23,462 research outputs found
Specification-Driven Predictive Business Process Monitoring
Predictive analysis in business process monitoring aims at forecasting the
future information of a running business process. The prediction is typically
made based on the model extracted from historical process execution logs (event
logs). In practice, different business domains might require different kinds of
predictions. Hence, it is important to have a means for properly specifying the
desired prediction tasks, and a mechanism to deal with these various prediction
tasks. Although there have been many studies in this area, they mostly focus on
a specific prediction task. This work introduces a language for specifying the
desired prediction tasks, and this language allows us to express various kinds
of prediction tasks. This work also presents a mechanism for automatically
creating the corresponding prediction model based on the given specification.
Differently from previous studies, instead of focusing on a particular
prediction task, we present an approach to deal with various prediction tasks
based on the given specification of the desired prediction tasks. We also
provide an implementation of the approach which is used to conduct experiments
using real-life event logs.Comment: This article significantly extends the previous work in
https://doi.org/10.1007/978-3-319-91704-7_7 which has a technical report in
arXiv:1804.00617. This article and the previous work have a coauthor in
commo
Exploring Interpretability for Predictive Process Analytics
Modern predictive analytics underpinned by machine learning techniques has
become a key enabler to the automation of data-driven decision making. In the
context of business process management, predictive analytics has been applied
to making predictions about the future state of an ongoing business process
instance, for example, when will the process instance complete and what will be
the outcome upon completion. Machine learning models can be trained on event
log data recording historical process execution to build the underlying
predictive models. Multiple techniques have been proposed so far which encode
the information available in an event log and construct input features required
to train a predictive model. While accuracy has been a dominant criterion in
the choice of various techniques, they are often applied as a black-box in
building predictive models. In this paper, we derive explanations using
interpretable machine learning techniques to compare and contrast the
suitability of multiple predictive models of high accuracy. The explanations
allow us to gain an understanding of the underlying reasons for a prediction
and highlight scenarios where accuracy alone may not be sufficient in assessing
the suitability of techniques used to encode event log data to features used by
a predictive model. Findings from this study motivate the need and importance
to incorporate interpretability in predictive process analytics.Comment: 15 pages, 7 figure
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
Predictive Process Monitoring Methods: Which One Suits Me Best?
Predictive process monitoring has recently gained traction in academia and is
maturing also in companies. However, with the growing body of research, it
might be daunting for companies to navigate in this domain in order to find,
provided certain data, what can be predicted and what methods to use. The main
objective of this paper is developing a value-driven framework for classifying
existing work on predictive process monitoring. This objective is achieved by
systematically identifying, categorizing, and analyzing existing approaches for
predictive process monitoring. The review is then used to develop a
value-driven framework that can support organizations to navigate in the
predictive process monitoring field and help them to find value and exploit the
opportunities enabled by these analysis techniques
Ăriprotsesside ajaliste nĂ€itajate selgitatav ennustav jĂ€lgimine
Kaasaegsed ettevĂ”tte infosĂŒsteemid vĂ”imaldavad ettevĂ”tetel koguda detailset informatsiooni Ă€riprotsesside tĂ€itmiste kohta. Eelnev koos masinĂ”ppe meetoditega vĂ”imaldab kasutada andmejuhitavaid ja ennustatavaid lĂ€henemisi Ă€riprotsesside jĂ”udluse jĂ€lgimiseks. Kasutades ennustuslike Ă€riprotsesside jĂ€lgimise tehnikaid on vĂ”imalik jĂ”udluse probleeme ennustada ning soovimatu tegurite mĂ”ju ennetavalt leevendada.
TĂŒĂŒpilised kĂŒsimused, millega tegeleb ennustuslik protsesside jĂ€lgimine on âmillal antud Ă€riprotsess lĂ”ppeb?â vĂ”i âmis on kĂ”ige tĂ”enĂ€olisem jĂ€rgmine sĂŒndmus antud Ă€riprotsessi jaoks?â. Suurim osa olemasolevatest lahendustest eelistavad tĂ€psust selgitatavusele. Praktikas, selgitatavus on ennustatavate tehnikate tĂ€htis tunnus. Ennustused, kas protsessi tĂ€itmine ebaĂ”nnestub vĂ”i selle tĂ€itmisel vĂ”ivad tekkida raskused, pole piisavad. On oluline kasutajatele seletada, kuidas on selline ennustuse tulemus saavutatud ning mida saab teha soovimatu tulemuse ennetamiseks.
Töö pakub vÀlja kaks meetodit ennustatavate mudelite konstrueerimiseks, mis vÔimaldavad jÀlgida Àriprotsesse ning keskenduvad selgitatavusel. Seda saavutatakse ennustuse lahtivÔtmisega elementaarosadeks. NÀiteks, kui ennustatakse, et Àriprotsessi lÔpuni on jÀÀnud aega 20 tundi, siis saame anda seletust, et see aeg on moodustatud kÔikide seni kÀsitlemata tegevuste lÔpetamiseks vajalikust ajast. Töös vÔrreldakse omavahel eelmainitud meetodeid, kÀsitledes Àriprotsesse erinevatest valdkondadest. Hindamine toob esile erinevusi selgitatava ja tÀpsusele pÔhinevale lÀhenemiste vahel.
Töö teaduslik panus on ennustuslikuks protsesside jĂ€lgimiseks vabavaralise tööriista arendamine. SĂŒsteemi nimeks on Nirdizati ning see sĂŒsteem vĂ”imaldab treenida ennustuslike masinĂ”ppe mudeleid, kasutades nii töös kirjeldatud meetodeid kui ka kolmanda osapoole meetodeid. Hiljem saab treenitud mudeleid kasutada hetkel kĂ€ivate Ă€riprotsesside tulemuste ennustamiseks, mis saab aidata kasutajaid reaalajas.Modern enterprise systems collect detailed data about the execution of the business processes they support. The widespread availability of such data in companies, coupled with advances in machine learning, have led to the emergence of data-driven and predictive approaches to monitor the performance of business processes. By using such predictive process monitoring approaches, potential performance issues can be anticipated and proactively mitigated.
Various approaches have been proposed to address typical predictive process monitoring questions, such as what is the most likely continuation of an ongoing process instance, or when it will finish. However, most existing approaches prioritize accuracy over explainability. Yet in practice, explainability is a critical property of predictive methods. It is not enough to accurately predict that a running process instance will end up in an undesired outcome. It is also important for users to understand why this prediction is made and what can be done to prevent this undesired outcome.
This thesis proposes two methods to build predictive models to monitor business processes in an explainable manner. This is achieved by decomposing a prediction into its elementary components. For example, to explain that the remaining execution time of a process execution is predicted to be 20 hours, we decompose this prediction into the predicted execution time of each activity that has not yet been executed. We evaluate the proposed methods against each other and various state-of-the-art baselines using a range of business processes from multiple domains. The evaluation reaffirms a fundamental trade-off between explainability and accuracy of predictions.
The research contributions of the thesis have been consolidated into an open-source tool for predictive business process monitoring, namely Nirdizati. It can be used to train predictive models using the methods described in this thesis, as well as third-party methods. These models are then used to make predictions for ongoing process instances; thus, the tool can also support users at runtime
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