103 research outputs found
Predictive Monitoring of Business Processes
Modern information systems that support complex business processes generally
maintain significant amounts of process execution data, particularly records of
events corresponding to the execution of activities (event logs). In this
paper, we present an approach to analyze such event logs in order to
predictively monitor business goals during business process execution. At any
point during an execution of a process, the user can define business goals in
the form of linear temporal logic rules. When an activity is being executed,
the framework identifies input data values that are more (or less) likely to
lead to the achievement of each business goal. Unlike reactive compliance
monitoring approaches that detect violations only after they have occurred, our
predictive monitoring approach provides early advice so that users can steer
ongoing process executions towards the achievement of business goals. In other
words, violations are predicted (and potentially prevented) rather than merely
detected. The approach has been implemented in the ProM process mining toolset
and validated on a real-life log pertaining to the treatment of cancer patients
in a large hospital
A Literature Review on Predictive Monitoring of Business Processes
Oleme läbi vaadanud mitmesuguseid ennetava jälgimise meetodeid äriprotsessides. Prognoositavate seirete eesmärk on aidata ettevõtetel oma eesmärke saavutada, aidata neil valida õige ärimudel, prognoosida tulemusi ja aega ning muuta äriprotsessid riskantsemaks. Antud väitekirjaga oleme hoolikalt kogunud ja üksikasjalikult läbi vaadanud selle väitekirja teemal oleva kirjanduse. Kirjandusuuringu tulemustest ja tähelepanekutest lähtuvalt oleme hoolikalt kavandanud ennetava jälgimisraamistiku. Raamistik on juhendiks ettevõtetele ja teadlastele, teadustöötajatele, kes uurivad selles valdkonnas ja ettevõtetele, kes soovivad neid tehnikaid oma valdkonnas rakendada.The goal of predictive monitoring is to help the business achieve their goals, help them take the right business path, predict outcomes, estimate delivery time, and make business processes risk aware. In this thesis, we have carefully collected and reviewed in detail all literature which falls in this process mining category. The objective of the thesis is to design a Predictive Monitoring Framework and classify the different predictive monitoring techniques. The framework acts as a guide for researchers and businesses. Researchers who are investigating in this field and businesses who want to apply these techniques in their respective field
Run-time prediction of business process indicators using evolutionary decision rules
Predictive monitoring of business processes is a challenging topic of process mining which is concerned with the prediction of process indicators of running process instances. The main value of predictive monitoring is to provide information in order to take proactive and corrective actions to improve process performance and mitigate risks in real time. In this paper, we present an approach for predictive monitoring based on the use of evolutionary algorithms. Our method provides a novel event window-based encoding and generates a set of decision rules for the run-time prediction of process indicators according to event log properties. These rules can be interpreted by users to extract further insight of the business processes while keeping a high level of accuracy. Furthermore, a full software stack consisting of a tool to support the training phase and a framework that enables the integration of run-time predictions with business process management systems, has been developed. Obtained results show the validity of our proposal for two large real-life datasets: BPI Challenge 2013 and IT Department of Andalusian Health Service (SAS).Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía P12TIC-186
What Automated Planning Can Do for Business Process Management
Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle
Context-Aware Process Performance Indicator Prediction
It is well-known that context impacts running instances of a process. Thus, defining and using
contextual information may help to improve the predictive monitoring of business processes, which is one
of the main challenges in process mining. However, identifying this contextual information is not an easy
task because it might change depending on the target of the prediction. In this paper, we propose a novel
methodology named CAP3 (Context-aware Process Performance indicator Prediction) which involves two
phases. The first phase guides process analysts on identifying the context for the predictive monitoring of
process performance indicators (PPIs), which are quantifiable metrics focused on measuring the progress
of strategic objectives aimed to improve the process. The second phase involves a context-aware predictive
monitoring technique that incorporates the relevant context information as input for the prediction. Our
methodology leverages context-oriented domain knowledge and experts’ feedback to discover the contextual
information useful to improve the quality of PPI prediction with a decrease of error rates in most cases,
by adding this information as features to the datasets used as input of the predictive monitoring process.
We experimentally evaluated our approach using two-real-life organizations. Process experts from both
organizations applied CAP3 methodology and identified the contextual information to be used for prediction.
The model learned using this information achieved lower error rates in most cases than the model learned
without contextual information confirming the benefits of CAP3.European Union Horizon 2020 No. 645751 (RISE BPM)Ministerio de Ciencia, Innovación y Universidades Horatio RTI2018-101204-B-C21Ministerio de Ciencia, Innovación y Universidades OPHELIA RTI2018-101204-B-C2
Handling Concept Drift for Predictions in Business Process Mining
Predictive services nowadays play an important role across all business
sectors. However, deployed machine learning models are challenged by changing
data streams over time which is described as concept drift. Prediction quality
of models can be largely influenced by this phenomenon. Therefore, concept
drift is usually handled by retraining of the model. However, current research
lacks a recommendation which data should be selected for the retraining of the
machine learning model. Therefore, we systematically analyze different data
selection strategies in this work. Subsequently, we instantiate our findings on
a use case in process mining which is strongly affected by concept drift. We
can show that we can improve accuracy from 0.5400 to 0.7010 with concept drift
handling. Furthermore, we depict the effects of the different data selection
strategies
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances
Predicting the completion time of business process instances would be a very
helpful aid when managing processes under service level agreement constraints.
The ability to know in advance the trend of running process instances would
allow business managers to react in time, in order to prevent delays or
undesirable situations. However, making such accurate forecasts is not easy:
many factors may influence the required time to complete a process instance. In
this paper, we propose an approach based on deep Recurrent Neural Networks
(specifically LSTMs) that is able to exploit arbitrary information associated
to single events, in order to produce an as-accurate-as-possible prediction of
the completion time of running instances. Experiments on real-world datasets
confirm the quality of our proposal.Comment: Article accepted for publication in 2017 IEEE Symposium on Deep
Learning (IEEE DL'17) @ SSC
Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
A characteristic of existing predictive process monitoring techniques is to
first construct a predictive model based on past process executions, and then
use it to predict the future of new ongoing cases, without the possibility of
updating it with new cases when they complete their execution. This can make
predictive process monitoring too rigid to deal with the variability of
processes working in real environments that continuously evolve and/or exhibit
new variant behaviors over time. As a solution to this problem, we propose the
use of algorithms that allow the incremental construction of the predictive
model. These incremental learning algorithms update the model whenever new
cases become available so that the predictive model evolves over time to fit
the current circumstances. The algorithms have been implemented using different
case encoding strategies and evaluated on a number of real and synthetic
datasets. The results provide a first evidence of the potential of incremental
learning strategies for predicting process monitoring in real environments, and
of the impact of different case encoding strategies in this setting
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