DISCOVERY AND ANALYSIS OF ACTIVITY PATTERN

CO-OCCURRENCES IN BUSINESS PROCESS MODELS

Jean Michel Lau, Cirano Iochpe

Informatics Institute, Federal University of Rio Grande do Sul, 9500 Bento Gonçalves Av., Porto Alegre, Brazil

Lucinéia Heloisa Thom, Manfred Reichert

Institute of Databases and Information Systems, University of Ulm, Germany

Keywords: Business process modeling, Workflow activity patterns, Knowledge discovery, Data mining, Reuse.

Abstract: Research on workflow activity patterns recently emerged in order to increase the reuse of recurring business

functions (e.g., notification, approval, and decision). One important aspect is to identify pattern co-

occurrences and to utilize respective information for creating modeling recommendations regarding the

most suited activity patterns to be combined with an already used one. Activity patterns as well as their co-

occurrences can be identified through the analysis of process models rather than event logs. Related to this

problem, this paper proposes a method for discovering and analyzing activity pattern co-occurrences in

business process models. Our results are used for developing a BPM tool which fosters the modeling of

business processes based on the reuse of activity patterns. Our tool includes an inference engine which

considers the patterns co-occurrences to give design time recommendations for pattern usage.

1 INTRODUCTION

Process-aware information systems (PAIS) prove to

be efficient tools for the design and automation of

business processes. Business process is a set of

(structured) activities which jointly perform a

particular business goal. Such activities are related

to specific business functions or process fragments

(e.g., notification, approval) having a well defined

semantics (Thom, 2006a, Thom, 2006b). In

particular, a certain process fragment or business

function (e.g., enabling document approval) may

occur several times within one or different process

models. That means multiple logical copies of the

same process fragment may be used with same or

different parameterization (e.g. approval by a single

actor or by multiple actors). In (Thom, 2009) such

fragments are represented as workflow activity

patterns: Request for Activity Execution without

Answer, Request for Activity Execution with Answer,

Approval, Notification, Decision-making and

Information Request.

An example of activity pattern is the

unidirectional performative pattern. This pattern

represents a unidirectional message as described in

(zur Muehlen, 2002). A sender uses unidirectional

performative messages to request the execution of a

particular activity from a receiver (e.g., human or

software agent) involved in the process. The sender

continues execution of his part of the process

immediately after having sent the request. The

complete set of activity patterns on which this work

is based is described in (Thom, 2006b, Thom, 2009).

Generally, multiple activity patterns can be

composed to a process model using workflow

patterns (e.g., Sequence, AND-Split, AND-Join,

XOR-Split). Through an empirical study, in which

214 real-world process models were analyzed, the

existence of seven activity patterns has been

confirmed (cf. Thom 2008a, Thom, 2008b, Thom,

2009). In that approach it was shown that the

analyzed process models can be completely

designed based on the aforementioned patterns; i.e.,

the set of identified activity patterns has been

necessary as well as sufficient to design the 214

process models, at least at a certain level of

granularity. This pattern set is closer to the

vocabulary abstraction level which business

processes are usually described by domain experts.

Though it is known that pattern utilization

Lau J., Iochpe C., Thom L. and Reichert M. (2009).

DISCOVERY AND ANALYSIS OF ACTIVITY PATTERN CO-OCCURRENCES IN BUSINESS PROCESS MODELS.

In Proceedings of the 11th International Conference on Enterprise Information Systems - Information Systems Analysis and Speciﬁcation, pages 83-88

DOI: 10.5220/0001958800830088

 SciTePress

improves the quality and performance of process

modeling, contemporary BPM tools like Intalio,

ARIS Toolset and WBI Modeler do not support

process designers in defining, querying and reusing

activity patterns as building blocks for business

process modelling (OASIS, 2006).

In this paper we present preliminary results

concerning the discovery and analyses of the co-

occurrences of the activity patterns in real-world

process models. Our goal is to use the results of this

analysis for developing a BPM tool, which fosters

the modeling of business processes based on the

reuse of activity patterns. The results of our analysis

can be further used by this tool to suggest a ranking

of the activity patterns best suited to succeed the last

applied pattern in a process design, facilitating the

modeling phase and leading to more standardized

and less error prone process models.

In order to perform the analysis we apply a

knowledge discovery on databases (KDD) process.

It is a process which involves theories and tools to

help humans to extract knowledge from large,

growing digital datasets. Its main purpose is to

identify valid patterns, potentially useful and

understandable from a set of data. In this context,

data constitute a set of facts; and patterns are

expressions in some language describing a subset of

the data or a model applicable to them. It can be split

into 5 main phases: 1) data selection; 2) data pre-

processing; 3) data transformation; 4) data mining,

and 5) result validation (Fayyad, 1996).

In particular, we implement a process model

mining tool (BPM mining tool for short) to be used

for identifying activity patterns co-occurrences. Our

miner allows analysing process models instead of

event logs as proposed in literature (Günther, 2008),

(Aalst, 2003). This can be considered as a very

important functionality to automatically identify

activity patterns co-occurrences in real-world

process models.

The remainder of this paper is organized as

follows: In section 2 we discuss related work.

Section 3 presents the data mining technique as well

as the algorithm used to perform the mining of the

activity patterns co-occurrences in real process

models. Section 4 applies the KDD process for

mining the process models. In this section we also

introduce the BPM mining tool we are developing

and some results generated by this tool when being

applied to real process models. Finally, Section 5

concludes the paper with a summary outlook.

2 RELATED WORK

Recently, a variety of workflow patterns was

suggested for capturing different aspects in PAISs

including control and data flow, resources, process

change, and exception handling (Aalst, 2003),

(Weber, 2007). However, there has not yet been a

mapping of activity patterns onto process (meta)

models and process modeling tools respectively.

Concerning workflow patterns, tool support is

provided by YAWL (Aalst, 2005), which uses

extended workflow nets as building blocks for

workflow specifications. Multiple extended

workflow nets involved in a workflow specification

can be connected to each other by composite tasks.

The PICTURE approach proposes a set of 37

domain specific process building blocks (Becker,

2007). More precisely, these building blocks are

used by end users in Public Administrations to

capture the process landscape and are also specific

to this domain). Finally, ProCycle presents an

approach implementing process change patterns in

ADEPT2 (Weber, 2009).

3 SELECTING A DATA MINING

METHOD AND ALGORITHM

If the goal of a KDD process is to predict the

behaviour of some variables, utilization of models,

which only describe or group the data, will not be

useful. Besides that the kind of learning, the task to

be done, the type of repository to be mined, and

knowledge representation must be considered during

the selection of the data mining technique (Brand,

1998), (Goebel, 1999), (Han, 2001). Table 1

presents these aspects and respective values which

were considered in the selection of the data mining

algorithms used in our approach.

Table 1: Aspects which were considered in the selection of

the data mining method.

Data mining aspect Value

Model Type Predictive

Learning Type Unsupervised/Batch

Task to be done Association Analysis

Repository to be mined Process models database

Knowledge Representation White box

Based on Table 1 we have studied the algorithms

for association rules discovery. This technique is

best suited for association analysis and is a

predictive one. This choice was made, because what

we are looking for is a future prediction based on

already occurred facts. In our case, the occurred

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facts are represented by process models partially

designed, while the goal is to discover (or to predict)

the next activity pattern to be used in the modeling

of these process models.

The technique is also based on

unsupervised/batch learning, i.e., it allows to prepare

a set of processes in a file and send this file to the

data mining algorithm in a batch mode.

Besides that, the white box concept guarantees

that the results when applying this technique are

easily understandable by humans so that we can

make some manual inferences with the mined

information (Agrawal, 1993).

Given a set of transactions, where each

transaction is a set of items, an association rule is an

expression A (antecedent) => C (consequent), where

A and C are sets of items. The intuitive meaning of

such a rule is that transactions in the database, which

contain the items in A, tend to contain the items in C

as well. (Agrawal, 1993).

The main advantages of association rules that

motivate their use in our approach are as follows: (a)

they are easily understood by humans; (b) they are

used to represent empirical associations; (c) through

special measures (support and confidence) it

becomes possible to evidence how useful mining

results are (Han, 2001).

A workflow process model can be understood as

a graph with a predefined semantics. In this context,

patterns are identified in recurrent subgraphs within

a set of graphs (e.g., a set of processes models).

Given a set of graphs, a graph mining algorithm

searches for substructures which satisfy some

criteria (e.g., minimum frequency of patterns and

minimum confidence). Based on recurrent

substructures, association rules can be derived. In

this context, we have selected the Frequent

SubGraph (FSG) algorithm to be used in our

approach (Kuramochi, 2004). We choose FSG due

to its good performance results, scalability, good

documentation and simple and well defined I/O.

4 APPLYING KDD FOR MINING

PROCESS MODELS

In the data selection step of our KDD process we

analyzed 190 process models. Most analyzed models

had been created with the Oracle tool or an UML

based process modeling tool. Altogether, the

considered process models stem from 12 different

organizations and are related to different application

domains. Note that the mining is performed

considering the process models and not their

execution logs as done in other approaches (see

(Tristão, 2008), (Aalst, 2003)).

BP – Bi-directional

NP – Notification

UP – Unidirectional

Figure 1: Example of activity patterns identification.

To mine process models, first of all we need to

transform the model such that the mining algorithm

can extract the needed information. In this pre-

processing phase the 190 process models were

analyzed and the activity patterns they use were

identified. Each activity or partial order of activities

was labelled as activity pattern.

t # Example – 1 – Approval process for a marketing

campaign of a new product

v 0 S

v 1 N

v 2 B

v 3 N

v 4 U

v 5 E0

u 0 1 N

u 1 2 N

u 2 3 N

u 3 4 N

u 4 5 N

t –transaction

v – graph node (activity pattern)

u –edge

S – start node

N – notification

B – bi-direcional pattern

U – unidirectional pattern

E0 – end node

# - comment sign

t # Example – 1 – Approval process for a marketing

campaign of a new product

v 0 S

v 1 N

v 2 B

v 3 N

v 4 U

v 5 E0

u 0 1 N

u 1 2 N

u 2 3 N

u 3 4 N

u 4 5 N

t –transaction

v – graph node (activity pattern)

u –edge

S – start node

N – notification

B – bi-direcional pattern

U – unidirectional pattern

E0 – end node

# - comment sign

Figure 2: Example of process transformation.

In the data transformation phase we mapped

each process model together with the identified

activity patterns, to the format required by the FSG

algorithm (cf. Section 3). For example, the process

model shown in Fig. 1 was transformed to the

format depicted in Fig. 2. Each node has an

incremental index and a label which indicates the

activity pattern this node represents. Edges refer to

the nodes they connect and have a label. All edges

have the N (Normal) label since we are not making

any differentiation between them.

In the mining phase the FSG algorithm is

executed. As a result of the mining, the frequent sub-

graphs in the analyzed process models are identified

as well as the support value of each sub-graph within

the set of process models and their parent-child

relation. This information is stored in a file similar

to the input file, since the frequent patterns are

DISCOVERY AND ANALYSIS OF ACTIVITY PATTERN CO-OCCURRENCES IN BUSINESS PROCESS MODELS

graphs as the input process models are.

4.1 On a Process Model Mining Tool

In order to analyze the results from the mining phase

we developed a BPM mining tool which allows to

automatically execute specific parts of the data

mining and the evaluation steps of the KDD process.

The tool receives a set of normalized activity

patterns as input and then simulates their

construction with the use of the mined co-

occurrences patterns. In this context co-occurrence is

a recurrent construction of activity patterns within

process models (e.g., the pattern pair D

ECISION Æ

NOTIFICATION).

To evaluate the usefulness of the activity patterns

co-occurrence, the tool simulates a user modeling

the input processes. In this modeling all processes

are re-constructed activity by activity. For each

activity insertion, the tool generates a

recommendation list. This list is composed by rules

describing how the process can evolve according to

the partial model designed so far and the co-

occurrences found on the data mining phase. These

rules have on their antecedent and consequent parts

of process models. Fig. 3 shows two examples of

rules (see item b).

As the tool receives as input the complete

process, it knows all intermediary stages of the

modeling phase as well as how process elements

must be added to completely design it. The order in

which elements are added to the process is obtained

from the order they appear in the input file.

Therefore, the tool knows what would be the user’s

choice on the recommendation list if he was

modeling the process with the help of it since the

order of elements insertion is specified. For instance,

the process shown in Fig. 2 could be part of an input

for the BPM mining tool. With that description the

tool simulates a user which models that process by

first adding an edge between the start node and the

notification pattern. Then, it adds an edge

connecting this notification pattern to a bi-

directional performative one. After that, the bi-

directional pattern is connected to other notification

pattern, which is then connected to a unidirectional

performative pattern, which is finally connected to

an end node.

The output of the BPM mining tool is a summary

containing calculations regarding the simulation of

the input models designed. It can be used to analyze

how useful the information on the co-occurrence of

activity patterns can be to predict and thus to foster

the design of process models. Sample outputs are:

 the total number of co-occurrence rules found

in the data mining phase;

 the number of steps of user’s rule selection

simulated, i.e., number of times that a

recommendation list was created and the

appropriate rule was searched on it;

 a ranking with the total number of steps where

the rule on position n th of the recommendation

list would be chosen by the user, i.e., the total

times that the rule on first (and second, …, n th)

position of the recommendation list would be

selected by a user when modeling the process;

 the total number of rules of size i that were

chosen in the simulation (in this analysis the size

of a rule is the number of edges that its

antecedent model has).

4.2 Implementation of a Process Model

Mining Tool

Based on the recurrent activity patterns found in the

mining phase, our BPM mining tool creates all rules

of interest with their respective support and

confidence. In future, we intend to implement some

filters within this class, or in a new class called by,

in order to filter the created rules according to

specific criteria. This filtering contributes to avoid

the creation of too many rules and improves the

performance of the algorithm. Currently, the used

filtering creates only one step rules. Item b of Fig. 3

shows two rules of this type. These rules have a

partial process model as antecedent and have on the

consequent the same process model with one more

edge and eventually a node. It is assumed that each

process model may evolve only by adding new edge

in each design step. When adding a new edge we

have two situations: a) the new edge closes a cycle

on the process, so that no new nodes are added; or b)

the new edge connects a node of the partial model

designed to a new activity pattern (node). In case of

b a new node is added to the model as well. By

doing so, we assure that all created rules represent

an evolution of the model of one step. Note that this

approach is easier for a user to understand when

comparing to rules which may present greater

modifications of the partial model designed so far. If

we allow to create rules which introduce greater

changes of the process they will hardly match all

user intentions. Besides that, this is not our purpose,

i.e., we do not want to present an almost complete

process to the user. Instead, we want to interactively

help on each pattern insertion to the process model,

so the rules are kept simple and easily readable.

Our tool also simulates the design of the input

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process models. It has an inference engine which

receives a partially modelled process as input and

then tries to match the complete process, or part of

it, with the antecedent of the rules as created in the

data mining phase. All matching rules are returned

in the ranking list of activity pattern co-occurrences,

so they represent a possible evolution for the design

of the partial model. Currently, the rules are ordered

by their size and confidence. Thus, rules which

match the largest part of the partial model are

presented first. If the rules have same size, the one

presenting the greatest confidence will be shown

first.

Fig. 3 shows a fragment of the process model we

introduced in Fig. 1, (see a). Fig. 3 also presents a

recommendation list with two rules to continue the

modeling of the process from the model fragment

designed so far (see b). The partial process model is

considered to construct this recommendation list. It

contains all rules that match entirely or partially the

model presented in a.

Figure 3: Modeling evolution.

Based on the complete process model (cf. Fig.

1), our tool knows that, in this case, if the user is

assisted by the recommendation list when designing

the process, he will choose rule number two, since it

represents an evolution of the partial model by

adding a new bi-directional performative pattern

connected to the already existing notification

pattern.

4.3 Evaluating the Discovered Patterns

To evaluate the patterns discovered with the KDD

process we have done a randomly stratified

partitioning of the available processes. We reserved

1/3 to pass to the Tester class once the co-

occurrences patterns are mined over the other 2/3 of

the processes. It was considered a minimum support

of 3, i.e., if three process models contain the same

co-occurrence of activity patterns it will be, then,

considered a pattern in our data mining step. Doing

so we assure that the co-occurrences found during

data mining step are not obtained from processes we

use to verify our approach. Thus, we can check

whether they are predictive enough for ‘never seen’

process models.

In our experiment we had observed that the 470

steps of rule selection were simulated, i.e., 470 times

a co-occurrence ranking of activity pattern was

generated and the appropriate rule which describes

the model evolution was searched. This ranking has

an average size of 26 rules. We can observe that on

almost 20% of the simulated steps, the rule which

correctly predicts the evolution of the model is on

the first three positions of the ranking. Accumulating

the values until the 10

position we have a correct

prediction in more than 35% of the simulated steps.

Considering that we have 6 activity patterns, the

probability of correctly predict the next activity

pattern, without additional information, is around

16.67%. Further, we need to predict where this new

activity pattern will be inserted. For instance, in a

model with 2 nodes, this probability decreases to the

half. In a model with 10 nodes, this probability is ten

times lower.

We can also see that the majority of the rules that

correctly predicts the model evolution are rules of

size 1, 2 or 3. This conclusion can further be used to

filter the generated rules to return rules of only these

sizes and reduce the amount of rules on the ranking.

We expect that this filter will improve the prediction

rate of the inference engine.

After evaluating the mined patterns we have re-

executed the process using all the available process

models. At this time, we used the complete set of

models to execute the data mining step and then

simulated their construction. 1425 simulation steps

were executed. On the first three positions of the

ranking we had a correct prediction rate of more

than 30% while on the 10

first positions this rate

goes to more than 50%. The percentage of

unmatched rules has decreased from 41% on before

execution to 20.5%. Considering the average size of

11 nodes in the processes models analysed, we have

around 1.52% of chances to correctly predict the

next pattern to be inserted in a model and where to

insert it if no prior information is used. With three

chances, i.e., creating a list of three possible next

patterns, we have 4.55% of chances to get a match.

Increasing from 4.55% to 30% is a great gain and

this illustrates the power of using mined information

to predict the future on our experiments.

Notification

Pattern (NP)

NP NP

(a)

Bi-directional

DISCOVERY AND ANALYSIS OF ACTIVITY PATTERN CO-OCCURRENCES IN BUSINESS PROCESS MODELS

5 SUMMARY

This paper reports on the use of KDD in the

development of a BPM mining tool, which allows

mining process models based on activity patterns as

highly relevant. The functionalities of this tool can

be considered very important: a) after having

identified the activity patterns in the process models

the tool can count the recurrences of each pattern as

well as their co-occurrences; b) the inference engine

of our BPM mining tool can give design time

recommendations for any new processes being

modelled, which ease process modelling based on

already mined information; c) we can use our tool

for conducting a series of experiments in which we

compare process modeling with and without activity

pattern support as well as investigate different

process classes and their most recurrent co-

occurrences and; d) finally, the basic concepts

behind this tool (e.g., the inference engine) can be

added as extensions to existing BPM tools.

As future work, we aim at extending our tool

with a module to update the frequency of activity

patterns co-occurrences and corresponding raking of

recommendations based on the user modeling. This

update will be done on-the-fly as new models are

developed aided by the inference engine. Thus, we

aim at increasing the accuracy of the

recommendations for each pattern co-occurrence.

In addition we intend to investigate methods

which allow the automatic identification of activity

patterns in real process models.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the

Coordination for the Improvement of Graduated

students (CAPES), the DBIS of the Ulm University.

(Germany), and the Informatics Institute of the

UFRGS (Brazil).

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