3,970 research outputs found
Clinical Processes - The Killer Application for Constraint-Based Process Interactions?
For more than a decade, the interest in aligning information
systems in a process-oriented way has been increasing. To enable operational
support for business processes, the latter are usually specified in
an imperative way. The resulting process models, however, tend to be too
rigid to meet the flexibility demands of the actors involved. Declarative
process modeling languages, in turn, provide a promising alternative in
scenarios in which a high level of flexibility is demanded. In the scientific
literature, declarative languages have been used for modeling rather simple
processes or synthetic examples. However, to the best of our knowledge,
they have not been used to model complex, real-world scenarios
that comprise constraints going beyond control-flow. In this paper, we
propose the use of a declarative language for modeling a sophisticated
healthcare process scenario from the real world. The scenario is subject to
complex temporal constraints and entails the need for coordinating the
constraint-based interactions among the processes related to a patient
treatment process. As demonstrated in this work, the selected real process
scenario can be suitably modeled through a declarative approach.Ministerio de EconomĂa y Competitividad TIN2016-76956-C3-2-RMinisterio de EconomĂa y Competitividad TIN2015-71938-RED
Discovering business process simulation models in the presence of multitasking and availability constraints
Business process simulation is a versatile technique for quantitative analysis of business
processes. A well-known limitation of process simulation is that the accuracy of the simulation
results is limited by the faithfulness of the process model and simulation parameters given as
input to the simulator. To tackle this limitation, various authors have proposed to discover
simulation models from process execution logs, so that the resulting simulation models more
closely match reality. However, existing techniques in this field make certain assumptions
about resource behavior that do not typically hold in practice, including: (i) that each resource
performs one task at a time; and (ii) that resources are continuously available (24/7). In reality,
resources may engage in multitasking behavior and they work only during certain periods
of the day or the week. This article proposes an approach to discover process simulation
models from execution logs in the presence of multitasking and availability constraints. To
account for multitasking, we adjust the processing times of tasks in such a way that executing
the multitasked tasks sequentially with the adjusted times is equivalent to executing them
concurrently with the original times. Meanwhile, to account for availability constraints, we
use an algorithm for discovering calendar expressions from collections of time-points to infer
resource timetables from an execution log. We then adjust the parameters of this algorithm
to maximize the similarity between the simulated log and the original one. We evaluate the
approach using real-life and synthetic datasets. The results show that the approach improves
the accuracy of simulation models discovered from execution logs both in the presence of
multitasking and availability constraintsEuropean Research Council PIX 834141Ministerio de Ciencia, InnovaciĂłn y Universidades OPHELIA RTI2018-101204-B-C22Junta de AndalucĂa EKIPMENTPLUS (P18âFRâ2895
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
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
Heuristic Approaches for Generating Local Process Models through Log Projections
Local Process Model (LPM) discovery is focused on the mining of a set of
process models where each model describes the behavior represented in the event
log only partially, i.e. subsets of possible events are taken into account to
create so-called local process models. Often such smaller models provide
valuable insights into the behavior of the process, especially when no adequate
and comprehensible single overall process model exists that is able to describe
the traces of the process from start to end. The practical application of LPM
discovery is however hindered by computational issues in the case of logs with
many activities (problems may already occur when there are more than 17 unique
activities). In this paper, we explore three heuristics to discover subsets of
activities that lead to useful log projections with the goal of speeding up LPM
discovery considerably while still finding high-quality LPMs. We found that a
Markov clustering approach to create projection sets results in the largest
improvement of execution time, with discovered LPMs still being better than
with the use of randomly generated activity sets of the same size. Another
heuristic, based on log entropy, yields a more moderate speedup, but enables
the discovery of higher quality LPMs. The third heuristic, based on the
relative information gain, shows unstable performance: for some data sets the
speedup and LPM quality are higher than with the log entropy based method,
while for other data sets there is no speedup at all.Comment: paper accepted and to appear in the proceedings of the IEEE Symposium
on Computational Intelligence and Data Mining (CIDM), special session on
Process Mining, part of the Symposium Series on Computational Intelligence
(SSCI
Visual analysis of sensor logs in smart spaces: Activities vs. situations
Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS
Advanced Methods in Business Process Deviance Mining
Ăriprotsessi hĂ€lve on nĂ€htus, kus alamhulk Ă€riprotsessi tĂ€itmistest erinevad soovitud vĂ”i ettenĂ€htud tulemusest, kas positiivses vĂ”i negatiivses mĂ”ttes. Ăriprotsesside hĂ€lbega tĂ€itmised sisaldavad endas tĂ€itmisi, mis ei vasta ettekirjutatud reeglitele vĂ”i tĂ€itmised, mis on jÀÀvad alla vĂ”i ĂŒletavad tulemuslikkuse eesmĂ€rke. HĂ€lbekaevandus tegeleb hĂ€lbe pĂ”hjuste otsimisega, analĂŒĂŒsides selleks Ă€riprotsesside sĂŒndmuste logisid.Antud töös lĂ€henetakse protsessihĂ€lvete pĂ”hjuste otsimise ĂŒlesandele, esmalt kasutades jĂ€rjestikkudel pĂ”hinevaid vĂ”i deklaratiivseid mustreid ning nende kombinatsiooni. HĂ€lbekaevandusest saadud pĂ”hjendusi saab parendada, kasutades sĂŒndmustes ja sĂŒndmusjĂ€lgede atribuutides sisalduvaid andmelaste. Andmelastidest konstrueeritakse uued tunnused nii otsekoheselt atribuute ekstraheerides ja agregeerides kui ka andmeteadlike deklaratiivseid piiranguid kasutades. HĂ€lbeid iseloomustavad pĂ”hjendused ekstraheeritakse kasutades kaudset ja otsest meetodit reeglite induktsiooniks. Kasutades sĂŒnteetilisi ja reaalseid logisid, hinnatakse erinevaid tunnuseid ja tulemuseks saadud otsustusreegleid nii nende vĂ”imekuses tĂ€pselt eristada hĂ€lbega ja hĂ€lbeta protsesside tĂ€itmiseid kui ka kasutajatele antud lĂ”pptulemustes.Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to its expected or desirable outcomes. Deviant executions of a business process include those that violate compliance rules, or executions that undershoot or exceed performance targets. Deviance mining is concerned with uncovering the reasons for deviant executions by analyzing business process event logs. In this thesis, the problem of explaining deviations in business processes is first investigated by using features based on sequential and declarative patterns, and a combination of them. The explanations are further improved by leveraging the data payload of events and traces in event logs through features based on pure data attribute values and data-aware declare constraints. The explanations characterizing the deviances are then extracted by direct and indirect methods for rule induction. Using synthetic and real-life logs from multiple domains, a range of feature types and different forms of decision rules are evaluated in terms of their ability to accurately discriminate between non-deviant and deviant executions of a process as well as in terms of the final outcome returned to the users
- âŠ