Automated Process Model Discovery
Limitations and Challenges
Norbert Gronau and Christian Glaschke
Chair of Business Information Systems and Electronic Government, University of Potsdam, August Bebel Straße 89,
Potsdam, Germany
Keywords: Process Mining, Process Model Discovery, Screen Capturing
Abstract: The implementation of business processes through the use of information systems (ERP, CRM, PLM and
MES) has become a key success factor for companies. For further development and optimization of
processes, many companies haven´t trusted processes for the analysis. Surveying as-is processes is complex
and only possible by manual recording. To perform this task automatically the theory shows us different
approaches (process mining, Application Usage Mining and Web Usage Mining). The target of the concepts
and tools is to complement the process of continuous improvement in the company with meaningful process
models, which can be reconstructed from protocols and user actions in the information systems. This article
focuses on the limitations of these concepts and the challenges they present and gives an outlook on how
future solutions must work to speed up the process of continuous improvement and to meet the challenges
of heterogeneity in IS - architectures.
1 INTRODUCTION
For more than 20 years business process
management has been the leading paradigm for
organizing and restructuring corporations and public
entities. Although all kinds of companies use
business process management in certain areas, there
are some challenges that require further analysis. To
name only a few:
 Improving learning while performing a
business process
 Making better use of person-bound knowledge
that is generated in or used during the business
process
 The establishment of PDCA cycles (plan-do-
check-act) in process management, that enable
the detection and subsequent correction of
deviations without interrupting the business
process
 Typically, business processes today are
supported by enterprise systems like ERP,
CRM or SCM. Normally, there are deviations
between the intended process covered, the
ERP and the actual process that is run in the
company (cf. Gronau, 2015)
 The human interface is more important than
ever in most business processes, despite
automation. When the automated business
process is interrupted, it is a human that has to
decide how to propel the process further. The
description of human interfaces is by no-way
interoperable now.
 A better real world awareness of the objects of
business processes (persons, information,
cases, instances and customer materials)
currently available, as well as approaches to
integrate such information into the process, is
more necessary than ever.
With respect to all these new challenges, detailed
and purpose-specific modeling is the precondition
for a purposeful analysis of the business process
necessary for its improvement. The detection of
business processes and the investigation into
necessary attributes of all objects tends to be very
time-consuming and is still incapable of being fully
automated.
There are some approaches like process mining
(cf. Van der Aalst, 2011) that can help identify
process patterns or recurrent instances, but the mere
act of modeling itself is one of the most challenging
tasks. This process also heavily influences the
quality of the results. Incorrect or missing attributes
19
Gronau N. and Glaschke C.
Automated Process Model Discovery - Limitations and Challenges.
DOI: 10.5220/0005885100190025
In Proceedings of the Fifth International Symposium on Business Modeling and Software Design (BMSD 2015), pages 19-25
ISBN: 978-989-758-111-3
Copyright
c
 2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

or objects mean that the purpose of analysis and the
goal of the improvement cannot be reached.
Therefore, this paper describes ongoing research
activities to determine an approach to automatically
identify business processes and model them from the
information that can be derived from information
systems like ERP and CRM.
2 PROCESS MODEL DISCOVERY
IN TIME FROM INTERNET OF
THINGS
In this section, the topic of process model discovery
in the area of a total digital integration is discussed.
Current trends, such as the internet of things,
industrial internet and digitalization (cf. Kagermann,
2014) are some of the main drivers for the
development of new concepts and technologies in
the area of business process modeling. At the center
of these approaches lies the question of how it is
possible to attain more efficient and transparent
business processes. Hence, there is great demand for
a current and trustworthy as-is process model (Houy
et. al., 2011). Such a model is necessary to decide
how to optimize or reengineer the process. Inquiries
to determine as-is processes are very complex and
labor intensive, and are typically carried out by
manual forms of observation and data collection.
The main question of the present paper is: How
can as-is processes in a corporation or a public entity
be determined automatically for further analysis?
This question has tackled in the past by using a
variety of different viewpoints. For instance, there
are techniques that use either certain properties of
technologies (Web Usage Mining, cf. Zhong, 2013)
or log files from enterprise systems to reconstruct
as-is processes. This approach is called Application
Usage and Process Mining. These tools and methods
have to be integrated into what are today more
heterogeneous application landscapes with variable
technologies and application systems (cf. Huber,
2015). This trend will be even more intense in the
future when more system elements from the Internet
of Things are incorporated within the application
landscape. Given that this development will occur in
the near future, the research question of this
contribution can be stated as follows: What kind of
information about the environment and the
enterprise are necessary in order to be able to
discover processes automatically? To answer this
question, current research approaches are described
and their limitations analyzed. Additionally,
solutions that allow for the use and analysis of the
available data are outlined, and the existing
challenges facing the development of a new method
are illustrated. At the end, an outlook for further
research is given.
3 EXISTING PROCESS
DISCOVERY APPROACHES
AND THEIR LIMITATIONS
A well-known approach for process discovery is the
concept of process mining that was developed by
Van der Aalst and his research group at Technical
University of Eindhoven (The Netherlands). This
approach uses log files from application systems (for
instance ERP systems) to reconstruct processes. To
be successful in that effort, the application system
has to deliver the needed information in a specific
manner (as shown in table 1).
Table 1: Example for a logfile.
PID Activity Worker Timestamp
452 registration 55
2011-12-24,
11:10:21
452 investigation 56
2011-12-24,
11:15:21
452 consulting 33
2011-12-24,
12:17:10
452 dismissal 55
2011-12-24,
12:47:11
453 registration 55
2011-12-24,
11:16:35
453 investigation 56
2011-12-24,
11:27:12
453 consulting 12
2011-12-24,
11:52:37
453 dismissal 55
2011-12-24,
11:59:54
454 registration 55
2011-12-24,
11:11:21
454 investigation 55
2011-12-24,
11:15:21
454 registration 56
2011-12-24,
12:17:00
An important component of this listing of
process instances is the process identification
number (PID). This number is used to create a
process diagram based on more than one process
instance (cf. Van der Aalst, 2012). In the
background, petri networks are used to be able to
Fifth International Symposium on Business Modeling and Software Design
20

generate process diagrams, to describe the different
conditions of the process and to create a graph for
visualization and analysis (cf. Van der Aalst, 2011 &
Accorsi et.al. 2012).
Another approach that uses more than one input
source is Application Usage Mining. In this
approach, the log files are complemented by
additional data from the data-base of the information
system: for instance on the users, workflows and
functions of the system. This information is used to
further enrich the models. In the background of this
approach petri networks are also used to describe the
logic of the process and to summarize the possible
states of a system (cf. Kassem, 2005).
Both approaches rely on the assumption that a
system that is able to generate data about business
processes and to deliver log files in the necessary
quality exists. This assumption is not valid for
enterprises with a huge number of information
systems, for instance best of breed solutions,
federated ERP systems or combinations of different
system classes (PLM and ERP and DMS and MES)
that are used together in the business process.
Furthermore, customizing the application systems is
very time consuming and a very high level of
expertise is required. One feature that complicates
this approach is that the functions executed by the
application system are used as a first hint for
classification and instance creation. Hence, the
relevant process tasks and functions have to be
known before the customizing of log files can take
place.
Another possibility to record the interactions
between user and system are log files of web-based
systems. This approach is called Web Usage Mining.
Here the access point for the process recording is the
technology used (PHP, HTML, Javascript, ...). As in
the process mining approach, the server can record
user requests and results into logs. The session ID is
used as process ID (cf. Bhart, 2014). This principle
is typically used for analyzing the user‘s behavior
and not for reconstructing business processes, but an
adaptation for web-based application systems seems
to be at least possible.
Starting from the preconditions that for a
successful execution of business processes more
than one application system and different
technologies are used and that some of the tasks are
performed outside of the information systems, a new
approach is necessary to be able to automatically
discover and record business processes.
To sum up, it can be said that existing
approaches for the data collection either have
specific knowledge about the process or concentrate
on only one application system or one single
technology. As information about real objects,
locations and movements are not taken into account,
these methods can only achieve a limited degree of
accuracy. Hence, we formulate another research
question: How can the user and his surrounding
likewise be taken into account for process model
discovery?
4 IDENTIFIED POSSIBILITIES
FOR DATA CAPTURING
This section shows possibilities to collect data from
performed processes without being dependent on
specific application systems or technologies.
Another aim of this section is to describe which
information user and environment can deliver and
how this information can be captured.
4.1 Screen Capturing and Optical
Character Recognition
A main point of criticism about the approaches
presented in section 3 is that a lot of work is
necessary to configure the log data, and that meta
information for the enrichment of log files in a
structured manner has to be available. To obtain this
information without any knowledge about the
business process, an approach is needed which
works independently from application systems.
Application systems are ideally closed systems with
complex interactions with clients that use different
data formats.
There is only one object that shows all the
interactions between user and system, the screen. To
be able to use this valuable source of information,
screen captures can be analyzed by an OCR software
(Shekappa et. al., 2015). From this information a
matrix can then be derived (table 2). The lines are
the clients where the software captures the screen
content. The columns are the different points in time
when the screen capturing took place. Every cell is
the result of an OCR recognition f(t) at a certain
moment (for instance t1). Available screen capturing
software is also able to capture the cursor positions
and mouse clicks of the user (cf. Huang et. al, 2011
& Johnson et. al., 2012). Therefore, it is also
possible to analyze table cells in an application
system selected by the user. This approach delivers
data about the interactions of the user with a
terminal that is sorted by date but unstructured. It is
also possible to find out the cursor position,
Automated Process Model Discovery: Limitations and Challenges
21

Table 2: Results table for OCR recording.
t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12 t13
Client 1
f(t1) f(t2) f(t3) f(t4) f(t5) f(t6) f(t7) f(t8) f(t9) f(t10) f(t11) f(t12) f(t13)
Client 2
f(t1) f(t2) f(t3) f(t4) f(t5) f(t6) f(t7) f(t8) f(t9) f(t10) f(t11) f(t12) f(t13)
Client 3
f(t1) f(t2) f(t3) f(t4) f(t5) f(t6) f(t7) f(t8) f(t9) f(t10) f(t11) f(t12) f(t13)
Client 4
f(t1) f(t2) f(t3) f(t4) f(t5) f(t6) f(t7) f(t8) f(t9) f(t10) f(t11) f(t12) f(t13)
Client 5
f(t1) f(t2) f(t3) f(t4) f(t5) f(t6) f(t7) f(t8) f(t9) f(t10) f(t11) f(t12) f(t13)
and to analyze which fields were selected by the user
and which functions were performed.
4.2 Operating System Information
Another point of criticism concerning the
approaches presented in section 2 is the exclusive
focus on application systems. Other software
running on the computer also delivers data that can
be used for the reconstruction of processes and the
generation of meta information.
Aside from the user entries, the operating system
can also deliver other valuable information. This
might include log-in credentials, the program used
and the data entered. Additionally, the operating
system is responsible for file operations and network
access (cf. Tanenbaum, 2003). Using this
information it is possible to determine which file
was opened or processed.
4.3 Process ID in Application Systems
The process ID makes a substantial contribution to
the discovery of a process. It makes it possible to
distinguish between different process instances.
From that structure, in turn, a process model can be
reconstructed. In application systems, a huge set of
distinct numbers for different kinds of data and
levels of detail exist. Execution data exist for the
accomplishment of business processes. This data, for
instance, reveals the change of storage data when a
delivery document changes the stored amount of an
item by a booking process. This delivery document
has a unique number and also points to its logical
predecessors. The offer number can be found in the
order; the order number is mentioned on a factory
order or on a delivery document. The document flow
that can be created from these numbers can be
reconstructed or read from the leading application
systems supporting this process. Additionally, these
numbers are also available when corresponding with
a client, for instance ticket numbers or invoice
numbers. Master data identifies a business object
(product resource, storage facility) uniquely by a
number. In some cases, not only are the products
identified, but also the object instances. Therefore, a
serial number or batch number is used. These
numbers are typically printed onto the product and
are used for traceability of every single item or batch
job in logistics and manufacturing.
4.4 Tracking of Business Objects
Another approach to collect information about the
environment and the user utilizes technologies to
spatially locate users and objects. Different
approaches, such as RFID or GPS, exist for this
purpose. A movement profile can be derived from
this data. These movements can be used to follow
processes when an object is moved and to define
interactions between users and business objects (cf.
Sultanow, 2015 & Gronau, 2014).
4.5 Summary
Four approaches can be used to collect data about
business processes in corporations. The next task is
to use that information to reconstruct the underlying
business processes.
5 CHALLENGES DURING THE
DEVELOPMENT OF A
METHOD
Combining the information from the different
approaches in section 4 leads to different challenges
for further research. In this section, the problems are
elaborated with the help of an exemplary scenario.
This scenario is described at first to depict the
challenges. A special emphasis is put on the aspects
of the existing approaches Process Mining,
Application Usage Mining and Web Usage Mining
that constitute weaknesses.
Fifth International Symposium on Business Modeling and Software Design
22

Figure 1: Product entry process.
5.1 The Example of the Data Entry
Process
To be able to describe the challenges a scenario was
developed, in which a product entry process is
performed. In figure 1, the process is illustrated in
the KMDL process view (Gronau, 2005).
The process goes as follows: after a successful
order from the procurement process (process
interface), the goods are delivered. The delivery is
accepted and driven through a scan gate. The
information system „scan gate control“ shows the
warehouse employee (role) the listing of the
delivered parts on a client computer and shows the
serial, order and supplier numbers from the RFID
tags of the packages. After checking that
information, the warehouse employee approves the
delivery document for the delivery man and in the
scan gate control Software the parts list. The data
from the scan gate control system is entered into the
ERP system as a quality control system. In the ERP
system, a new bill of delivery is generated that
contains the articles and their amounts. The
warehouse employee unpacks the goods and gets the
bill of delivery in the ERP system in order to check
the quantities. After confirming the amount, the ERP
bill of delivery is handed over for inspection to the
Quality Control System, which creates a test order,
which includes the different properties of the
articles. The Quality employee checks the properties
per serial number and deposited a test result. Some
parts are identified as unsuitable. This has to be
entered into the quality control system. Most articles
pass the amount check successfully. Acknowledging
that the ERP system has been checked automatically
produces the results of the quality assurance task and
generates a storage location for every serial number
of the delivered goods in the bill of delivery. Good
parts are now transported to the manufacturing
storage, bad parts into a reclaim storage yard.
Articles marked as consumables are stored in the
warehousing process. In process step book delivery
note the articles in different charge carrier / transport
units are separated. Then, the delivery will be
booked. The ERP system generates from a transfer
order, which is processed by the logistics depart-
ment. Hence, the goods are stored in different
locations.
5.2 Challenges of Automated Process
Model Discovery
The first challenge for the approaches from section 3
is to recognize that the procurement process is the
trigger for the warehouse entry process. In the
process “assume delivery,” the order number is the
only connection between these two processes. This
connection has to be recognized and assigned to the
order process. This is the main result to expect from
the method to be developed. Therefore, the
recognition mechanism has to find out that the same
order number is now used during the delivery, and
has to interpret this as a unique number.
Using the statements above we can formulate
some requirements for the necessary recognition
mechanism.
The method has to reliably find out the unique
identifiers by screen capturing and OCR.
A second challenge can be derived from the
“assume delivery” process. Here it is necessary to
find out that the serial numbers indicate the different
flows of goods. To achieve this, the products that are
equipped with RFID tags at different locations
(storage, quality control, ...) show the serial numbers
and the current location belonging to that serial
number. Of course, the information about the
locations of the RFID reader stations must be
known. Second challenge: The method must
combine information from the screen capturing with
the determined location of the goods.
Another challenge is the assignment of roles and
information systems to the steps of the process. The
warehouse employee logs himself into the system
with his mobile device and connects himself to the
scan gate control system. The operating system then
captures the user group or the log-in name. With
Automated Process Model Discovery: Limitations and Challenges
23

other information systems the process is performed
in a similar manner. The operating system registers
the usage of the scan gate control software and
allocates this software to the process. The only role
in the exemplary process that cannot be captured in
this manner is the delivery man. Third challenge:
The method must be able to recognize the external
and internal roles involved in the process.
The next challenge is it to differentiate between
the different process steps. In the example, the
differentiation between the process steps “assume
delivery” and “check quantity” can be performed by
different information systems. When the process
transfers from “check quantity” to “check quality”,
different roles and different systems allow one to
find out that different process steps are performed. A
differentiation on that level is at any rate impossible.
An example of this can be seen in the work of a
sourcing employee who works with an ERP system
and does everything in the sourcing process, from
ordering to invoice checks, on his or her own. Fourth
challenge: The method must discover the different
process steps and be able to see the limit of one
process step and the beginning of another one.
The fifth challenge is to find out the description
of the process. To that end, a lot of information is
collected from OCR or screen capturing, but their
interpretation is difficult. An example is the “check
quantity” process, and the question: how can we
derive that term? One approach would be to assign
the function to the location; another approach would
be to use the window title of the ERP system
(“delivery note”). Sometimes this task can be done
by manual configuration, or by screen capturing.
The fifth challenge is, therefore, that the method
must be able to determine the name of a process
step.
The sixth challenge is to recognize different
target locations (storage locations in the logistic
process) from logistics and from the transport of
goods. Therefore, these different locations have to
be distinguished in the process by using different
process interfaces. To meet this challenge, the
master data of the storage in the warehouse
management system could be used to help
understand the structure of the storage groups and
their functionality in the process. Another possibility
is to assign this information to the different
locations. When this information is available for
differentiation, the process interfaces into the storage
area can be reconstructed. The sixth challenge is:
The method must have knowledge that specifies the
environment.
6 CONCLUSION AND OUTLOOK
The contribution has shown that an automated
discovery of process models is possible when some
new approaches are applied. The investigation of
current approaches showed that systems and
technologies deliver valuable information about the
process flow, but a configuration for a case of
specific use is necessary. The main gap in the
research is the lack of consideration of human tasks
and environmental data.
For the research task to develop a new integrated
approach, a couple of challenges must be dealt with.
One of the most important requirements of a new
method is to see the corporation and its data sources
in an integrated manner. Another important topic is
the collection of data according to location, time and
their connection to the process model. No
satisfactory answer could be given to the research
question concerning which information about the
user and the environment has to be collected in order
to be able to sufficiently discover process models.
On one hand, information about location must be
available (for instance which task is performed
where), while on the other hand, the master data that
holds that information has to be investigated. In any
case, the demand for and benefit of that kind of input
can be shown. Finally, there remains the question of
how the recognition mechanism uses semantic
techniques. Here it might be possible that the user
has to assist the recognition mechanism to describe
the process models.
An open issue after creating process models is to
interpret these semi-formal models. To reach an
understanding about a process solely by using a
model is very difficult. The authors thinks that
human beings, too, will have to participate in that
process in the future.
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