Process and Organizational Data Integration from BPMS and

Relational/NoSQL Sources for Process Mining

Andrea Delgado and Daniel Calegari

Instituto de Computaci

on, Facultad de Ingenier

ıa, Universidad de la Rep

ublica, Montevideo, 11300, Uruguay

ﬁ

Keywords:

Process Mining, Data Science, Process and Organizational Data Integration, Process Improvement.

Abstract:

Business Process execution analysis is crucial for organizations to evaluate and improve them. Process mining

provides the means to do so, but several challenges arise when dealing with data extraction and integration.

Most scenarios consider implicit processes in support systems, with the process and organizational data being

analyzed separately. Nowadays, many organizations increasingly integrate process-oriented support systems,

such as BPMS, where process data execution is registered within the process engine database and organiza-

tional data in distributed potentially heterogeneous databases. They can follow the relational model or NoSQL

ones, and organizational data can come from different systems, services, social media, or several other sources.

Then, process and organizational data must be integrated to be used as input for process mining tasks and pro-

vide a complete view of the operation to detect and make improvements. In this paper, we extend previous

work to support the collection of process and organizational data from heterogeneous sources, the integration

of these data, and the automated generation of XES event logs to be used as input for process mining.

1 INTRODUCTION

Business Process Management (BPM) (van der Aalst,

2013; Dumas et al., 2018; Weske, 2019) focuses on

business processes in organizations, covering their

lifecycle from modeling, conﬁguration, execution and

evaluation, to support their continuous improvement.

Data science (van der Aalst, 2016; IEEE, 2020) has

emerged in recent years as an interdisciplinary disci-

pline to deal with the management, analysis, and dis-

covery of information in large volumes of data that are

generated at high speed (velocity), with great variety,

and also considering its veracity (the three V) (Furht

and Villanustre, 2016), which is stored in structured

or unstructured forms.

Process Mining (van der Aalst, 2016) is a disci-

pline within Process Science (van der Aalst, 2016),

and Data Science (van der Aalst, 2016; IEEE, 2020)

that can be seen as a bridge between those areas, and

has been developed in the last two decades to pro-

vide techniques, algorithms, and tools to discover in-

formation from process execution data. These data

are registered within traditional organization’s infor-

mation systems (IS) or BPM systems (BPMS), where

events that occur within each process instance (case)

are registered in a so-called event log. Process mining

provides three main approaches (van der Aalst, 2016):

i) discovering processes from event logs, i.e., generat-

ing process models based on execution process data;

ii) process conformance, i.e., checking the actual ex-

ecution in event logs against existing or discovered

BP models; and iii) enhancing BP models with other

information such as roles and resources involved.

Organizations face several challenges regarding

their daily operation and technical support infras-

tructure and the large amount of data they con-

tinuously gather from different and heterogeneous

sources. These sources include relational and NoSQL

databases nowadays, distributed within the organiza-

tion or several organizations working together, tradi-

tional IS with implicit business processes, and BPMS

with explicit business processes. A key challenge is to

seize all this data and get information and value from

it to improve their business. It involves collecting, in-

tegrating, and processing process and organizational

data in an integrated manner to get a complete picture

of their processes and organizational data.

In previous works (Delgado and Calegari, 2020;

Calegari et al., 2021) we have presented the problem

of dealing with the compartmentalized vision of pro-

cesses on the one hand and organizational data on

the other. We introduced a model-driven proposal

for data integration using an integrated metamodel in

which data is collected, and a matching algorithm re-

Delgado, A. and Calegari, D.

Process and Organizational Data Integration from BPMS and Relational/NoSQL Sources for Process Mining.

DOI: 10.5220/0011322500003266

In Proceedings of the 17th International Conference on Software Technologies (ICSOFT 2022), pages 557-566

ISBN: 978-989-758-588-3; ISSN: 2184-2833

557

lates it. This integrated data is transformed from the

metamodel to many targets, e.g., an event log for pro-

cess mining. In (Delgado et al., 2021), we envision a

generic Application Programming Interface (API) to

collect data from any BPMS, which was initially pro-

posed and implemented in a previous work (Delgado

et al., 2016).

A signiﬁcant drawback that presents the approach

is the high coupling of the ETL process to the data

model type and the speciﬁc implementation of the

data source. Within the whole proposal, we envision

a generic API to collect data from any database, both

relational or NoSQL, but it was not previously deﬁned

in detail.

This paper presents an extension of the former

proposal with the joint deﬁnition of both APIs that

allow us to collect process and organizational data

from heterogeneous sources. In particular, the generic

API for organizational data will enable us to decou-

ple the ETL process both from the data model type

and the speciﬁc database implementation. The ex-

tension includes changes in the metamodel to adapt

the data concepts to other data models and changes in

the model-driven approach to automatically generate

an extended event log that contains the correspond-

ing organizational data for each event (activity) of the

process.

The rest of the article is organized as follows: In

Section 2 we introduce key concepts related to the

main elements included in our proposal. In Section 3

we discuss related work. In Section 4 we describe our

proposal, including the deﬁnition of the process and

organizational data integration with a generic API, as

well as preliminary results. In Section 5 we provide

some examples of applications. Finally, in Section 6

we present some conclusions and future work.

2 BACKGROUND

Process execution data is registered from IS or BPMS

from the organization’s daily operation, based on im-

plicit or explicit process models that depend on the

existing settings. Most data are mainly scattered in

several heterogeneous databases, which can be rela-

tional or NoSQL, with no explicit relationship be-

tween the execution of the process and the associated

organizational data it manages. This compartmental-

ized vision of processes on the one hand and organi-

zational data on the other is not adequate to provide

the organization with the evidence-based business in-

telligence necessary to improve their daily operation.

A typical scenario in an organization regarding the

ecosystem of systems and infrastructure needed to

support its daily operation is depicted in Figure 1.

As it can be seen in Figure 1, process and organi-

zational data can come from heterogeneous sources,

such as IS, BPMS, services, Internet of Things

(IoT) settings, social media interacting with IS or

BPMS, which are registered in distributed (proba-

bly not linked) databases, such as the process engine

database, or several organizational databases both re-

lational and NoSQL. This scenario can be extended

to inter-organizational collaborative processes involv-

ing several organizations, where apart from these set-

tings, organizations interact using interchanging mes-

sages that also contain data, which is also registered.

In (Calegari et al., 2021) we discussed two main sce-

narios for inter-organizational collaborative BPs, one

that involves direct interaction between participants

and the other with interaction via an Interoperability

Platform (InP) which registers all interactions. Inter-

organizational collaborative processes add even more

complexity to the data integration problem.

In previous works (Delgado and Calegari, 2020;

Calegari et al., 2021) we have presented the problem

of dealing with the compartmentalized vision of pro-

cesses on the one hand and organizational data on the

other. We introduced a model-driven proposal for data

integration using an integrated metamodel in which

data is collected and a matching algorithm to recon-

struct the relationships between them. We have also

deﬁned an extension of the eXtensible Event Stream

(XES) (IEEE, 2016) format, called an extended event

log. An XES log represents events grouped in traces

(cases) for a given process, and it is used as stan-

dard input for process mining. It provides an exten-

sion mechanism for deﬁning new attributes to events.

We include the associated data entities and attributes

as deﬁned by the matching algorithm for each event

within the extended event log.

3 RELATED WORK

The idea of connecting databases with process data

was mainly focused on the perspective of process

mining and not on the exploitation of both sources

of information altogether, i.e., process data and or-

ganizational data. Examples of this are the work in

(Claes and Poels, 2014) in which the authors analyze

the exploitation of database events as a source of in-

formation for event logs. Also, in (Berti and van der

Aalst, 2020), the authors propose building multiple

viewpoint models from databases, providing a holis-

tic view of a process.

Nevertheless, some works are tackling the integra-

tion of both sources of information. In (de Murillas

ICSOFT 2022 - 17th International Conference on Software Technologies

558

Figure 1: Linking process and organizational data from heterogeneus sources extended from (Delgado and Calegari, 2020).

et al., 2019), the authors propose a comprehensive in-

tegration of process and data information in a con-

sistent and uniﬁed format through the deﬁnition of a

metamodel. This work has some aspects in common

with our proposal in (Delgado and Calegari, 2020).

Moreover, in (Tsoury et al., 2018), the authors de-

ﬁne a conceptual framework for a deep exploration

of process behavior by combining information from

the event log, the database, and the transaction (redo)

log. Complimentary to these ideas, in (Radesch

utz

et al., 2008; Radesch

utz et al., 2015) the authors de-

scribe concrete matching techniques between process

data and operational data. As far as we know, none of

these works considered the existence of a generic API

for BPMS (or databases) as we deﬁned and discussed

in (Delgado et al., 2016).

4 DATA INTEGRATION

PROPOSAL

Figure 2 presents the complete integration approach

we have deﬁned. In Figure 2a the Extraction Transfor-

mation Load (ETL) process, to collect process and or-

ganizational data from heterogeneous sources. Most

process mining approaches deal with implicit pro-

cesses registered along with the organizational data

within the execution of traditional IS in one or sev-

eral distributed databases. On the contrary, we as-

sume that processes are explicitly deﬁned and exe-

cuted within a BPMS, where process data are auto-

matically registered in the process engine database

(mostly relational, but recently there are initial imple-

mentations for NoSQL databases such as MongoDB),

and organizational data are registered in a different

database (which can be relational or NoSQL) within

the organization, where other systems also insert and

access organizational data.

In our initial proposal (Delgado and Calegari,

2020; Calegari et al., 2021) we have deﬁned a meta-

model in which process and organizational data col-

lected from those heterogeneous sources are stored

and integrated with a matching algorithm. The meta-

model is divided horizontally into an upper and lower

part, where the upper part corresponds to process

data, and the lower one corresponds to organizational

data. It is also divided vertically into a left and a right

part. The left part corresponds to process and organi-

zational deﬁnition data, and the right corresponds to

process and organizational instances data. We have

presented a prototype implemented in Activiti BPMS

with a PostgreSQL database, and the organizational

data is also registered in a PostgreSQL database. We

directly extracted the corresponding data from both

databases and loaded it into the metamodel, running

the matching algorithm to connect the process and or-

ganizational data elements.

Although the approach is general for any BPMS

and any organizational database, we realized that a

signiﬁcant drawback of our proposal was coupling the

data extraction to a speciﬁc type of data model and a

particular implementation of a database. For example,

the relational model and PostgreSQL, since chang-

ing the data model or the database implementation re-

quires a new ETL implementation to extract process

and organizational data to the metamodel. Therefore,

Process and Organizational Data Integration from BPMS and Relational/NoSQL Sources for Process Mining

559

(a) Mechanism for the ETL process and organizational data collection extended from (Delgado et al., 2021).

(b) Model-driven approach for generating the extended event log for process mining.

Figure 2: Complete approach for collection, integration and process mining of process and organizational integrated data.

in (Delgado et al., 2021) we have envisioned an ex-

tension in which a generic API is deﬁned and used

for extracting process data from BPMS. Another API

is deﬁned and used for extracting organizational data

from heterogeneous databases. To include such exten-

sion for heterogeneous data models, we also extended

the metamodel to better reﬂect different approaches.

We present here such extension, for which we in-

tegrated and extended a previously deﬁned generic

API for BPMS (Delgado et al., 2016), that we used to

propose a generic user portal for BPMS that can work

with different BPMS as backend. This generic API

operates over a generic data model for process execu-

tion that includes several concepts and relations that

are also present in the integrated metamodel, i.e., pro-

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560

Table 1: Example of the generic API for BPMS excerpt from (Delgado et al., 2016).

Process category

GetProcessDefinitions(string name, string category, bool active):Enumerable<Process>

Returns a list with existing process deﬁnition by name, category and state.

GetProcessDefinition(int processId):Process

Returns the deﬁnition of the selected process.

SuspendProcess(int processId):void

Suspends process with processId so no cases can be generated from it.

Cases category

CreateCase(int processId):void

Allows the creation of a case from the process with id processId.

GetAllCases(string name, string creator, string user, string state):IEnumerable<Case>

Returns a list of cases ﬁltered by process name, assignee, creator or in some state.

AddCommentToCase(int caseId, string userId, string comment):void

Allows to add a comment to a case by the selected user.

Tasks category

GetTasks(string name, bool assignee, string candidateUser):IEnumerable<TaskInstance>

Returns a list of tasks by name, assigned or not, to be taken by the user.

TakeTask(string taskId):bool

Assigns the task to the user performing the invocation.

GetTaskVariables(string taskId):IEnumerable<VariableInstance>

Returns the list of variables associated with the selected task.

Table 2: Example of the generic API for heterogeneous databases.

Entity category

List <String>GetEntityNames()

Returns a list with the names of existing entities.

List <Entity>GetEntityDefinitions()

Returns a list of Entities, including their attributes.

Entity GetEntityDefinition(string entityName)

Returns the Entity selected by name.

List<EntityInstance> GetEntityInstances(string entityName)

Returns all instances of an Entity selected by name.

cess deﬁnition and instance, element deﬁnition and

instance (activity, tasks), role, user. The operations

deﬁned in the API allow gathering information from

the BPMS by deﬁning a speciﬁc wrapper for each

BPMS. The generic operations are translated to spe-

ciﬁc operations provided by the BPMS to obtain pro-

cess instances, activities, users, etc. In Table 1 we

present an example of the generic API for BPMS ex-

cerpt from (Delgado et al., 2016).

The generic API for organizational data has been

deﬁned from scratch through analyzing the data mod-

els that different NoSQL approaches deﬁned, such

as document, graphs, key-value and column-oriented,

and the relational model, which was the ﬁrst one con-

sidered in the proposal. In Table 2 we present an ex-

ample of the generic API for organizational data for

the Entity category. Based on our analysis, we have

also extended the metamodel to reﬂect the entities bet-

ter, relationships and attributes involved. We have in-

cluded a few changes to the original modeling, in the

ﬁrst place, in the relation between entities modeling,

which is now explicitly modeled by adding the no-

tion of Relation as a concept, allowing attributes in

a relation (particularly for graph databases). A Re-

lation connects one (self-relation) or two entities and

can also have attributes. Secondly, we have added at-

tributes Source and SourceType in Entity and Process

to allow traceability for the ETL process. A source

can be the database connection string plus the loca-

tion of the generic connector or other data to identify

the origin of the data. SourceType is, for the Entity

element, the type of data model, i.e., relational, doc-

ument, graph, etc., and, for Process, the process type,

i.e., BPMN, CMMN.

In Figure 2b we present the second part of our

data integration approach. Once the metamodel is

loaded with the process and organizational data, we

can run the matching algorithm to reconstruct the re-

Process and Organizational Data Integration from BPMS and Relational/NoSQL Sources for Process Mining

561

Table 3: Mapping from the integrated metamodel to the extended XES event log.

Meta-model

XES extended

Generated label Included in

Process

Role <string key=”org:role” value=””/ > <event ></event >

Element

Deﬁnition

Variable

Deﬁnition

Case

User <string key=”org:resource” value=””/ > <event ></event >

Element

Instance

Variable

Instance

Entity

Attribute

Entity

Instance

Attribute

Instance

lations between data from process and organizational

databases. From the integrated metamodel, we have

deﬁned a model-driven approach for generating the

extended XES event log, which is then used as in-

put in the ProM framework for process mining tasks.

To support the automated generation of the extended

event log, we have deﬁned mappings between the

metamodel concepts and the XES format tags that

should be generated. In Table 3 we present the deﬁned

mappings. It is worth noting that once the metamodel

is loaded with the process and organizational data for

the selected process, through the ETL using the de-

ﬁned APIs for BPMS and organizational data, and the

data is integrated using the matching algorithm, the

generation of the extended XES event log from the

metamodel is the same for all cases. It does not mat-

ter from which sources or type of sources (i.e., which

BPMS, or which relational or NoSQL database) the

data was obtained since it is expressed using concepts

and relationships within the metamodel.

As shown in Figure 2b, the extended event log can

be used for process discovery as input for existing al-

gorithms. These algorithms are implemented in dif-

ferent plug-ins since the organizational data is treated

as other attributes of the events and ignored for con-

trol ﬂow discovery. It can also be used for process ex-

ecution analysis based on traditional attributes such as

timestamps for performance evaluation, as throughput

time and bottlenecks. Finally, it can be used as input

for a plug-in of our own (under development) to apply

process mining and data mining in an integrated way.

It allows a complete analysis of data by crossing pro-

cess and organizational data for speciﬁc views (e.g.,

clustering, association rules) on data managed by dif-

ferent types of cases (e.g., variants) or types of cases

that lead to different data results.

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(a) Students Mobility BP from (Delgado and Calegari, 2020).

(b) Extended data model for the Students Mobility BP.

Figure 3: Students Mobility BP and data model from (Delgado et al., 2021).

5 EXAMPLE OF APPLICATION

We have implemented several prototypes using dif-

ferent technologies and deﬁning different settings that

allowed us to probe the feasibility of our proposal us-

ing the same BP and organizational data. The process

we used corresponds to our university’s real process,

the “Student mobility” BP, where scholarships are of-

fered to students from different interchange programs

and applications are evaluated. Some are selected as

holders, and other remains as the alternate. The orga-

nizational data model includes students, applications,

programs, and courses. We have essay three proto-

type implementations, with settings combining differ-

ent BPMS, different process databases, and different

organizational databases. In Figure 3 we present the

deﬁnition of the “Student Mobility” BP and its cor-

responding organizational data model from (Delgado

and Calegari, 2020).

The prototype was implemented using Activiti

BPMS with a PostgreSQL database for the process

engine database and a PostgreSQL database for the

organizational data. We implemented the ETL pro-

cess directly from the databases to the metamodel.

We also implemented the matching algorithm over the

deﬁned metamodel. It was presented and discussed in

Process and Organizational Data Integration from BPMS and Relational/NoSQL Sources for Process Mining

563

(a) Historic tasks as collection of documents. (b) Historic variables as collection of documents.

Figure 4: Example of Flowable BPMS with Mongodb.

Figure 5: Example of graph modeling for the Mobility organizational data in Neo4j.

(Delgado and Calegari, 2020; Calegari et al., 2021).

The second prototype uses Flowable BPMS in-

tegrated with a MongoDB (document) database

as the process engine database and a Post-

greSQL database for the organizational data.

MongoDB deﬁnes a collection of documents

for each table in the process engine, e.g.,

activityInstances, historicTaskInstances,

historicActivityInstances, variables,

historicVariableInstances, etc. Each doc-

ument is equivalent to a row in the table, and each

ﬁeld in the document is equivalent to a column.

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564

Listing 1: Load algorithm using the generic API for organizational data.

db = ge t T ar g e tD B ( )

L i s t <E n t i t y > e n t i t i e s = G e t E n t i t y D e f i n i t i o n s ( )

f o r e a c h e n t i t y i n e n t i t i e s

db . e n t i t y . i n s e r t ( e n t i t y )

f o r e a c h a t t r i b u t e i n e n t i t y . A t t r i b u t e s

db . a t t r i b u t e . i n s e r t ( a t t r i b u t e )

e n d f o r

f o r e a c h t a r g e t E n t i t y i n e n t i t y . R e l a t i o n s h i p s

db . e n t i t y E n t i t y . i n s e r t ( e n t i t y . name , t a r g e t E n t i t y )

e n d f o r

L i s t <E n t i t y I n s t a n c e > i n s t a n c e s = G e t E n t i t y I n s t a n c e s ( e n t i t y . name )

f o r e a c h i n s t a n c e i n i n s t a n c e s

db . e n t i t y I n s t a n c e . i n s e r t ( i n s t a n c e )

f o r e a c h a t t r i b u t e I n s t a n c e i n i n s t a n c e . a t t r i b u t e s

db . a t t r i b u t e I n s t a n c e . i n s e r t ( a t t r i b u t e I n s t a n c e )

e n d f o r

f o r e a c h r e l a t i o n s h i p I n s t a n c e i n i n s t a n c e . R e l a t i o n s h i p s I n s t a n c e s

f o r e a c h t a r g e t I n s t a n c e I d i n r e l a t i o n s h i p s I n s t a n c e . T a r g e t I n s t a n c e s

db . e n t i t y I n s t a n c e E n t i t y I n s t a n c e . i n s e r t ( i n s t a n c e . Id ,

t a r g e t I n s t a n c e I d )

e n d f o r

Figure 4 shows the document collections for historic

task instances and variables instances for the “Stu-

dent Mobility” implemented in Flowable BPMS with

MongoDB.

To collect the process data from Flowable, the

generic API for BPMS is used, with an adapter that

implements the invocation of the corresponding op-

erations from the Flowable REST API in the same

manner that we did in previous work (Delgado et al.,

2016). To collect the organizational data from the

PostgreSQL database, the generic API for organiza-

tional data is used in the same way, with the same

select statements executed directly in the prototype.

The third prototype uses Camunda with a Post-

greSQL database for the process engine and differ-

ent NoSQL databases for organizational data: Mon-

goDB and Neo4j (graph database). As before, Mon-

goDB stores the organizational data model (Mo-

bility database) as a collection of documents, and

each document with corresponding ﬁelds, for tables

such as Student, Application, and Program. The

Neo4j database stores rows as nodes with properties

for columns and labels for tables; foreign keys and

join tables correspond to relationships (edges) in the

graph. The Mobility database is then transformed

into nodes with labels Student, Application,

and Program, and relationships PRESENTS between

Student and Application, and CORRESPONDS TO

between Application and Program. Figure 5 de-

picts an example of the Mobility database in Neo4j.

As before, the generic API for BPMS is used to

collect process data from Camunda, with an adapter

that invokes the Camunda REST API in the same

manner that we did in previous work (Delgado et al.,

2016). The organizational data is then collected us-

ing the generic API for organizational data, deﬁning

for each NoSQL database the corresponding adapters

with speciﬁc queries to gather it. The ETL process

works directly against the generic API for BPMS to

load the process deﬁnition and process instance quad-

rant of the metamodel and against the generic API for

organizational data to load the data deﬁnition and in-

stance quadrant of the metamodel. Listing 1 presents

the load algorithm for the organizational data quad-

rants using the operations deﬁned in the generic API

for organizational data.

Regarding the automated generation of the ex-

tended XES event log from the integrated metamodel,

we have deﬁned an M2T transformation using Ac-

celeo

, based on the mappings we presented in Sec-

tion 4. The logs generated from the metamodel

were successfully imported into both ProM and Disco

tools. It conﬁrms that the complete chain from col-

lecting the data to loading it in the metamodel, inte-

grating it using the matching algorithm, and generat-

ing the extended XES event log is feasible and useful

for mining activities.

Acceleo: https://www.eclipse.org/acceleo/

Process and Organizational Data Integration from BPMS and Relational/NoSQL Sources for Process Mining

565

6 CONCLUSION

We have presented an extension of a proposal for pro-

cess and organizational data integration from BPMS

and relational/NoSQL DB sources to provide the ba-

sis for business process execution evaluation with pro-

cess mining. The general proposal deﬁnes an inte-

grated metamodel as a target of the ETL process to

collect process and organizational data that is then in-

tegrated using a matching algorithm. The extension

we proposed in this paper includes changes in the

metamodel to adapt the data concepts to other data

models, i.e., NoSQL. We also deﬁne the joint use of a

generic API for BPMS and a generic API for organi-

zational data that allows us to decouple the ETL pro-

cess both from the BPMS and DB sources. In partic-

ular, we have deﬁned a generic API for organizational

data from scratch. We implemented three prototypes

considering different settings of BPMS and process

engine DBs, in combination with organizational DBs

of different types, that allowed us to probe the feasi-

bility of our approach.

We have deﬁned a model-driven approach from

the metamodel with the integrated process and orga-

nizational data to automatically generate an extended

event log that includes the corresponding organiza-

tional data for each event (activity) of the process.

This extended event log can be used as input in pro-

cess mining tools. It can also be used for integrated

process and data mining analysis, crossing the process

view with the associated organizational data view. As

future work we plan on continue applying the ap-

proach within other domains (e.g. e-Government), in

heterogeneous scenarios with other BPMS and DBs.

ACKNOWLEDGEMENTS

Supported by project “Miner

ıa de procesos y datos

para la mejora de procesos colaborativos aplicada a

e-Government” funded by Agencia Nacional de In-

vestigaci

on e Innovaci

on (ANII), Fondo Mar

ıa Vi

nas

(FMV) ”Proyecto ANII N° FMV 1 2021 1 167483”,

Uruguay. We would like to thank students: Alexis

Artus, Andr

es Borges, Santiago Sosa, Germ

Gonz

alez, Alvaro Vallv

e, Yonathan Benelli, Rafael

opez and St

efano Pesamosca, for their work in the

complete data integration approach prototypes.

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