Process Management Enhancement by using Image Mining Techniques:
A Position Paper
Myriel Fichtner
, Stefan Sch
and Stefan Jablonski
University of Bayreuth, Germany
University of Regensburg, Germany
Image Mining, Process Model Enhancement, Quality Control, Recommendation System, Process Redesign.
Business process modeling is a well-established method to define and visualize business processes. In complex
processes, related process models may become large and hard to trace. To keep the readability of process
models, process details are omitted. In other cases, process designers are not aware which process steps should
be modelled in detail. However, the input specification of some process steps or the order of internal sub-steps
could have an impact on the success of the overall process. The most straightforward solution is to identify the
cause of reduced process success in order to improve the process results. This can be challenging, especially in
flexible process environments with multiple process participants. In this paper we tackle this problem through
recording image data of process executions and analyzing them with image mining techniques. We propose to
redesign business process models considering the analysis results to reach more effective and efficient process
Companies use business process models to visualize
and control internal workflows describing the nec-
essary process steps for each process participant to
reach a company goal, for example manufacturing
a certain product. Such process models may con-
tain hundreds of modeling elements leading to large
and hardly traceable process models (A. Polyvyanyy,
2008b). Besides, though there exists modeling rec-
ommendations regarding general aspects like correct-
ness or comparability (J. Becker, 2000), there is no
concrete rule or guidance how detailed a process has
to be modelled. Thus, it is quite obvious that pro-
cesses are often modelled in an abstract way to keep
up clarity and traceability. Sometimes also missing
knowledge about process details prevents a process
modeller to add more detailed (sub-)steps. This high
level of abstraction leads to the fact that detailed infor-
mation of process steps are omitted (R. Bobrik, 2007),
(A. Polyvyanyy, 2008a). Two typical examples illus-
trate the observation from above.
In a first case, diverse liquids have to be filled into
a casting mold. Due to missing knowledge or due
to keeping the process description simple, the pro-
cess modelers describe this process step on an abstract
level as “add all ingredients”. Looking into process
details reveals that three ingredients have to be added.
So the process could also be refined into three process
(sub-)steps ”add blue liquid”, ”add red liquid”, and
”add green liquid”. Process participants are free to
choose the execution order of low-level process steps
which are not modelled but contained implicitly, lead-
ing to an excessive flexibility in the execution step.
In some other cases, process modelers are not aware
of the optimal execution sequence of process steps.
For instance, they allow that two steps are executed
independent from each other, i.e. in arbitrary order.
However, the execution sequence does have an im-
pact on process performance, regarding the execution
time and/or the quality of process outcomes. Follow-
ing the example above, the addition of the red liquid
as second step instead of third step results in a mixture
with reduced binding capability, i.e. this execution se-
quence is finally not desired. This affects the overall
process success since the result of any subsequent task
depends on the quality of the mixture.
In a second case, parts have to be disposed on a
pallet. Again, due to missing knowledge or due to
keeping the process description simple, the process
designers are not more specific about placing the parts
on the pallet. Furthermore, some tasks are hard to de-
scribe or important details can’t be described with es-
tablished process modeling languages. In this palleti-
Fichtner, M., Schönig, S. and Jablonski, S.
Process Management Enhancement by using Image Mining Techniques: A Position Paper.
DOI: 10.5220/0009573502490255
In Proceedings of the 22nd International Conference on Enterprise Information Systems (ICEIS 2020) - Volume 1, pages 249-255
ISBN: 978-989-758-423-7
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
sation scenario this may include (i) information con-
sidering the position of objects in the product environ-
ment, (ii) information that is related to special move-
ments. If modellable, such information can only be
integrated in a process model through a large num-
ber of process modeling elements. Especially if dif-
ferent alternatives are allowed, the number of mod-
eling elements strongly increases what contradicts
the ideal to keep business process models traceable
through preserving a certain level of abstraction. Al-
though there are approaches that deal with that chal-
lenge, e.g. (Wiedmann, 2017), (M. La Rosa, 2011),
(A. Polyvyanyy, 2008b), they assume that knowledge
about the correlation between this details and the pro-
cess success already exists. To guarantee the success
of a process and to enhance its execution, necessary
details of activities that are not contained in the busi-
ness process model have to be discovered while satis-
fying the following three conditions:
1) Process analysis has to be done automatically since
participants are not aware that execution details may
influence the process success. Also in the case that the
process model is executed for the first time, no prior
or expert knowledge exists. Furthermore, the analysis
should be done with a minimum interference in the
regular working process to ensure correct results.
2) The analysis should be able to identify the cause of
reduced process success by extracting necessary in-
formation and developping suggestions for improve-
3) The extracted information has to be represented in
the process model in an appropriate way.
Although quality control and process monitoring
are popular topics in research, existing approaches
(e.g. (D. E. Lee, 2006), (T. Pryk
ari, 2010)) focus on
the identification of deviations in process results but
do not meet the listed requirements. In this paper we
suggest to tackle this problem by recording and ana-
lyzing image data of activity executions. We propose
to apply image mining techniques in the process man-
agement context to enhance business process models
and ensure process sucess.
In our work, we extract necessary process steps
and related information from image data which is not
yet contained in the existing process model. In or-
der to identify these missing details, we propose to
use established image mining techniques. The pro-
cess model is then enriched by this content through
adequate techniques. We therefore go beyond the lim-
itations of regular process modeling languages.
In general camera systems are cost-efficient
sensors which often already exist in small- and
medium-sized enterprises and industrial working ar-
eas. Recorded image data created in such process en-
vironments contains valuable information which is of-
ten not fully analyzed. Image data can either contain
static information, like the input or output of a task or
dynamic information, like the task processing itself.
Both may contain necessary information for process
enhancement. In our conceptual approach, we there-
fore dinstinguish between the analysis of images and
videos. Images are related to snap-shots of the execu-
tion while videos capture the whole execution of the
task including related subtasks.
Our overall system meets all three requirements as
described above. Furthermore, the image analysis re-
sults of our conceptual approach may serve as input
for recommendation systems to support given recom-
mendations. For example, our system reveals the best
execution of an activity and therefore identifies the
most suitable process participant for this task. In con-
trast to previous work that relates images to process
context, we focus on the process execution step and
analyze recorded image data that contains real infor-
mation of the process environment.
The remainder of this paper is structured as fol-
lows. The following section summarizes background
information and gives an overview of important re-
lated work. Section 3 presents our conceptual ap-
proach to reach process enhancement through analyz-
ing image data and points out our contribution. We
conclude our work and give a recommendation for fu-
ture work in Section 4.
In general, images can be understood as complex
data collection. Depending on the context in which
they are created, the knowledge about this context
and other associations, images may contain meaning-
ful information if analyzed and interpreted correctly.
How to achieve an effective extraction of this infor-
mation is the research question in work that is re-
lated to image mining. According to (J. Zhang, 2001),
image mining deals with the extraction of implicit
knowledge, image data relationship, or other patterns
not explicitly stored in the image databases. Among
others, different methods from computer vision, data
mining and machine learning are used to process low-
level pixel representations contained in raw images or
image sequences in order to identify high-level spatial
objects and relationships. The overall image mining
process is well described in (M. Hsu, 2010). Summa-
rized, the process can be divided into three parts:
Pre-processing: In order to reduce the cost of the
analysis step which can be high in time and space,
ICEIS 2020 - 22nd International Conference on Enterprise Information Systems
images have to be preprocessed. Therefore unneces-
sary or unrelated data is cleaned up and quality reduc-
tions due to noise are eliminated through filtering op-
erations. This step may include image-thresholding,
border-tracing an wavelet-based segmentation.
Feature Extraction: Algorithms are used to detect
features such as shapes, edges or other basic elements
in the images. Therefore the image content is reduced
while unimportant features can easily be discarded. A
promising feature extraction approach can be found in
(P. G. Foschi, 2002), where a combination of the fea-
tures color, edge and texture is suggested.
Image Mining Technology: Image mining tech-
niques are used on the extracted feature vectors to
reveal, evaluate and explain high-level knowledge.
Several methods have been developped which real-
ize this procedure in different ways: Image classifica-
tion, clustering, indexing and retrieval, object recog-
nition, association rule mining and approaches that
work with neural networks.
The techniques are used in many different real-
world applications, like for example the analysis of
paths and trends of forest fires over years in satel-
lite images in order to enable firefighters to fight fire
more effectively (J. Zhang, 2002). Another work
uses images gathered from the Web for learning of
a generic image classification system and enables a
Web image mining approach for generic image clas-
sification (Yanai, 2003). Image mining was intro-
duced by (Ordonez and Omiecinski, 1998) as new ap-
proach for data mining. The fundamental concepts of
discovering knowledge from data stored in relational
databases are transfered to image databases.
Related to this idea and the fact that process min-
ing builds on data mining, also the association of im-
age mining with process mining techniques or rather
the application of image mining in the context of busi-
ness process management is reasonable. However,
the application of image mining techniques or in gen-
eral the integration of images in this field is not yet
fully explored. The work of (Wiedmann, 2017) and
(R. Schmidt, 2016) suggest two different approaches
to introduce images and related mining techniques in
business process management. In the thesis of (Wied-
mann, 2017), the business process modeling language
BPMN is extended to a more intuitive modeling lan-
guage which allows to annotate tasks with multime-
dial content like images or videos. This approach en-
ables to add non-formalized descriptions to a process
task and enriches the process model with additional
information. During the execution of this task, pro-
cess participants can follow the referenced execution
in the video. The work of (R. Schmidt, 2016) con-
firms the potential of image mining for business pro-
cess management. The process management lifecycle
according to (M. Dumas, 2013) is presented and the
application and intregation of images and suitable im-
age mining techniques for each phase are discussed.
This approach focuses on image data which is cre-
ated in each phase. The authors differentiate between
documents, drawings and pictures, while documents
contain textual information and are analyzed with op-
tical character recognition methods. Drawings and
pictures are analyzed by using one of the image min-
ing techniques as described above. Furthermore, the
authors present a protoype for object recognition of
business process models which detects modeling ele-
ments like gateways, activities etc. from images.
Altogether, both authors explain how images con-
tribute to support the overall process. Particulary in-
teresting is the suggestion of (R. Schmidt, 2016) to
analyze pictures, taken with phones or tablets, which
contain information of the production environment.
Covering the process monitoring and controlling step,
these images are analyzed subsequently in order to
find possible process improvements. This proposal
corresponds with our approach, while we share the
idea that any issues that may reduce the overall pro-
cess success could revealed through monitoring the
process execution.
In contrast to the work of (R. Schmidt, 2016), we
suggest a concrete approach that aims to process im-
provement through an overall system that solely bases
on the image data that is produced in the process ex-
ecution step (cf. Section 3). Our system is restricted
to images or videos which contain real information of
the production environment or capture actual states of
a product. We go further by restoring the analyzed im-
provements in the existing process model. For this we
propose to translate them in the considered process
modeling language or to use media annotations like
suggested by (Wiedmann, 2017). Compared to clas-
sical machine vision approaches that control the qual-
ity of a product like (Manigel and Leonhard, 1992),
(H. Paulo, 2002) or (E. Saldana, 2013), our concept
starts one stage earlier and identifies the causes of de-
fects in products if they are related to human task ex-
ecutions. However, such systems can easily be inte-
grated in our overall concept while the implemented
techniques can be used in the image analysis step of
our concept.
We illustrate our conceptual approach by a running
example. In this example, a certain product has to be
manufactured according to a process model PM. We
Process Management Enhancement by using Image Mining Techniques: A Position Paper
Figure 1: Our overall concepts consists of 5 high-level steps while in one cycle, task t
of all process tasks T is taken into
account. Furthermore, the existing business process model PM is adapted to PM
and the database DB serves as storage
for pairs of recorded image data (images and videos) and related feedback of each process execution (i, f ). Based on the
validation of the calculated reference data r, either PM is extended with missing information to PM, or new reference data
is evaluated or t
T is taken into account.
assume that PM contains all tasks T of the process
necessary to create that product. Although all (most)
process executions were successful, i.e. the manufac-
turing processes were completed correctly, the final
products were revealing quite different quality. Since
the process model is already in place and process ex-
ecutions did not show errors, the assumption was that
not all process steps were modelled – and thus finally
performed optimally. The process experts presumed
that either (i) some of the process steps were not mod-
elled in enough detail, i.e. are too abstract since in-
ternal sub-steps are not specified sufficiently (process
steps of Type A); or that (ii) the input specification
of some process steps were not modelled prescrip-
tively enough (process steps of Type B). Focusing
these two flaws in this paper, our conceptual approach
comprises the following 5 steps (cf. Figure 1).
1. Preparation.
By examining the process model, the process
experts identify all tasks of Type A and Type B
as candidate steps that might lead to disparate
production results. Although, at this point in time,
the process experts still do not know whether this
assumption is correct and in case it is correct
how the solutions would look like. All tasks that
meet these conditions are summarized in a task
list T
T . To continue our overall example, we
assume that we analyzed PM while identifying
exactly two tasks t
(”add all ingredients to
the glass”) matching Type A and t
(”place all
machine parts on the palette”) matching Type B
with t
, t
T out of overall n tasks (|T | = n)
leading to T
= {t
, t
2. Definition.
In this step, all tasks contained in T
are analyzed
incrementally. That means that any task t T
is selected and prepared for further examination.
Therefore PM has to be redesigned to PM
the definition of t has to be adapted as follows. If
t belongs to Type A, the new definition of this task
has to contain the information that its execution
has to be monitored by recording videos of the ex-
ecution. If t belongs to Type B, the task has to be
redesigned so that an image has to be taken. De-
pending on the context, the image has to be taken
at the beginning, before starting the execution or
after finishing the execution of t. Furthermore, a
camera system has to be provided and installed in
order to enable the recording of image data. In our
example, the process experts perceive that task t
is composite. However, they are not sure what
the sub-tasks are at all. So, they should moni-
tor the upcoming executions of this process step
by videoing it. Additionally, the process experts
see that task t
requires a complex input config-
uration. Thus, it is proposed that process experts
take pictures of the input configuration of upcom-
ing process executions. Due to the presumption of
the process experts that t
might have more impact
on the overall process sucess, they decide to start
with the analysis of t
. Therefore PM is redefined
in the way that an image is taken after placing all
machine parts on the palette.
3. Execution and Labelling.
In this step, the redefined process model PM
executed and image data (images and videos) is
generated as specified in the model. At the end
ICEIS 2020 - 22nd International Conference on Enterprise Information Systems
of each process execution, the data is labelled,
i.e. process experts evaluate the process success
through giving feedback considering different cri-
teria. Traditional criteria that are related to pro-
cess success are the production time, cost and
quality of the result (A. Collins, 2004). We sug-
gest to implement the feedback step at the end of
the overall process execution in order to identify
the influence of the considered task on subsequent
tasks in the process model. Depending on the
situation and the issue, giving feedback directly
after the execution of the considered task possi-
bly does not reveal correlations within the process
like we will see later in the example. However, if
the feedback is given directly after the execution,
the considered task is analyzed related to classi-
cal machine vision approaches but provides more
flexibility. For both alternatives it should be en-
sured that all feedback refers to the same criterion
in order to enable comparability between them.
Any feedback f and the related image data i that
has been recorded during the execution step are
stored as pair (i, f ) in a database DB. Each set
of image data referring to one execution is hence
associated with an evaluation of success. This
procedure is repeated until DB contains enough
data to serve as basis for a meaningful analysis.
In our running example, we assume that PM
executed 100 times under the assumption that we
know that 100 executions provide a sufficient data
basis for analysis. This means that 100 images are
recorded, which show the resulting palette after an
employee placed all machine parts on it. Together
with the associated feedback 100 pairs (i, f ) are
stored in DB.
4. Image (and Video) Analysis.
In this step, all entries in DB are analyzed by us-
ing image mining techniques including all 3 parts
of the image mining process (cf. Section 2). De-
pending on the type of i that is stored in DB, dif-
ferent objectives are defined and therefore differ-
ent methods have to be applied. In all cases, the
overall goal is to analyze the differences between
all entries and to relate them with the feedback in
order to identify which differences cause good or
insufficient process results. Based on the analysis
results, reference data r is created which serves as
input for the validation step and the camera sys-
tem. This data can be understood as the image
data which is recorded in the context of an ideal
execution and is used as guideline for further exe-
If DB contains images (i.e. i is from Type A), it
holds entries capturing the same state of each pro-
cess execution. To somehow compare these im-
ages and to find relevant features that differ them,
image retrieval techniques as well as image clas-
sification and image clustering are suitable meth-
ods. But also the application of neural networks is
quite promising, especially in finding an adequate
reference image. The most promising image, i.e.
the arrangement that is rated best after feedback
evaluation, is stored as guideline in r.
If DB contains videos (i.e. i is from Type B), it
holds several recordings of the full execution of
the same task. As described above these execu-
tions may differ in the order of underlying sub-
tasks. Therefore all subtasks has to be identified
in a first step and then the order of them has to be
analyzed and compared in a second step. These
requirements could be reformulated as challenge
to extract event logs and therefore process mod-
els from videos and to find techniques to compare
them. The order of the subtasks that has the most
success is finally stored in r.
In our example, the 100 entries in DB are ana-
lyzed, while it turns out that the positioning of the
machine parts on the palette seems to have an im-
pact on the overall process sucess. The image data
which refers to an ideal execution is stored in r
and is prepared as input for the camera system.
5. Validation.
The instructions identified in phase ”Image Anal-
ysis” are incorporated into PM
resulting in PM,
while t is modified to match r. Therefore PM can
be extended by additional modeling elements to
include necessary subtasks or other detailed in-
formation. Alternatively we suggest to follow the
concept of (Wiedmann, 2017) and to use media
annotations. This approach enables the integra-
tion of image data and allows the use of a more
powerful process modeling language what supp-
ports our idea of process enhancement.
Afterwards, this new process model version PM
is further executed. Observing and measuring
process success determines whether this new ver-
sion of a process is accepted or further investiga-
tion have to be undertaken. In the latter case, the
whole process improvement process has to start
from its beginning.
To realize this procedure, t is still taken into ac-
count and image data is recorded while PM is
executed. In contrast to the previous execution
step related to PM or PM
, process participants
now execute an estimated more successful version
of t. Furthermore, the camera systems recognize
deviations, leading to an interruption of the exe-
cution if the recorded image data differs from r.
Process Management Enhancement by using Image Mining Techniques: A Position Paper
Figure 2: The image i recorded after executing a task (left), the reference image r (middle) and the calculated difference image
(right). Black areas in r refer to pixel colors that deviate from each other, while white areas indicate same pixel colors.
If it does, the execution has to be adapted until
it matches r including a predefined threshold. In
this step, again image mining techniques are used
to identify this deviations. Just as in the previ-
ous execution step, pairs of image data and feed-
back are collected and analyzed in order to vali-
date if the restriction to executions that relate to
r really lead to process sucess. If the validation
fails and DB still contains negative feedback, ei-
ther r has to be recomputed based on a larger num-
ber of entries in DB or t was not the task that led
to the unsatisfying process success and the pro-
cedure has to be repeated for the next task t
while T = T
\{t}. If the definition of r as guide-
line leads to more successful process executions,
the modified process model PM replaces PM.
Applied to our example, we assume that the pro-
cess model is adapted as described above. We fur-
ther assume that during the execution of t
, de-
viations are detected because a machine part was
placed wrong on the palette. For this we imple-
mented a prototype and built up a small scenario
according to t
as shown in Figure 2. While there
exist several approaches to find the difference in
two frames, one of the easiest ways is to determine
the pixel differences. In this technique the num-
ber of pixels that change in value more than some
threshold are counted (cf. (Boreczky, 2006)). By
comparing i and r it can be seen that two of three
machine parts differ in their position and orienta-
tion leading to their reoccurences in the difference
image. In this example we assume that the valida-
tion step fails. The process experts decide to fur-
ther examine t
instead of taking t
into account.
Therefore further process model executions are
performed in order to reach a larger data set for
DB and to identify a better reference image r
The presented example shows how our concept can
be applied to enhance processes through using image
mining techniques. Our approach enables the identi-
fication of process model execution details that have
an impact on the overall success of the process. The
existing process model is only extended by informa-
tion, which is identified as necessary in the analysis
step. Therefore the predefined abstraction of the pro-
cess model is preserved but details which improve the
overall process are included.
Since the presented idea considers the overall success,
it is possible to reveal complex interrelations and de-
pendencies between tasks that are not contained in the
process model but influence the process result. Qual-
ity assurance approaches only evaluate the result of
a single (mostly non-human) task execution. There-
fore they are only able to analyze the direct correla-
tion of a task execution with its output. In contrast, we
suggest to consider the overall process success and to
focus on all tasks, where the input is not fixed or mul-
tiple executions are possible. Our concept therefore
enables to analyze the effect of a task on a subsequent
task which leads to a reduction of the process suc-
cess. In most cases, all single tasks were executed
according to their rules defined in the process model
while an direct evaluation of the task execution result
would confirm process success. In contrast, the re-
sult of the overall process might not be satisfying. At
this point, existing approaches reach their limitations
since they are not able to identify the cause of the re-
duced process success sourcing in the dependency be-
tween tasks. Like described above, our concept tack-
les this issue and provides a more general process ex-
ecution analysis.
Our approach is based on the analysis of image data
while we therefore introduce an innovative appli-
cation of image mining techniques in the process
management context. Image data serves as valuable
source for complex analysis since it has high informa-
tion content, provides several features and supports
the flexibility of our approach. This flexibility reflects
in the possibility to deal with static as well as dynamic
information related to the process environment.
ICEIS 2020 - 22nd International Conference on Enterprise Information Systems
Concluding, our system does not only extract relevant
features from images but identifies process execution
dependencies affecting the process success and sug-
gests reference data related to optimal executions. We
want to point out that the intregration of this infor-
mation in the existing process model is important to
ensure process success in future executions, while we
suggest media annotations presented by (Wiedmann,
2017) as adequate technique.
In this paper we present our idea to use image min-
ing techniques in order to enhance business pro-
cesses. We suggest a flexible approach which is
based on monitoring process executions while image
data related to the process environment is collected.
This image data is labelled and analyzed to identify
execution-specific features that have an impact on the
overall process sucess. We further show how this in-
formation can be integrated in existing process mod-
els to enhance future executions without reducing the
traceability of process models. The review of exist-
ing approaches confirms that the application of image
mining techniques in the process management context
is an open research gap.
As for future work, technical aspects have to be
discussed which include the evaluation of existing im-
age mining techniques depending on the use case.
Furthermore, we plan to implement an overall pro-
totype and to proceed evaluations with simple exam-
ples. Finally, we intend to further investigate the real-
ization of our idea in real process environments. We
will collect broader use case scenarios and explore
suitable application areas.
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