=Paper=
{{Paper
|id=Vol-1930/paper-7
|storemode=property
|title=Activity Duration Prediction of Workflows by
using a Data Science Approach: Unveiling the Advantage of Semantics
|pdfUrl=https://ceur-ws.org/Vol-1930/paper-7.pdf
|volume=Vol-1930
|authors=Tobias Weller,Maria Maleshkova
|dblpUrl=https://dblp.org/rec/conf/semweb/WellerM17
}}
==Activity Duration Prediction of Workflows by
using a Data Science Approach: Unveiling the Advantage of Semantics==
https://ceur-ws.org/Vol-1930/paper-7.pdf
Activity Duration Prediction of Workflows by
using a Data Science Approach:
Unveiling the Advantage of Semantics
Tobias Weller and Maria Maleshkova
AIFB Institute (KIT)
Kaiserstr. 89, 76131 Karlsruhe, Germany
{tobias.weller,maria.maleshkova}@kit.edu
http://www.aifb.kit.edu.
Abstract. Organizations often have to face a dynamic market environ-
ment. Processes must be frequently adapted in order to stay competitive
and allow an efficient workflow. Data Science approaches are currently
often used in analysis methods to identify influential indicators on pro-
cesses and learn predictive models to estimate the duration of an activity.
However, current methods do not or only partially make use of semantic
information in process analysis. The results are unprecise or incomplete,
because not all influential indicators have been unveiled and therefore
used in the predictive models.
We want to make use of the semantics and show the advantage by apply-
ing them on existing data science methods for predicting the duration of
an activity in a process. Therefore, we 1) enrich process data with meta-
information and background knowledge 2) extend existing data science
methods so that they include semantic information in their analysis and
3) apply data science methods for predicting values and compare the
results with methods, which do not use semantics.
Keywords: Data Science, Workflow Analysis, Semantic Annotations,
Activity Duration Prediction
1 Introduction
Processes and data are key elements for value creation in companies. A large
number of heterogeneous processes contribute to the added value. Large amount
of data is created during the execution of processes. These process-generated
data is increasingly an essential part of the respective business models. Therefore,
Information Systems like ERPs (Enterprise Resource Systems), WFM (Work-
flow Management Systems) and SCM (Supply Chain Management Systems) are
recording events occurring in workflows in a structured way as they take place in
order to comprehend past workflows but also using the information in possible
analysis. In practice, however, it is often not possible for the employees, involved
in the processes, to have all the relevant processes within the organization in
mind. This hampers to include all the resulting data in possible analysis for
2 Tobias Weller and Maria Maleshkova
the detection and optimization of weaknesses in a process, as well as using it in
predictive models. However, customers and logistics are interested in knowing
the duration of an activity. To improve processes and satisfy the requirements
of customers and logistics is process analysis a continuous and important task of
organizational development. The purpose of the process analysis is to evaluate
organization-specific processes, to identify errors and to improve the possibili-
ties, and to identify deviations from predefined standards, guidelines and existing
processes in a system [11].
Process Mining [17] is a technique in the rapidly growing data science dis-
cipline for analyzing complex processes. It combines traditional model-based
process analysis and data mining techniques. It is used in various domains such
as e.g. health-care [17, 18], and industry [1]. This well-known technique is often
used because it allows for making implicit, and thus hidden, knowledge about a
process transparent. Thus techniques are used to estimate the time of a task by
using regression models [6] and descriptive statistical methods [2]. At the mo-
ment, however, no semantics are used, and no background and expert knowledge
is taken into consideration. Previous research has shown that the inclusion of
background knowledge on data improves analysis in clustering [29] and similarity
analysis of processes [8]. We want to strengthen this fact by including semantics
in process data and apply well-established Process Mining methods on process
data, which is not enriched with semantic information, and on process data that
is enriched with semantics. We draw a comparison between the results of both
approaches. Our goal in this work is to unveil the advantages of including seman-
tics in Process Mining methods. We believe that by clearly demonstrating these,
the trend of using semantic information in data science, as well as in process
mining, approaches will grow, due to the key fact that the analysis results are
improved.
In order to achieve our goals, based on previous work we i) represent a pe-
rioperative process in sBPMN and extend our annotation tool to ii) capture
meta-information, as well as background knowledge about the process, iii) apply
data science methods on the process for analyzing influencing factors on perfor-
mance indicators of the process and iv) predict the duration of an activity by
using different approaches, among others based on semantics and the analyzes
of influencing factors. The used materials and methods are shown in section 2.
We explain the approach, as well as the metrics used in the experiment sec-
tion for comparing the results. The concrete implementation and evaluation of
our approach is shown in section 3. We model a perioperative process and en-
rich it with meta-information and background knowledge. The evaluation of our
approach includes showing the application, and comparing the results of the dif-
ferent approaches by using well-known metrics, including Root Mean Squared
Error and Accuracy. Especially we go into detail in comparing other approaches
with our approach, which exploits semantic information. The semantic infor-
mation is modeled according to the Linked Data principles [3]. Related work is
described in section 4. A short discussion, outlook and lessons learned is given
in section 5.
Activity Duration Prediction of Workflows 3
2 Material And Methods
The main focus of this work is supporting process analysis by predicting the
duration of an activity. We include background knowledge and exploit semantic
descriptions of the process in order to make the predictions even more precise,
compared to existing approaches. As an additional benefit of this work, the
advantage of semantics is revealed. In the following we provide details on our
approach. First we state our basic assumptions, as well as the modeling approach
for the workflows. After the workflows are modeled, we explain the enrichment
with additional information, including background information from experts. In
the end we explain the used methods to predict the duration of an activity and
describe the evaluation metrics, that are used to compare the results. There, we
are keen to make our approach as comparable as possible and reuse well-known
data science methods, as well as using leading evaluation metrics for comparing
the results. We depict our approach in figure 1.
Fig. 1. Performed approach in this work to predict the duration of activities.
We assume a given set of process instances, stored in log files. This assump-
tion is depicted as Data Export in figure 1. Usually the data of a workflow is
recorded in an ERP system, which allows to export it in a flat file. Due to incor-
rect and contradictory entries in the datasets, we also assume a data refinement
step. Within this step, we delete false entries or replace them with the correct
entry, if the entry can be computed. For analyzing and enriching the process
instances and each of the corresponding activities with background knowledge,
we first have to model the processes in an appropriate way to have it available
in a structured format, which can be queried.
A key fact in Knowledge Management is sharing and creating information col-
laboratively. Various users might bring different kinds of background knowledge.
Thus, we rely on a collaborative design and usage of our methods. Therefore,
we apply a collaborative system that allows for a controlled environment and
for tracing the provenance of information. Semantic Media Wiki1 (SMW) [28]
serves as collaborative platform to capture and store annotations on documents.
We have already tackled the capturing of processes in a collaborative environ-
ment such as Semantic MediaWiki as part of previous work [31, 32]. This work
builds upon this previous results. We used a BPMN Ontology from the Data &
Knowledge Management (DKM) research unit [23] to structure processes. This
1
https://semantic-mediawiki.org, accessed: 2017-07-27
4 Tobias Weller and Maria Maleshkova
ontology has a very detailed formalization in OWL 2 DL of the BPMN 2.0 spec-
ification. Further ontologies that can be applied, in order to describe the data in
more detail, are foaf2 , Dublin Core3 and SKOS4 . These ontologies are used to
structure and describe the process data in detail. Background knowledge from
experts can now be applied to have their knowledge available in a structure and
reusable format. The captured knowledge can be used for analysis, as well as for
predicting the duration of an activity.
In addition to background knowledge from experts, meta-information about
the workflow of a process is added to the activities. The captured meta-information
is linked to the activities, which are influenced by or influence the information.
For instance, if the information about the death of a patient during a surgi-
cal process is available, it is attached to the activity that mostly causes this
circumstance. The advantage of this approach is to have the information in a
structured and standardized format that allows for querying and using it in
analysis. Other meta-information includes the persons involved in the task, the
runtime of an activity, and several parameters about the patient like if he is a
smoker and the number of stays in the hospital. The background knowledge and
meta-information are used for correlation analysis to identify and quantify the
influence of variables on the process [33]. We used, depending on the level of
measurements of the variables, different correlation analysis (Pearson Correla-
tion [21], Spearman Correlation [26], Kendall Correlation [12] and Chi-squared
test [22]). We build on these results in our models for predicting the duration of
an activity.
The main contribution of this work is the prediction of the duration of an
activity. Therefore, we use various approaches, among others one which exploits
the semantics in order to show the advantage of semantic information and a
well-considered Data Science approach. We are keen to present our methods in a
comparable way and reuse well-known methods and evaluation metrics. There-
fore, we apply methods on the process instances which do and do not exploit the
semantics. Previous methods [2] use the average value (and thus the expected
value) of the duration of an activity as prediction. For comparison purposes we
apply this approach in our scenario as well and call it in the following Average
Value Approach. However, this approach is very basic. In our opinion, a process
is more complex and has multiple key factors that influence the duration of an
activity. Therefore, we analyzed process data according to certain dependencies.
Thus we detected coherences between certain variables. The analysis exploits
semantic background knowledge [33]. Based on the correlations, we can use the
knowledge to select variables that influence the duration of an activity. Instead
of using the average of all duration of an activity, as estimator, we shrink the set
of activities to compute the average duration, based on similar variables which
2
http://www.foaf-project.org/, accessed: 2017-07-27
3
http://dublincore.org, accessed: 2017-07-27
4
https://www.w3.org/TR/2008/WD-skos-reference-20080829/skos.html, ac-
cessed: 2017-07-27
Activity Duration Prediction of Workflows 5
influence the duration. We call this approach in the following Semantic Average
Value Approach.
Besides these two approaches, we also use a regression model. Usually are
regression models used to quantify the coherence between values [10], however
the model can also be used to predict the outcome, based on input variables.
Thus we learn a regression model, based on the training data, and try to predict
the test data, based on the input parameter x. However, as a drawback to the
previous approaches, the regression models usually requires a lot of available
data. Otherwise, the detected coherence between variables are not statistically
verified. We call this last approach Regression Model Approach.
Same as the used methods, we use existing and well-known metrics to evalu-
ate the predictions of the duration of activities. Well-known metrics to quantify
the quality of predictions are Mean Absolute Error (MAE), Mean Squared Error
(MSE) and Root Mean Squared Error (RMSE). Especially the last one is com-
monly used as metric in forecasting models to quantify the result of predictions.
RMSE computes the standard deviation between the actually observed value and
the predicted value. MSE and RMSE are very similar, however, RMSE punishes
outlier more or rather is more sensitive to outliers. Another metric that we use
in our evaluation is the accuracy. Therefore, we compute the number of correct
predictions divided by the total number of predictions. However, due to a ratio
scale of the duration of activities, we allow a deviation of +/- 10%.
An overview of the metrics and their formula is given in table 1.
Name Formula
1
Pn
Mean Absolute Error (MAE) n Pi=1
|yi − xi |
1 n 2
Mean Squared Error (MSE) n
q i=1 i − xi )
(y
Root Mean Squared Error (RMSE) n1 n
P
i=1 (yi − xi )
2
Table 1. Metrics for evaluating predictions.
For evaluating our model, we use k-fold cross-validation [27]. This validation
technique is commonly used in statistical analysis. It splits the available data in
k folds and trains for each fold one estimator and evaluates the estimators on
the other k − 1 folds. This allows for testing the estimators on an independent
data set. k-fold cross-validation is exemplary depicted in figure 2 with k = 4.
For each estimator the metrics given in table 1 are computed. In addition, we
compute the standard deviation of the RMSE. A value close to zero of the stan-
dard deviation of the RMSE indicates a stable estimator over each independent
fold. Thus, an equally distribution of the duration can be assumed. The advan-
tage of the cross-validation is the usage of each observation for both, training
and validation, and each observation is used for training exactly once.
By using well-known metrics and validation techniques, we are able to com-
pare our results with other approaches and allow for an enhanced comprehen-
sibility of the results. The approach, as well as the used metrics, abstract from
6 Tobias Weller and Maria Maleshkova
Fig. 2. k-fold cross-validation example by using k = 4.
the use-case scenario and the domain. Thus, the approach can be applied to any
domain. In the next section we implement our approach in a health-care use-case
scenario and evaluate the different approaches. We compare the results by using
the above metrics.
3 Experiments
We evaluate our approach by using real-life data from the University Hospital
Heidelberg. The process we considered is a perioperative process which describes
the workflow of preparing the operating room, bringing a patient into the op-
erating room, the incision and suture of the surgery, and bringing the patient
out of the operating room. The considered process is depicted in figure 3 by
using BPMN. We only had rectum resection surgeries records available, so we
did not look at other performed surgeries. Therefore, we did not include the type
of surgery, because it is the same for all considered data sets.
Fig. 3. Considered perioperative process in the evaluation.
The data we received were stored in spreadsheet format. It contained in-
formation about the timestamps of every activity, basic information about the
patient, like e.g. the age, height and weight, as well as information about former
diseases and progress of the surgery. In total, 65 attributes were available. Not
all attributes were for each data set available. Some were missing or contained
false entries, like e.g. a negative loss of blood. Therefore, we first refined the
data. The refinement of the data was done manually. We calculated the dura-
tion of each activity according to the timestamps. However, some duration were
negative, which we ignored in our analysis, because negative durations are not
Activity Duration Prediction of Workflows 7
possible. We had in total 1,690 data sets available so that the refinement of the
data was a huge workload.
In the following, we consider three approaches: The first approach is the ba-
sic approach by using the average value of the historic data as prediction of the
duration for an activity [2] (Average Value Approach). The second approach is
by using our semantic analysis in combination with the existing approach to
show the advantage of semantics (Semantic Average Value Approach). We per-
formed the correlation analysis on the semantically enriched data in a previous
paper [33], so that we can build upon those results in order to use them for
predicting the duration of an activity. The third approach is by using regression
analysis to predict the duration of an activity (Regression Model Approach). We
use k = 10 as cross-validation, because it is commonly used and size of the folds
are appropriate. We use MAE, MSE and RMSE as evaluation metrics of each
fold and compute the overall accuracy and the deviation of RMSE to evaluate
the results.
Average Value Approach: We split the data set in 10 uniform folds and
computed on each the average value as predictive value for the other folds. We
computed for each fold the MAE, MSE and RMSE. Afterwards, we computed for
the RMSE the standard deviation to show how the value vary. A small deviation
would show that the root man square error is on every fold uniformly. We com-
puted for each activity the duration. Thus, in total, we performed our approach
on 11 activities. The standard deviation of the RMSE for the duration of surgery
is 15.2793. This shows that the predicted value of each fold is close to each other
and does not vary. An average accuracy of 19.2683% is accomplished by using
this method. Similar underperforming results are achieved by predicting other
durations like e.g. the preparation of the operating room (Accuracy: 13.4146%)
and the operating room time (Accuracy: 22.0122%), which is the time between
incision and suture.
Semantic Average Value Approach: There were 65 attributes available.
However, due to incomplete and inconsistent data, we could not use every data
point in our analysis [33]. Due to the performed correlation tests, we analyzed
the data and found out which attributes correlate to each other, among others
to the duration of activities. The facts if a patient has a relapse, the amount of
red cell concentrate, received Fresh frozen plasma, and if a patient received an
intraoperative radiotherapy influence the duration of a surgery. We focused on
meta-information, which was known for an activity in advance. We calculated
the average value of similar initial positions of the considered process, according
to the correlated attributes, and used this as predictive value. The standard de-
viation of the RMSE for the duration of surgery is 16.3042. Thus it is a bit higher
than the RMSE of the previous approach. However, this is lead by the adapted
predictions due to the correlated attributes. The average accuracy of predicting
the duration of a surgery is 24.6104% and thus better than the previous ap-
proach, were no semantics and the correlations were considered. Predicting the
8 Tobias Weller and Maria Maleshkova
other duration were better than the previous approach, too. The preparation of
the operating room achieved an overall accuracy of 19.5092% and the operating
room time had an accuracy of 25.6173%. This clearly shows that our approach
performs better than the basic procedure in all the three presented predictions
of the duration of an activity. We also achieved improved results in the other
nine cases of duration prediction. Nevertheless, we want to point out, that the
results of 20% − 25% is still not satisfying and needs to be improved.
Regression Model Approach: We compared our method with a regression
analysis as last evaluation part. Regression Analysis is used to predict the du-
ration of an activity in the past (see [6]). Therefore, for comparison reasons,
we applied a regression analysis, too. We used a Polynomial Regression Analy-
sis [14]. The best results were achieved by using a degree of 6 and using the age
of the patient as input parameter for the duration of a surgery. The standard de-
viation of the RMSE over all folds is 7.0497. This clearly points out the stability
of the estimators. However, the results are not as good as the previous approach,
in which we exploited the semantics. The accuracy of the Regression Model Ap-
proach for predicting the operating time is 18.7662%. Similar underperforming
results are achieved by predicting other duration like e.g. the Preparation of the
operating room (Accuracy: 14.1830%). Likewise the previous results, the operat-
ing room time had the highest accuracy of the three considered ones of 23.0263%.
The coefficient of determination of the regression analysis is R2 = 0.0168, which
shows that this model cannot explain the data sufficiently to predict values. Ta-
ble 2 summarizes the results of the overall accuracy of the evaluated approaches
and shows that exploiting meta-information and semantics achieves best results.
Exploiting these information, if available, and using it in data science methods
is essential for achieving better results.
Approach Operating Time Preparation of the Operating Room
Operating Room Time
Average Value Ap- 19.2683% 13.4146% 22.0122%
proach
Semantic Average 24.6104% 19.5092% 25.6173%
Value Approach
Regression Model 18.7662% 14.1830% 23.0263%
Approach
Table 2. Overview of Overall Accuracy of the different approaches.
4 Related Work
Our approach is addressed by roughly three kinds of work: 1) Match BPMN
process to sBPMN, 2) annotating business processes with meta-information and
4) using Data Science methods for predicting the activity duration.
BPMN [20] is a de-facto standard for representing processes in a very expres-
sive graphical. BPMN defines semantics partly, which means that the symbols
Activity Duration Prediction of Workflows 9
have meanings. However, the semantics just have little weights and there is no
much attention paid to the formal definitions of the symbols. One advantage of
BPMN is its executability [5]. Since 2011, it’s current version is BPMN 2.0 [20].
So far, existing work already addressed the transformation of BPMN into other
languages like e.g. BPEL [7] and Petri-Net [16]. sBPMN (Semantic BPMN) was
developed to allow for tackling the disadvantage of the lack of formal definitions
and provide an unambiguous and consistent semantics [13]. sBPMN extends
BPMN elements with additional information and background knowledge to en-
hance analysis [?].
The second aspect that is tackled in this paper is the annotation of business pro-
cesses with meta-information. This issue had been addressed among other by us
in previous works by stating out a meta-model for processes [34], as well as ap-
proaches for annotating decision trees and processes with meta-information [31].
Existing works has also pointed out the advantage of using meta-information
in process mining [24]. A published survey summarizes existing approaches for
business process annotations [15]. One aspect, considered in the survey, was the
possibility to capture semantic annotations. The meta-information in processes
are used for reasoning purposes, which is used to support analytics and opti-
mization of processes [19].
The last addressed issue in our work is using Data Science methods for predict-
ing activity duration. Previous approaches has mostly focused on control-flow
discovery [30, 9]. However, when event logs contain time information, the discov-
ered models can be extended with timing information. Most related to our work
is the time prediction by van der Aalst [2]. Van der Aalst took the expected
value in order to estimate the time of a future activity [2]. They only took the
structure of the workflow into account but no other meta-information. However,
there are factors, other than the structure of the workflow, which influence the
duration of an activity. In health-care these factors might be the age of a patient,
the condition and drugs that had been used by a patient. Therefore, this work
is not sufficient for a more precise prediction. Another related work is the esti-
mation of workflow execution time [4]. In this work, a more advanced method
to estimated the execution time of workflows were used. The execution time of
workflows was computed based on stochastic estimates of tasks’ execution time.
For calculating the tasks’ execution time, they split up its execution time in
multiple variables like e.g. resource preparation time, queuing time and data
transfer time. We do not go into this detail, because we even do not have this
detail of information in our time logs. For calculating the executions time, they
performed, like we did, statistical hypothesis test for checking dependent vari-
ables. Based on the results of statistical tests, they proposed a combination of
Chebyshev-like distribution-free inequalities and distribution-based approaches
to computer a tasks’ runtime.
10 Tobias Weller and Maria Maleshkova
5 Conclusions
We showed an approach of exploiting semantic information in process data.
Therefore, we first captured the process data and refined in the case of incon-
sistent and incomplete data. Afterwards we transformed the data into sBPMN
by using a BPMN Ontology from the Data & Knowledge Management (DKM)
research unit [23]. This enabled us to enrich it with further information in a
structured way and to use the semantic information about the process. We used
the results from the enrichenment with meta-informatio in our prediction mod-
els and compared it to existing standards. We used an existing approach [2],
based on the average value of historic data as estimator. We compared this ap-
proach with ours, which exploits the semantics, by computing the average value
of similar data sets, according to performed correlation tests, from historic data
as estimator. Thus, we did not used all attributes for determining similar data
sets, but selected ones, which were determined by using correlation tests. As a
last approach, we used a polynomial regression analysis to predict the duration
of an activity.
For evaluating the three approaches, we used k-fold cross-validation, with
k = 10. Therefore, every data set was used as test and training data. Besides
the fact that every data set is used as test and training data, we used it because
it provides more accurate results of the models [25]. We computed for every
fold the MAE, MSE and RMSE and provided the number of hits by allowing a
deviation of +/- 10%. These well-known metrics are usually used in Data Science
to indicate the results of a prediction model and allows for comparing the results.
We showed that our approach performs best, compared to the other ones and
achieved a better overall accuracy. In this work we focused on three out of 11
activity duration due to a better clarity. However, similarly to the presented
results, the Semantic Average Value Approach performed best for predicting
the other activity duration. The standard deviation of the RMSE over all folds
were in the Semantic Average Value Approach higher, compared to the other
ones. However, this is due to the individual predictions, based on the correlated
attributes.
Even if our approach outperforms the other two approaches, there is still a
lot of work to be done in this area. Computing the duration of an activity is still
a challenging task and the results also shows that, although we improved the
accuracy, they are still not satisfying. Therefore, we have to improve our model.
One way we would like to improve it is to include more attributes and data sets.
Due to the increasing amount of data that is nowadays stored, we see this as a
chance to collect more data and use it in our analysis and prediction models.
Another future work that we would like to tackle is to transfer our approach to
other domains and show its applicability. We built the approach in such a way
that it abstracts from the underlying data and domain. Therefore, we expect
that the approach is also usable in other domains and not limited to a certain
scenario.
In conclusion, we have taken a first step towards exploiting semantic informa-
tion of processes, stored according to the Linked Data principles for predicting
Activity Duration Prediction of Workflows 11
the duration of activities. We showed that our approach outperforms existing
ones. The semantics in the data can be used in order to improve existing meth-
ods.
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