=Paper=
{{Paper
|id=Vol-1289/kese10-05_submission_10
|storemode=property
|title=Integration of Activity Modeller with Bayesian Network Based Recommender for Business Processes
|pdfUrl=https://ceur-ws.org/Vol-1289/kese10-05_submission_10.pdf
|volume=Vol-1289
|dblpUrl=https://dblp.org/rec/conf/ecai/BobekNG14
}}
==Integration of Activity Modeller with Bayesian Network Based Recommender for Business Processes==
https://ceur-ws.org/Vol-1289/kese10-05_submission_10.pdf
Integration of Activity Modeller with Bayesian network
based recommender for business processes?
Szymon Bobek , Grzegorz J. Nalepa, Olgierd Grodzki
AGH University of Science and Technology,
al. A. Mickiewicza 30, 30-059 Krakow, Poland
{szymon.bobek,gjn}@agh.edu.pl
Abstract Formalized process models help to handle, design and store processes
in a form understandable for the designers and users. As model repositories of-
ten contain similar or related models, they should be used when modelling new
processes in a form of automated recommendations. It is important, as designers
prefer to receive and use suggestions during the modelling process. Recommen-
dations make modelling faster and less error-prone because a set of good models
is automatically used to help the designer. In this paper, we describe and evaluate
a method that uses Bayesian Networks and configurable models for recommen-
dation purposes in process modelling. The practical integration of the recommen-
dation module with a Activity Modeller tool is also presented.
1 Introduction
Processes are one of the most popular methods for modelling flow of information and/or
control within a sequence of activities, actions or tasks. Among many notations that
allow to define and build business process diagrams, the Business Process Modeling
Notation (BPMN) [1] is currently considered as a standard. BPMN is a set of graphical
elements denoting such constructs as activities, splits and joins, events etc. These ele-
ments can be connected using control flow and provide a visual description of process
logic [2]. Thus, a visual model is easier to understand than textual description and helps
to manage software complexity [3].
Several visual editors were developed to support design of business processes in
BPMN, one of which is Activity Modeller 1 . It is a web modeller component that is
available as part of the Activiti Explorer web application. The Modeller is a fork of the
Signavio Core Components project 2 . The goal of the Activiti Modeller is to support all
the BPMN elements and extension supported by the Activiti Engine – a Java process
engine that runs BPMN 2 processes natively.
Although visual editors like Activity provide support for building and executing
business processes, this support does not include design recommendations. By recom-
mendation we mean suggestions that the system can give to the designer to improve the
design process both in terms of quality and time.
?
The paper is supported by the Prosecco project.
1
See http://activiti.org/
2
See http://www.signavio.com/
Three different types of recommendations can be distinguished depending on the
subject of recommendation process [4]. These types are:
– structural recommendations – that allows to suggest structural elements of the BP
diagram, like tasks, events, etc,
– textual recommendations –that are used to allow suggestions of names of elements,
guard conditions, etc.
– attachment recommendations –that allows to recommend attachments to the BP in
a form of decision tables, links, service tasks, etc.
In this paper we focus on structural recommendation, that allows for automated
generation of suggestions for the next (forward recommendation), previous (backward
recommendation) or missing elements (autocompletion) of the designed BP. Such rec-
ommendations improves time needed to build new business process and prevents user
from making most common mistakes. What is more, such suggestions allow the de-
signer to interactively learn best practices in designing BPMN diagrams as this practices
are encoded into the recommendation model.
In this paper we present the implementation and evaluation of the method for struc-
tural recommendation of business processes that uses Bayesian networks and config-
urable processes. The work presented in this paper is part of the Prosecco project3 . The
objective of the project is to provide tools supporting the management of Small and
Medium Enterprises (SMEs) by the introduction of methods for declarative specifica-
tion of business process models and their semantics. The work described in this article
is a continuation of our previous research presented in [5].
The rest of the paper is organized as follows. In Section 2 related work is presented
and motivation for our research was stated. Section 3 describes briefly the recommen-
dation method developed. A prototype implementation of the recommendation module,
and its integration with Activity Modeller in Section 4. This section provides also an
evaluation of the method on a real-case scenario. Section 5 provides summary of the
research and open issues that are planned to be solved in a future work.
2 Related work and motivation
As empirical studies have proven that users prefer to receive and use suggestions dur-
ing modelling processes [6], several approaches to recommendations in BP modelling
have been developed. They are based on different factors such as labels of elements,
current progress of modelling process, or additional pieces of information like process
descriptions or annotations.
Among attachment recommendations, support with finding appropriate services was
proposed by Born et al. [7] and Nguyen et al. [8]. Such a recommendation mechanism
can take advantage of context specified by the process fragment [8] or historical data [9].
Approaches that recommend textual pieces of information, such as names of tasks, were
proposed by Leopold et al. [10] and extended in [11].
In the case of structural recommendations, Kopp et al. [12] showed how to auto-
complete BPMN fragments in order to enable its verification. Although this approach
3
See http://prosecco.agh.edu.pl
does not require any additional information, it is very limited in the case of recommen-
dations. The more useful existing algorithms are based on graph grammars for process
models [13,14], process descriptions [15], automatic tagging mechanism [16,6], anno-
tations of process tasks [17] or context matching [18].
Gather data Refine
about client information
about project
[1,2,3,4] [1,2]
Make the Make
schedule of settlements
the project
[1] [1] [1,2,4] [2]
Start of the
project
Perform
market analysis
[1]
Milestone
reached
[4]
Divide the
project into Perform tasks
parts
[1,2,3,4]
[1,4]
Is the project
small?
[1] [1]
Yes Prepare the
application
[1]
[2] [2] [4]
Verify progress
[2]
Send the Correct the
project to the Make
client project settlements
[1,2,3] [1,3,4]
[1,2] End of the
project
Figure 1. Configurable Business Process [5]
Case-based reasoning for workflow adaptation was discussed in [19]. It allows for
structural adaptations of workflow instances at build time or at run time, and supports
the designer in performing such adaptations by an automated method based on the adap-
tation episodes from the past.
The work presented in this paper is a continuation of our previous research pre-
sented in [5]. We use Bayesian networks (BN) for recommendation purposes. In this
approach a BN is created and learned based on a configurable business process. The
motivation for the current work was to evaluate the methods developed in previous re-
search. Therefore this paper focuses on the issues of matching Bayesian network to
business processes to allow probabilistic recommendation queries. The BN learning
was presented in our previous work and is beyond the scope of this paper. For the eval-
uation environment we decided to use Activity Modeller 4 , which is part of one of the
most widely used software bundle for designing and executing BPMN models.
In the following section we present a short overview of the recommendation method,
that uses BN for structural recommendations. It also describes an algorithm for mapping
BPMN process elements to random variables of Bayesian network.
3 Bayesian network based recommendations
Bayesian Network [20] is an acyclic graph that represents dependencies between ran-
dom variables and provide graphical representation of the probabilistic model. This
representation serves as the basis for compactly encoding a complex probability distri-
bution over a high-dimensional space [21].
In the subject of structural recommendations with BN approach, the random vari-
ables are BP structural elements (tasks, events, gates, etc). Connections between the
random variables are obtained from the configurable process, that captures similarities
between two or more BP models and encapsulates them within one meta-model. For
the configuration model example, see Figure 1.
The transformation from a configurable model to a BN model is straightforward.
Each node in a configurable process has to be modeled as a random variable in BN.
Therefore, each node in a configurable process is translated into a node in the net-
work. The flow that is modeled by configurable process represents dependencies be-
tween nodes. These dependencies also can be translated directly to the BN model (See
Figure 2).
The BN network obtained from the configurable model encodes just the structure
of the process. To allow querying the network for the recommendations it is necessary
to train it. The comprehensive list and comparison of them can be found in [22]. For
the purpose of this paper, we use the Expectation Maximization algorithm to perform
Bayesian network training. The software we used to model and train our network is
called Samiam5 . The training data was a configurable process serialized to a CSV file.
Each column in the file represents a node in configurable process, whereas each row
represents a separate process model that was used to create the former.
3.1 Querying the model for recommendations
We defined three different structural recommendation modes that include forward rec-
ommendations, backward recommendation and autocompletion [5]. So far we success-
fully implemented and evaluated forward recommendations that allows for automated
generation of suggestions for the next element of the designed BP. Although the back-
ward recommendations and autocompletion scenarios are not presented in the paper,
the overall algorithm remains the same for all three scenarios. The difference between
them lays on the implementation rather than conceptual level, and therefore they were
skipped for the sake of clarity and simplicity. In this section we describe details of the
aforementioned forward recommendation algorithm.
4
http://activiti.org
5
See: http://reasoning.cs.ucla.edu/samiam.
Figure 2. Bayesian Network representing the configurable process from Figure 1
Figure 3 describes a possible query for forward recommendation. The red circle
denotes observed states (so called evidence) that represents BPMN elements that were
already included by the designed in the model. In the case presented in the Figure 3,
the only observed evidence is a Start element. The remaining circles denotes possible
configuration of BPMN blocks with probabilities assigned to them. For instance proba-
bility, that the block Perform market analysis will be present in the model is 25%.
The forward recommendation algorithm will scan the Bayesian network starting
from the last observed block in a topological order, and return three blocks with the
probabilities of presence in the model greater than 50%. The most challenging task
in this algorithm was mapping the nodes from BPMN process to nodes in Bayesian
network. This was particularly difficult because the BPMN elements are identified by
the unique IDs that are different every time a new process is created.
Figure 3. Forward recommendations of the process for the model presented in Fig. 2
Therefore, we distinguished several possible paths for matching the BN model to
the process that is designed:
– graph-based metrics, that allows to compare structures of two networks and identify
areas that may correspond to the same elements [23],
– text-based metrics, that allows to compare elements based on their labels [24], and
– semantic-based comparisons, that provides more advanced matching based on the
elements labels, taking into consideration semantics of the labels [25].
Because the recommendation module should work in a real-time, we decided to
use the second approach which is more efficient comparing to graph-based approaches
and requires less implementation effort than the third option. This choice was motivated
also by the fact that BPMN elements usually have very informative labels and hence, the
text comparison should give good results. As the metric for comparing nodes labels, a
Levenshtein distance [26] was used. Mathematically, the Levenshtein distance between
two strings a, b is given by the equation 1, where where 1(ai 6=bj ) is the indicator function
equal to 0 when ai = bj and equal to 1 otherwise.
max(i,
j) if min(i, j) = 0,
leva,b (i − 1, j) + 1
leva,b (i, j) = (1)
min leva,b (i, j − 1) + 1 otherwise.
leva,b (i − 1, j − 1) + 1(ai 6=bj )
Such text-based matching performs well until two or more nodes have similar or the
same labels. For instance in Figure 1 there are four And nodes and two Make settlements
nodes. Hence, when the user puts the block with a label And, the recommendation
algorithm has to decide to which of the blocks in the Bayesian network it corresponds.
This is performed by the neighborhood scanning algorithm.
The algorithm performs a breadth-first search on the currently designed model, and
try to match the neighborhood of the node from BPMN diagram to the neighborhoods of
the ambiguous nodes in the Bayesian networks. The node from the Bayesian network
which neighborhood matches the most of the nodes from BPMN diagram is chosen.
For instance in the example from Figure 3, the user choses to include the And gateway
in the diagram. The recommendation algorithm has to decide which And node from
the Bayesian network presented in the Figure 2 should be treated as a reference point
for the next recommendation. Because in the BPMN diagram the neighborhood of the
And node is just one element called Start, the neighborhood scanning algorithm will
search in the BN for the And node with a Start element as a neighbor. If the ambiguity
cannot be resolved by the first level neighborhood scanning, the algorithm continues
the process in a breath-search manner.
The following section presents details of the implementation of the recommendation
module and presents brief evaluation of the approach.
4 Activity recommender
In [5] the process of transforming a configurable process into a Bayesian Network is
performed manually. In order to automate this process 3 auxiliary modules have been
implemented.
The first module is the converter, which creates a Bayesian Network file from a
BPMN file. The second module generates a training data file based on the information
about each block’s occurrence in each of the processes that the configurable process
is composed of. The third module takes the untrained Bayesian Network file and the
training data file as input and trains the network using the EM algorithm.
Figure 4. Architecture of the Activity recommender extension.
The output of this three modules is an input for the Activity recommender presented
in the following section.
4.1 Implementation
The architecture of the solution consists of four elements as depicted in Figure 4. These
elements are:
– Recommendation module – an element responsible for providing recommendations
based on the given BN model and evidences. It executes the recommendations
queries described in Section 3.
– SamIam inference library 6 – A library that allows for probabilistic reasoning that is
used by the recommendation module. The probabilities of occurrence of elements
in designed BPMN diagram are calculated by this element.
6
http://reasoning.cs.ucla.edu/samiam/
– Recommendation plugin – A user interface element, that presents the recommen-
dations to the designer and allows to query the recommendation module.
– Shape Menu plugin – A plugin that is a set of icons that surround a selected block
providing shortcuts for the most commonly used operations. In this case, a plugin
allows to insert the recommended element just after the selected one (see Figure 5).
The Recommendation module and SamIam inference library were encapsulated into
a webservice restlet to fit the Activity software architecture. Recommendation plugin
and Shape Menu plugin have been implemented as a frontend plugins for Activity
modeller. The communication between frontend and backend is based on the JSON
exchange format.
Figure 5. An example of a recommendation process in Activity modeller
To better visualize this process of recommendation performed by the Activity rec-
ommender, lets assume that the previously trained Bayesian network was deployed into
the Activity recommender system. When a designer queries the system for the recom-
mendation, all the BPMN elements labels that were already placed by the designer into
the model are treated as an evidence. The currently selected element is tarted as a ref-
erence element for which the forward recommendation should be performed. All the
evidence are packaged into the JSON format and sent to the backend, where the recom-
mendation module performs a query to the BN and returns the recommended elements
back to the frontend. In the frontend the recommendation plugin and shape menu plugin
present the recommendations to the designer and the process continues.
The following section describes a brief evaluation of the descried solution on the
simple use-case scenario.
4.2 Evaluation
The evaluation of the Activity recommender was performed on the simple model pre-
sented in Figure 2. The model was learnt from the configurable process presented in
Figure 1 with an auxiliary modules described briefly at the beginning of this section.
The Figure 5 presents the beginning of the design process, when only two elements
are inserted into the diagram: the Start of the project element and the And gateway. The
Bayesian network representing this state was depicted in the Figure 3. If the user selects
the node and presses the button depicted with a question mark icon that is located on
the top bar of the modeller, the recommendation query will be send to the recommenda-
tion module. The module will then perform forward recommendation starting from the
element that was selected by the designer (in this case the And gateway). The results of
the query are presented in the sidebar on the left side of the Activity modeller, and are
also accessible through shape menu plugin, which is activated when the user hover the
mouse over the element.
As presented in Figure 5, the recommendations are consistent with the probabilities
calculated from the Bayesian network presented in Figure 3. It is worth noting, that al-
though in the Bayesian network there exist four And gateways, the correct gateway was
chosen for the recommendation reference point, thanks to the neighborhood scanning
algorithm described in Section 3.
5 Summary and future work
In this paper we presented an implementation and evaluation of the structural recom-
mendation module for BPMN diagrams. We integrated one of the most popular BPMN
modeller called Ativity with our recommendation module providing practical tool for
structural recommendation of BP models. We also presented an approach that supports
matching the similar areas of two graphs that is based on the Levenshtein metric and
neighbor scanning algorithm. The evaluation was presented on a simple use-case sce-
nario that was part of the Prosecco project.
The future works assumes implementing remaining two recommendation modes
that are: backward recommendation and autocompletion. It is also considered to com-
pare the solution based on the Bayesian networks to the other approach that originates
from phrase prediction algorithms [27].
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