168 research outputs found
Guided Interaction Exploration in Artifact-centric Process Models
Artifact-centric process models aim to describe complex processes as a
collection of interacting artifacts. Recent development in process mining allow
for the discovery of such models. However, the focus is often on the
representation of the individual artifacts rather than their interactions.
Based on event data we can automatically discover composite state machines
representing artifact-centric processes. Moreover, we provide ways of
visualizing and quantifying interactions among different artifacts. For
example, we are able to highlight strongly correlated behaviours in different
artifacts. The approach has been fully implemented as a ProM plug-in; the CSM
Miner provides an interactive artifact-centric process discovery tool focussing
on interactions. The approach has been evaluated using real life data sets,
including the personal loan and overdraft process of a Dutch financial
institution.Comment: 10 pages, 4 figures, to be published in proceedings of the 19th IEEE
Conference on Business Informatics, CBI 201
Advancements and Challenges in Object-Centric Process Mining: A Systematic Literature Review
Recent years have seen the emergence of object-centric process mining
techniques. Born as a response to the limitations of traditional process mining
in analyzing event data from prevalent information systems like CRM and ERP,
these techniques aim to tackle the deficiency, convergence, and divergence
issues seen in traditional event logs. Despite the promise, the adoption in
real-world process mining analyses remains limited. This paper embarks on a
comprehensive literature review of object-centric process mining, providing
insights into the current status of the discipline and its historical
trajectory
A visual analysis of the process of process modeling
The construction of business process models has become an important requisite
in the analysis and optimization of processes. The success of the analysis and
optimization efforts heavily depends on the quality of the models. Therefore, a
research domain emerged that studies the process of process modeling. This
paper contributes to this research by presenting a way of visualizing the
different steps a modeler undertakes to construct a process model, in a
so-called process of process modeling Chart. The graphical representation
lowers the cognitive efforts to discover properties of the modeling process,
which facilitates the research and the development of theory, training and tool
support for improving model quality. The paper contains an extensive overview
of applications of the tool that demonstrate its usefulness for research and
practice and discusses the observations from the visualization in relation to
other work. The visualization was evaluated through a qualitative study that
confirmed its usefulness and added value compared to the Dotted Chart on which
the visualization was inspired
Modeling of IoT devices in Business Processes: A Systematic Mapping Study
[EN] The Internet of Things (IoT) enables to connect the physical world to digital business processes (BP). By using the IoT, a BP can, e.g.: 1) take into account real-world data to take more informed business decisions, and 2) automate and/or improve BP tasks. To achieve these benefits, the integration of IoT and BPs needs to be successful. The first step to this end is to support the modeling of IoT-enhanced BPs. Although numerous researchers have studied this subject, it is unclear what is the current state of the art in terms of current modeling solutions and gaps. In this work, we carry out a Systematic Mapping Study (SMS) to find out how current solutions are modelling IoT into business processes. After studying 600 papers, we identified and analyzed in depth a total of 36 different solutions. In addition, we report on some important issues that should be addressed in the near future, such as, for instance the lack of standardization.This research has been funded by Internal Funds KU Leuven (Interne
Fondsen KU Leuven) and the financial support of the Spanish State Research
Agency under the project TIN2017-84094-R and co-financed with ERDF.Torres Bosch, MV.; Serral, E.; Valderas, P.; Pelechano Ferragud, V.; Grefen, P. (2020). Modeling of IoT devices in Business Processes: A Systematic Mapping Study. IEEE. 221-230. https://doi.org/10.1109/CBI49978.2020.00031S22123
OC-PM: Analyzing Object-Centric Event Logs and Process Models
Object-centric process mining is a novel branch of process mining that aims
to analyze event data from mainstream information systems (such as SAP) more
naturally, without being forced to form mutually exclusive groups of events
with the specification of a case notion. The development of object-centric
process mining is related to exploiting object-centric event logs, which
includes exploring and filtering the behavior contained in the logs and
constructing process models which can encode the behavior of different classes
of objects and their interactions (which can be discovered from object-centric
event logs). This paper aims to provide a broad look at the exploration and
processing of object-centric event logs to discover information related to the
lifecycle of the different objects composing the event log. Also, comprehensive
tool support (OC-PM) implementing the proposed techniques is described in the
paper
Blockchains for Business Process Management - Challenges and Opportunities
Blockchain technology promises a sizable potential for executing
inter-organizational business processes without requiring a central party
serving as a single point of trust (and failure). This paper analyzes its
impact on business process management (BPM). We structure the discussion using
two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle.
This paper provides research directions for investigating the application of
blockchain technology to BPM.Comment: Preprint for ACM TMI
Automated Process Discovery: A Literature Review and a Comparative Evaluation with Domain Experts
Äriprotsesside kaeve meetodi võimaldavad analüütikul kasutada logisid saamaks teadmisi protsessi tegeliku toimise kohta. Neist meetodist üks enim uuritud on automaatne äriprotsesside avastamine. Sündmuste logi võetakse kui sisend automaatse äriprotsesside avastamise meetodi poolt ning väljundina toodetakse äriprotsessi mudel, mis kujutab logis talletatud sündmuste kontrollvoogu. Viimase kahe kümnendi jooksul on väljapakutud mitmeidki automaatseid äriprotsessi avastamise meetodeid balansseerides erinevalt toodetavate mudelite skaleeruvuse, täpsuse ning keerukuse vahel. Siiani on automaatsed äriprotsesside avastamise meetodid testitud ad-hoc kombel, kus erinevad autorid kasutavad erinevaid andmestike, seadistusi, hindamismeetrikuid ning alustõdesid, mis viib tihti võrdlematute tulemusteni ning mõnikord ka mittetaastoodetavate tulemusteni suletud andmestike kasutamise tõttu. Eelpool toodu mõistes sooritatakse antud magistritöö raames süstemaatiline kirjanduse ülevaade automaatsete äriprotsesside avastamise meetoditest ja ka süstemaatiline hindav võrdlus üle nelja kvaliteedimeetriku olemasolevate automaatsete äriprotsesside avastamise meetodite kohta koostöös domeeniekspertidega ning kasutades reaalset logi rahvusvahelisest tarkvara firmast. Kirjanduse ülevaate ning hindamise tulemused tõstavad esile puudujääke ning seni uurimata kompromisse mudelite loomiseks nelja kvaliteedimeetriku kontekstis. Antud magistritöö tulemused võimaldavad teaduritel parandada puudujäägid meetodites. Samuti vastatakse küsimusele automaatsete äriprotsesside avastamise meetodite kasutamise kohta väljaspool akadeemilist maailma.Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual performance of these processes.One of the most widely studied process mining operations is automated process discovery.An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log.Several automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models.So far, automated process discovery methods have been evaluated in an ad hoc manner, with different authors employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of non-publicly available datasets.In this setting, this thesis provides a systematic review of automated process discovery methods and a systematic comparative evaluation of existing implementations of these methods with domain experts by using a real-life event log extracted from a international software engineering company and four quality metrics.The review and evaluation results highlight gaps and unexplored tradeoffs in the field in the context of four business process model quality metrics.The results of this master thesis allows researchers to improve the lacks in the automated process discovery methods and also answers question about the usability of process discovery techniques in industry
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