem to be faced in this scenario is the lack of proposals to extract
event logs from complex semi-structured data. The majority of the approaches
that focus on the extraction of event logs have been developed using relational
databases as a starting point. The main idea in these cases is that events leave
footprints by changing the underlying databases that are registered in the redo
logs of the database system [2, 15–17]. This idea has been supported by tools,
such as XESame and PromImport plugin3 [10, 20], that transform relational
database information into XES event logs. This data extraction from relational
databases has also been analysed from the perspective of ontology-based data
access [6,7] by using metamodels as an intermediary [9], and has been supported
by the onProm tool4 .
Nonetheless, as the scenario of IoT confirms, relational databases are not
considered as the unique source of events. In [11], an approach is presented that
shows how data is extracted from bug report histories, which are XML, JSON,
or log files, although nested and complex structures are excluded. This shows
that the need to extract event logs from unstructured sources is a real issue,
but there is an absence of frameworks that facilitate it. In the same study, a
quasi-manual mapping of the attributes of the report and those of the process is
carried out. Then, they are stored in a relational database from which the log of
events will be extracted. In other contexts, such as CRM (Customer Relationship
Management), the possibility of transforming unstructured data into a log of
events in XES log format [1] has also been studied (e.g., [5]). In that case, the
proposal involves a framework to discover business process models from semi-
structured data which applies various pre-processing steps to CRM activities and
then uses Latent Dirichlet Allocation (LDA) to classify and label all activities
automatically, by constructing a log of XES events.
To the best of our knowledge, our proposal constitutes the first approach for
the construction of XES event logs from complex semi-structured data, abstract-
ing non-expert users from the definition of complex transformations required to
obtain a XES event logs.
3 Action taken
First of all, the characteristics of the dataset (i.e., raw logs) for the real scenario
are depicted. In order to tackle the problems of event log extraction, we have
developed a Domain-Specific Language (DSL) [19] to enable XES event log ex-
traction from complex semi-structured data. It fulfills the main objective, since
it abstract users from the necessity of transforming complex data with nested
and recursive structures when generating event logs in XES format. In short,
the proposal is based on the specification of the paths to the attributes to be
employed as case_id, activity_id, timestamp and other optional parameters of
3
PromImport plugin: http://www.promtools.org/promimport/
4
onProm tool: https://onprom.inf.unibz.it/
Enabling Process Mining in Internet-of-Things Environments 5
XES event logs. The underlying framework then infers the transformations to
be performed in order to reach the desired schema, in this case, a XES event log
with the attributes indicated by the user.
Understanding the data
In order to measure the complexity of the scenario, it is necessary to ascertain the
characteristics of the datasets (i.e., raw logs) used as the input of our approach.
As shown in Table 1, the dataset contains 9367 sets of tests, provided by 52
workstations. In these workstations, 15 aircraft have been tested, with a total
number of 1, 110 tests (i.e., GTI). The tests can be executed more than once for
each aircraft. This is the reason why there are 9397 different executions of these
tests. These repetitions occur when a test execution is unsuccessful, thereby
causing, an incident, hence there are 6, 049 incidents in the raw log. In order
to complement this information, some statistical data has been extracted (for
instance, the average of GTI per aircraft) that provide a greater level of detail.
We would like to point out that the dataset presented is a subset of the real
data obtained in the project Clean Sky 2. Our dataset is just a small part of the
large amount of information generated, is made up of 9397 raw logs, filtered and
modified.
Taking into account the IoT scenario, each workstation periodically provides
a raw log on the execution of a test on a particular aircraft, which may also
contain incidents.
Table 1. Characteristics of the raw log 5 .
Description Values
Number of Aircraft 15
Number of Workstations 52
Number of GTIs 1,110
Number of Executions 9,397
Number of Incidents 3,049
Illustration of event log extraction
Example 1.1 illustrates a use of the DSL. It represents the process of the assembly
aircraft for each of the workstations where the tests are evaluated. The aircraft
production follows a process where each part thereof is assembled and tested in
accordance with the design of the engineers. Each step of the assembly process
is performed in a workstation, where the resulting event log is formed of a set
of traces with the following information:
5
Due to confidentiality reasons, the data shown regarding incidents, time, dates have
been manipulated and anonymised to avoid showing real data and the values only
represent a subset of the real data.
6 A. Valencia et al.
– case_id : accode (i.e., the aircraft identifier).
– activity_id : workstation. Several tests can be executed in the same work-
station, and therefore if more than one consecutive register of the testing of
an aircraft is found in the same workstation, then only the first registration
is considered in the creation of the event log.
– timestamp: start_date (i.e., the date when the aircraft passed through the
workstation).
Example 1.1: Piece of extraction code
1 extract(
2 define trace id("accode"),
3 define trace event(
4 activity = "workstation",
5 criteria =
6 orderBy(t"start_date" -> toDate("MM/dd/yyyy HH:mm:ss")),
7 timestamp = t"start_date" -> toDate("MM/dd/yyyy HH:mm:ss")
8 )
9 ) from ("mongodb://mongo-instance:27017/db/logs")
The event log generated by the piece of code in Example 1.1 is shown in
Example 1.2.
Example 1.2: Example of generated event log
1
4
5
6
7
8
10
11 [...]
12
13 [...]
14
4 Results achieved
This section describes how the obtained results help improve the process in
the assembly-aircraft process. A tool implemented to validate the results is also
present.
Extraction of event logs
First of all, a set of test cases are outlined bellow. The results are discussed in
next subsections.
– Test Case A. Process of the aircraft according to the workstation
that executes the test. This test case has been described in Section 3 (cf.
Example 1.1).
Enabling Process Mining in Internet-of-Things Environments 7
– Test Case B. Process of the aircraft according to the GTI execu-
tion. During the aircraft assembly process, a set of test instructions must
be carried out. In this test case, the processes of the aircraft according to
the tests applied to them (gticode) are extracted. Hence, the resulting event
log has the form:
• case_id : accode (i.e., the aircraft identifier).
• activity_id : gticode. If an aircraft passes the same test more than once,
then it is only considered the first time that it passed.
• timestamp: start_date (i.e., the first time the test was applied to the
aircraft).
The piece of code which enables this test case to be carried out is shown in
Example 1.3.
Example 1.3: Test Case B
1 extract(
2 define trace id("accode"),
3 define trace event(
4 activity = "gticode",
5 criteria =
6 orderBy(t"start_date" -> toDate("MM/dd/yyyy HH:mm:ss")),
7 timestamp = t"start_date" -> toDate("MM/dd/yyyy HH:mm:ss")
8 )
9 ) from ("mongodb://mongo-instance:27017/db/logs")
– Test Case C. Process of the incidents according to the type of
incident. As explained in Section 2, the tests (gticode) could report a set
of incidents with an (incidenttype). The resulting event log must be in the
following form:
• case_id : gticode (i.e., the test identifier).
• activity_id : incidents.incidenttype. If a type of incident is produced
more than once in a test, then it is only considered the first time it was
produced.
• timestamp: start_date (i.e., the first time this type of incident oc-
curred).
The piece of code which corresponds to this extraction is shown in Example
1.4.
Example 1.4: Test Case C
1 extract(
2 define trace id("gticode"),
3 define trace event(
4 activity = "incident.incidenttype",
5 criteria =
6 orderBy(t"start_date" -> toDate("MM/dd/yyyy HH:mm:ss")),
7 timestamp = t"start_date" -> toDate("MM/dd/yyyy HH:mm:ss")
8 )
9 ) from ("mongodb://mongo-instance:27017/db/logs")
8 A. Valencia et al.
Analysis of the extracted event logs
Once the event logs (i.e., XES files) are obtained, it is possible to perform certain
studies on the data that they contain and discover the processes. Next, the logs
retrieved after each extraction are explained in a more precise manner. Table 2
describes the features attained for each example of data extraction. A process-
discovery tool, Disco6 , has been employed to analyse these logs.
Table 2. Extracted logs features 7 .
Description Test Case A Test Case B Test Case C
CaseId 15 15 673
Events 369 5771 1777
Activities 52 1110 10
Median case duration 75.8 days 50.2 days 0 miliseconds
Mean case duration 76.1 days 67.1 days 20.8 days
Activities minimal frequency 1 1 16
Activities median frequency 6 3 108
Activities mean frequency 7.1 5.2 177.7
Activities maximal frequency 15 15 424
Activities frequency standard deviation 3.45 4.58 144.3
The resulting processes discovered are illustrated in Figure 3. These processes
help in the understanding and identification of certain information that could not
be analysed manually. For instance, the process for Test Case A helps towards
the understanding of the flow of the aircraft through the different workstations
in the factory. We can observe how certain workstations can perform tests in
parallel with other workstations. Test Case C can help in the understanding of
the flow of the incidents, by showing how the incidents evolve.
Effects of resulted action taken
The event logs and consequently discovered processes helped to improve the in-
dustry in the understanding and knowledge about the aircraft-assembly process:
Test Case A. In some cases an aircraft was in two workstations simultane-
ously, because for some tests it is easier to move the station than to move the
aircraft. Regarding workstations, there are workstations with assumable tran-
sition values between them (hours or days) but also workstations in which the
time in which the aircraft is at, is always 0 seconds, thus, we can assume that
they are intermediate states in which nothing is actually done. On the other
hand, there are workstations in which the aircraft has only been once and in
almost all but one workstation the aircraft has been 0ms, in one of them (55
6
Disco by Fluxicon: https://fluxicon.com/disco/
7
Due to confidentiality reasons, the data shown regarding incidents, time, dates have
been manipulated and anonymised to avoid showing real data and the values only
represent a subset of the real data.
Enabling Process Mining in Internet-of-Things Environments 9
Test Case A
WorkStation
WorkStation
Test Case C
WorkStation
WorkStation
WorkStation
Test Case B
Fig. 3. Result of the process discovery for the extraction of test case A, B and C.
days). Knowing the average time invested in each workstation and the worksta-
tions through which the aircraft must pass for aircraft of the same type, it is
possible for experts to know if deviations are occurring and the current state
against what would be desired.
Test Case B. This case helped experts to know the sequence of tests that
must be carried out and the time that must be spent in each one. Finally, it
allows experts to make decisions on the improvements that could be applied to
the tests performed on the workstations.
Test Case C. The discovery of the incident process by type makes it easier
for experts to know which types of incidents are most likely to occur after another
incident occurred. The discovery of the incident process by type helps know the
average time that is likely to be invested in the resolution of the same incident
and hence know how this will affect the entire assembly and testing process. The
most frequent type of test is in all cases the first incident and also the last one.
Other particular aspects are detected such as after the incident of shift change of
operators, if an incident occurs, which occurs in more than 30 percent of cases,
it is always of two possible types: Aircraft Configuration Failure or Test Edition
Failure.
Tool and Technical Details
The implemented tool is based on three main components: (1) the DSL lan-
guage, which enables the specification of the recipes of extraction from NoSQL
databases, such as MongoDB, and the generation of event logs in XES format, (2)
10 A. Valencia et al.
a prototype of application (cf., Figure 4) in which transformation can be defined
in a user-friendly way, and; (3) a connector which allows the generated XES logs
to automatically feed the ProMTM8 tool for the discovery of process models. All
the resources that have been employed in this work are freely available at [19].
The raw log used in the paper has been stored in a MongoDB database and the
extraction recipes have been implemented in the DSL language, based on Scala,
and developed using a model-driven engineering approach, which enables inte-
gration with other platforms. The current implementation of the connector uses
only Inductive Miner [18]. However, in order to illustrate the discovery process,
other tools, such as Disco from Fluxicon, have also been employed.
Event Log Extractor
1. Import Data Source
Select Data Source...
2. Configure XES Event Log
Drag the dataset attributes and drop them to the XES attributes
workstation str Trace ID accode str Transform
accode str Activity workstation str Transform
gticode str Timestamp start_date date Transform
Criteria start_date date Transform
testcode str
start_date str
Transform XES field: Criteria
executor str
Select transformation
final_date str String to Date
successdate str
Insert Date format
incidents array
"" "MM/dd/yyyy HH:mm:ss"
incidentcode str Criteria
Order by (asc)
incidenttype str
start_date str
resolution_date str
label str
3. Export XES File
Select destination...
Fig. 4. Prototype of UI for transformation.
5 Lessons learned
This paper presented an industrial approach to cover the extraction of data from
complex semi-unstructured databases for the generation of an event log that can
be used by process-mining tools. The solution includes a DSL and a prototype
that enable non-expert users to describe how event logs can be extracted from
NoSQL sources. The solution has been applied to a real case study based on
the aircraft-assembly process, thereby showing the applicability of our proposal
to a real scenario after the discovery processes using the extracted data. The
8
ProM: http://www.processmining.org/prom/start
Enabling Process Mining in Internet-of-Things Environments 11
proposal has been also supported by the implementation of an instrument which
is connected to process-mining tools.
The following important lessons have been learned in the development of the
approach: i) extraction of event logs in IoT scenarios. The complexity of
the data in IoT scenarios increases the complexity in the extraction of event logs
that require a intensive analysis; ii) massive data production and process-
ing. An environment where data is continuously being generated could require
the integration with Big Data architectures; iii) the selection of case anal-
ysis useful for the organization. The selection of the case analysis and the
activity filtering criteria can influence in the results of the selected case, there-
fore, it is not always an easy task since it involves a great pre-analysis of the
data and a huge understanding of the organization, and ; iv) risk factors of
discovery spaghetti-like processes. Depending on the data and the case of
study the discovery process can retrieve a spaghetti-like process which increase
the complexity of analysis of the discovery process.
Acknowledgement
This work has been partially funded by the Ministry of Science and Technology of
Spain through ECLIPSE (RTI2018-094283-B-C33), the Junta de Andalucia via the
PIRAMIDE and METAMORFOSIS projects, and the European Regional Development
Fund (ERDF/FEDER). The authors would like to thank the Cátedra de Telefónica
“Inteligencia en la Red“ of the Universidad de Sevilla for its support.
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