Table Of ContentGiovanni Meroni
8 Artifact-Driven Business
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Process Monitoring
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A Novel Approach to Transparently Monitor
Business Processes, Supported by Methods,
Tools, and Real-World Applications
123
Lecture Notes
in Business Information Processing 368
Series Editors
Wil van der Aalst
RWTH Aachen University, Aachen, Germany
John Mylopoulos
University of Trento, Trento, Italy
Michael Rosemann
Queensland University of Technology, Brisbane, QLD, Australia
Michael J. Shaw
University of Illinois, Urbana-Champaign, IL, USA
Clemens Szyperski
Microsoft Research, Redmond, WA, USA
More information about this series at http://www.springer.com/series/7911
Giovanni Meroni
Artifact-Driven Business
Process Monitoring
A Novel Approach to Transparently Monitor
Business Processes, Supported by Methods,
Tools, and Real-World Applications
123
Author
GiovanniMeroni
Politecnico di Milano
Milan,Italy
ISSN 1865-1348 ISSN 1865-1356 (electronic)
Lecture Notesin Business Information Processing
ISBN 978-3-030-32411-7 ISBN978-3-030-32412-4 (eBook)
https://doi.org/10.1007/978-3-030-32412-4
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In memory of my grandmother, Lidia
Preface
ThisbookisarevisedversionofthePhDdissertationwrittenbytheauthoratthe
PhD School of Information Engineering of Politecnico di Milano (Italy). In 2019,
the PhD dissertation won the CAiSE PhD Award, granted to outstanding PhD
theses in the field of information systems engineering.
Traditionally, to monitor the execution of a business process, organizations
rely on monitoring modules provided by Business Process Management Systems
(BPMSs),whichautomateandkeeptrackoftheexecutionofprocesses[38].While
the adoption of a BPMS to monitor a single-party, fully-automated business
process is straightforward, the same cannot be said for multi-party processes relying
heavily on manual activities.
Infact,aBPMSrequiresexplicitnotificationstodeterminewhenactivitiesthat
are not under its direct control are executed. This requires organizations to fed-
erate their BPMSs, a complex task that has to be performed whenever a new
organization participates in the process. Also, when activities are not automated,
human operators are responsible for manually sending notifications to the BPMS,
a task that disrupts the operators’ work and, as such, is prone to being forgotten
or postponed.
Tocontinuouslyandautonomouslymonitormulti-partyprocessesinvolvingnon-
automated activities, this book proposes a novel technique, named artifact-driven
process monitoring. ThistechniqueexploitstheInternetofThings(IoT)paradigm
tomakethephysicalobjectsparticipatinginaprocess smart. Beingequippedwith
sensors,acomputingdevice,andacommunicationinterface,suchsmartobjectscan
thenbecomeself-awareoftheirownconditionsandoftheprocesstheyparticipate
in, and exchange this information with the other smart objects and the involved
organizations. In this way, it is possible for the monitoring infrastructure to stay in
close contact with the process, and to cross the boundaries of the organizations.
To be aware of the process to monitor, instead of using activity-centric pro-
cess models, usually adopted by BPMSs, smart objects rely on an extension
of the Guard-Stage-Milestone (GSM) artifact-centric modeling language, named
Extended-GSM (E-GSM). Normally, a BPMS expects the execution to rigidly ad-
here to the process model defined in advance. Therefore, whenever a deviation
between the execution and the model is detected, a BPMS requires human
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viii Preface
intervention to resume process monitoring. E-GSM, on the other hand, treats the
execution flow (i.e., dependencies among activities) in a descriptive rather than
prescriptive way. Consequently, smart objects can detect violations during execu-
tionwithout interruptingthe monitoring.Additionally,E-GSM canmonitorif the
physical objects evolve as expected while the process is executed. Finally, E-GSM
provides constructs to determine, based on the conditions of the physical objects,
when activities are started or ended.
Thisbookalsopresentsanapproachtodeterminetowhatextentsmartobjects
aresuitedtomonitoraparticularprocess,giventheirsensingcapabilities.Torelieve
processdesignersfromlearningtheE-GSMnotation,andtoalloworganizationsto
reusepreexistingprocessmodels,amethodtoinstructsmartobjectsgivenBusiness
Process Model and Notation (BPMN) collaboration diagrams is also presented.
Finally,aprototypeofanartifact-drivenmonitoringplatform,namedSMARTifact,
is developed and tested against both historical and live sensor data.
Acknowledgements
I would like to express my gratitude to all the people who helped me on the path
towards achieving the PhD title. First and foremost, I would like to thank my
supervisor Pierluigi Plebani, not only for giving me the opportunity, together with
Prof. Luciano Baresi, to start the PhD school, but also for his support, comments,
and useful feedback throughout my studies. I would also like to thank my tutor
Prof. Barbara Pernici for her support.
I am also grateful to all my colleagues at Politecnico di Milano for providing
a friendly and stimulating research environment, in particular Cinzia Cappiello,
MonicaVitali,FlorianDaniel,XuesongPeng,PaoloRavanelli,andMattiaSalnitri.
Likewise,IwouldliketothanktheresearchgroupinInformationBusinessatWU
Vienna, who hosted me as a visiting student, and in particular Prof. Jan Mendling
and Claudio Di Ciccio. I am also very thankful to Prof. Marco Montali from the
Free University of Bozen-Bolzano for the research collaboration carried out during
my PhD work.
Additionally,IwouldliketothanktheItalianprojectITS2020andtheMinistero
dell’Istruzione dell’Università e della Ricerca (MIUR) for funding my PhD. I am
alsoverythankfultotheexternalreviewers,Prof.MassimoMecellafromUniversità
di Roma La Sapienza and Prof. Barbara Weber from Technical University of
Denmark, for their scrupulous review work and their useful comments. I would
also like to express my gratitude to Prof. Selmin Nurcan and the CAiSE Committee
for awarding me the prestigious CAiSE PhD Award.
Finally, I would like to thank my family for their love, understanding, support,
and encouragement in undertaking the PhD school.
ix
Contents
1 Introduction................................................... 1
1.1 Motivations................................................. 1
1.2 Research Challenges ......................................... 4
1.3 Research Questions .......................................... 7
1.4 Major Contributions......................................... 8
1.5 Book Structure.............................................. 11
2 Related Work ................................................. 13
2.1 Business Process Monitoring .................................. 13
2.1.1 Process Monitoring Based on Sensor Data ................ 17
2.1.2 Event Data Logging ................................... 19
2.1.3 Business Activity Monitoring ........................... 20
2.1.4 Conformance and Compliance Checking .................. 21
2.2 Declarative Languages ....................................... 25
2.2.1 Constraint-based Languages ............................ 25
2.2.2 Artifact-centric Languages.............................. 26
2.2.3 Case Management Languages ........................... 27
2.2.4 Imperative to Declarative Model Translators.............. 29
2.3 The Internet of Things ....................................... 29
2.3.1 Enabling technologies of the IoT ........................ 30
2.3.2 Ontologies for the IoT ................................. 32
2.3.3 Synergies between the IoT and BPM..................... 33
3 Artifact-driven Process Monitoring Overview.................. 37
3.1 Motivating Example ......................................... 38
3.2 Introducing Artifact-driven Process Monitoring.................. 39
3.3 Reference Architecture ....................................... 41
3.4 Summary................................................... 43
4 E-GSM: an Artifact-centric Language for Process Monitoring.. 45
4.1 The Guard-Stage-Milestone Artifact-centric Language............ 45
4.2 Extending GSM............................................. 48
4.3 Assessing the severity of Constraints Violations ................. 53
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