222 research outputs found
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
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
Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles
Recent papers have introduced a novel approach to explain why a Predictive
Process Monitoring (PPM) model for outcome-oriented predictions provides wrong
predictions. Moreover, they have shown how to exploit the explanations,
obtained using state-of-the art post-hoc explainers, to identify the most
common features that induce a predictor to make mistakes in a semi-automated
way, and, in turn, to reduce the impact of those features and increase the
accuracy of the predictive model. This work starts from the assumption that
frequent control flow patterns in event logs may represent important features
that characterize, and therefore explain, a certain prediction. Therefore, in
this paper, we (i) employ a novel encoding able to leverage DECLARE constraints
in Predictive Process Monitoring and compare the effectiveness of this encoding
with Predictive Process Monitoring state-of-the art encodings, in particular
for the task of outcome-oriented predictions; (ii) introduce a completely
automated pipeline for the identification of the most common features inducing
a predictor to make mistakes; and (iii) show the effectiveness of the proposed
pipeline in increasing the accuracy of the predictive model by validating it on
different real-life datasets
A support for understanding medical notes: Correcting spelling errors in Italian clinical records
Evaluating Wiki Collaborative Features in Ontology Authoring (Extended abstract)
Abstract: This extended abstract summarizes a rigorous investigation about the effectiveness of the impact of wiki collaborative functionalities on the collaborative ontology authoring. The work summarized in this extended abstract has been published in Context. This extended abstract summarizes a rigorous investigation about the impact of wiki collaborative functionalities on ontology modelling, presented in: Good quality ontology modelling often demands for multiple competencies and skills, which are difficult to find in a single person. This results in the need of involving more actors, possibly with different roles and expertise, collaborating towards the ontology construction. Collaborative ontology authoring has been recently widely investigated in the literature A first requirement deals with the collaboration between who knows the domain that is going to be modelled, i.e., the Domain Expert (DE) and who has the technical skills to formalize the domain modelling. i.e., the Knowledge Engineer (KE). Traditional methodologies and tools were mainly based on the idea that knowledge engineers should drive the modelling process (producing ontologies in a formalism which is usually not understandable for domain experts) and domain experts should only report to KEs their knowledge of the domain. However, these methodologies often create an unnecessary extra layer of indirectness, an imbalance between the two roles and the impossibility for the domain experts to understand the modelled ontology. DEs should be actively involved in the ontology modelling process rather than only provide domain knowledge to KEs. A second important requirement deals with the support of distributed teams of actors. Independently of their geographical position or their role, team members should be made aware about the collaborative development of the modelled artefacts, should be supported in the communication of modeling choices, as well as in the work coordination. Wiki tools for the ontology authoring offer an appealing option for tackling these collaborative aspects. Indeed wikis usually provide collaborative features (wiki collaborative 1 Fondazione Bruno Kessler, Via Sommarive, 18, 38123 Trento, dfmchiara|ghidini|rospocher@fbk,e
Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns
Prescriptive Process Monitoring systems recommend, during the execution of a
business process, interventions that, if followed, prevent a negative outcome
of the process. Such interventions have to be reliable, that is, they have to
guarantee the achievement of the desired outcome or performance, and they have
to be flexible, that is, they have to avoid overturning the normal process
execution or forcing the execution of a given activity. Most of the existing
Prescriptive Process Monitoring solutions, however, while performing well in
terms of recommendation reliability, provide the users with very specific
(sequences of) activities that have to be executed without caring about the
feasibility of these recommendations. In order to face this issue, we propose a
new Outcome-Oriented Prescriptive Process Monitoring system recommending
temporal relations between activities that have to be guaranteed during the
process execution in order to achieve a desired outcome. This softens the
mandatory execution of an activity at a given point in time, thus leaving more
freedom to the user in deciding the interventions to put in place. Our approach
defines these temporal relations with Linear Temporal Logic over finite traces
patterns that are used as features to describe the historical process data
recorded in an event log by the information systems supporting the execution of
the process. Such encoded log is used to train a Machine Learning classifier to
learn a mapping between the temporal patterns and the outcome of a process
execution. The classifier is then queried at runtime to return as
recommendations the most salient temporal patterns to be satisfied to maximize
the likelihood of a certain outcome for an input ongoing process execution. The
proposed system is assessed using a pool of 22 real-life event logs that have
already been used as a benchmark in the Process Mining community.Comment: 38 pages, 6 figures, 8 table
An Incremental Learning Method to Support the Annotation of Workflows with Data-to-Data Relations
Workflow formalisations are often focused on the representation of a process with the primary objective to support execution. However, there are scenarios where what needs to be represented is the effect of the process on the data artefacts involved, for example when reasoning over the corresponding data policies. This can be achieved by annotating the workflow with the semantic relations that occur between these data artefacts. However, manually producing such annotations is difficult and time consuming. In this paper we introduce a method based on recommendations to support users in this task. Our approach is centred on an incremental rule association mining technique that allows to compensate the cold start problem due to the lack of a training set of annotated workflows. We discuss the implementation of a tool relying on this approach and how its application on an existing repository of workflows effectively enable the generation of such annotations
Genetic algorithms for hyperparameter optimization in predictive business process monitoring
Predictive business process monitoring exploits event logs to predict how ongoing (uncompleted) traces will unfold up to their completion. A predictive process monitoring framework collects a range of techniques that allow users to get accurate predictions about the achievement of a goal for a given ongoing trace. These techniques can be combined and their parameters configured in different framework instances. Unfortunately, a unique framework instance that is general enough to outperform others for every dataset, goal or type of prediction is elusive. Thus, the selection and configuration of a framework instance needs to be done for a given dataset. This paper presents a predictive process monitoring framework armed with a hyperparameter optimization method to select a suitable framework instance for a given dataset
Enhancing workflow-nets with data for trace completion
The growing adoption of IT-systems for modeling and executing (business)
processes or services has thrust the scientific investigation towards
techniques and tools which support more complex forms of process analysis. Many
of them, such as conformance checking, process alignment, mining and
enhancement, rely on complete observation of past (tracked and logged)
executions. In many real cases, however, the lack of human or IT-support on all
the steps of process execution, as well as information hiding and abstraction
of model and data, result in incomplete log information of both data and
activities. This paper tackles the issue of automatically repairing traces with
missing information by notably considering not only activities but also data
manipulated by them. Our technique recasts such a problem in a reachability
problem and provides an encoding in an action language which allows to
virtually use any state-of-the-art planning to return solutions
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