40,996 research outputs found
Predictive Process Model Monitoring using Recurrent Neural Networks
The field of predictive process monitoring focuses on modelling future
characteristics of running business process instances, typically by either
predicting the outcome of particular objectives (e.g. completion (time), cost),
or next-in-sequence prediction (e.g. what is the next activity to execute).
This paper introduces Processes-As-Movies (PAM), a technique that provides a
middle ground between these predictive monitoring. It does so by capturing
declarative process constraints between activities in various windows of a
process execution trace, which represent a declarative process model at
subsequent stages of execution. This high-dimensional representation of a
process model allows the application of predictive modelling on how such
constraints appear and vanish throughout a process' execution. Various
recurrent neural network topologies tailored to high-dimensional input are used
to model the process model evolution with windows as time steps, including
encoder-decoder long short-term memory networks, and convolutional long
short-term memory networks. Results show that these topologies are very
effective in terms of accuracy and precision to predict a process model's
future state, which allows process owners to simultaneously verify what linear
temporal logic rules hold in a predicted process window (objective-based), and
verify what future execution traces are allowed by all the constraints together
(trace-based)
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
Clustering-Based Predictive Process Monitoring
Business process enactment is generally supported by information systems that
record data about process executions, which can be extracted as event logs.
Predictive process monitoring is concerned with exploiting such event logs to
predict how running (uncompleted) cases will unfold up to their completion. In
this paper, we propose a predictive process monitoring framework for estimating
the probability that a given predicate will be fulfilled upon completion of a
running case. The predicate can be, for example, a temporal logic constraint or
a time constraint, or any predicate that can be evaluated over a completed
trace. The framework takes into account both the sequence of events observed in
the current trace, as well as data attributes associated to these events. The
prediction problem is approached in two phases. First, prefixes of previous
traces are clustered according to control flow information. Secondly, a
classifier is built for each cluster using event data to discriminate between
fulfillments and violations. At runtime, a prediction is made on a running case
by mapping it to a cluster and applying the corresponding classifier. The
framework has been implemented in the ProM toolset and validated on a log
pertaining to the treatment of cancer patients in a large hospital
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