24,191 research outputs found
Artifact Lifecycle Discovery
Artifact-centric modeling is a promising approach for modeling business
processes based on the so-called business artifacts - key entities driving the
company's operations and whose lifecycles define the overall business process.
While artifact-centric modeling shows significant advantages, the overwhelming
majority of existing process mining methods cannot be applied (directly) as
they are tailored to discover monolithic process models. This paper addresses
the problem by proposing a chain of methods that can be applied to discover
artifact lifecycle models in Guard-Stage-Milestone notation. We decompose the
problem in such a way that a wide range of existing (non-artifact-centric)
process discovery and analysis methods can be reused in a flexible manner. The
methods presented in this paper are implemented as software plug-ins for ProM,
a generic open-source framework and architecture for implementing process
mining tools
A Method to Improve the Early Stages of the Robotic Process Automation Lifecycle
The robotic automation of processes is of much interest to
organizations. A common use case is to automate the repetitive manual
tasks (or processes) that are currently done by back-office staff
through some information system (IS). The lifecycle of any Robotic Process
Automation (RPA) project starts with the analysis of the process
to automate. This is a very time-consuming phase, which in practical
settings often relies on the study of process documentation. Such documentation
is typically incomplete or inaccurate, e.g., some documented
cases never occur, occurring cases are not documented, or documented
cases differ from reality. To deploy robots in a production environment
that are designed on such a shaky basis entails a high risk. This paper
describes and evaluates a new proposal for the early stages of an RPA
project: the analysis of a process and its subsequent design. The idea is to
leverage the knowledge of back-office staff, which starts by monitoring
them in a non-invasive manner. This is done through a screen-mousekey-
logger, i.e., a sequence of images, mouse actions, and key actions
are stored along with their timestamps. The log which is obtained in
this way is transformed into a UI log through image-analysis techniques
(e.g., fingerprinting or OCR) and then transformed into a process model
by the use of process discovery algorithms. We evaluated this method for
two real-life, industrial cases. The evaluation shows clear and substantial
benefits in terms of accuracy and speed. This paper presents the method,
along with a number of limitations that need to be addressed such that
it can be applied in wider contexts.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-
Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches
Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements
Model-driven Enterprise Systems Configuration
Enterprise Systems potentially lead to significant efficiency gains but require a well-conducted configuration process. A promising idea to manage and simplify the configuration process is based on the premise of using reference models for this task. Our paper continues along this idea and delivers a two-fold contribution: first, we present a generic process for the task of model-driven Enterprise Systems configuration including the steps of (a) Specification of configurable reference models, (b) Configuration of configurable reference models, (c) Transformation of configured reference models to regular build time models, (d) Deployment of the generated build time models, (e) Controlling of implementation models to provide input to the configuration, and (f) Consolidation of implementation models to provide input to reference model specification. We discuss inputs and outputs as well as the involvement of different roles and validation mechanisms. Second, we present an instantiation case of this generic process for Enterprise Systems configuration based on Configurable EPCs
Mining structured Petri nets for the visualization of process behavior
Visualization is essential for understanding the models obtained by process mining. Clear and efficient visual representations make the embedded information more accessible and analyzable. This work presents a novel approach for generating process models with structural properties that induce visually friendly layouts. Rather than generating a single model that captures all behaviors, a set of Petri net models is delivered, each one covering a subset of traces of the log. The models are mined by extracting slices of labelled transition systems with specific properties from the complete state space produced by the process logs. In most cases, few Petri nets are sufficient to cover a significant part of the behavior produced by the log.Peer ReviewedPostprint (author's final draft
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A MapReduce architecture for web site user behaviour monitoring in real time
Monitoring the behaviour of large numbers of web site users in real time poses significant performance challenges, due to the decentralised location and volume of generated data. This paper proposes a MapReduce-style architecture where the processing of event series from the Web users is performed by a number of cascading mappers, reducers and rereducers, local to the event origin. With the use of static analysis and a prototype implementation, we show how this architecture is capable to carry out time series analysis in real time for very large web data sets, based on the actual events, instead of resorting to sampling or other extrapolation techniques
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