86 research outputs found
A System for Deduction-based Formal Verification of Workflow-oriented Software Models
The work concerns formal verification of workflow-oriented software models
using deductive approach. The formal correctness of a model's behaviour is
considered. Manually building logical specifications, which are considered as a
set of temporal logic formulas, seems to be the significant obstacle for an
inexperienced user when applying the deductive approach. A system, and its
architecture, for the deduction-based verification of workflow-oriented models
is proposed. The process of inference is based on the semantic tableaux method
which has some advantages when compared to traditional deduction strategies.
The algorithm for an automatic generation of logical specifications is
proposed. The generation procedure is based on the predefined workflow patterns
for BPMN, which is a standard and dominant notation for the modeling of
business processes. The main idea for the approach is to consider patterns,
defined in terms of temporal logic,as a kind of (logical) primitives which
enable the transformation of models to temporal logic formulas constituting a
logical specification. Automation of the generation process is crucial for
bridging the gap between intuitiveness of the deductive reasoning and the
difficulty of its practical application in the case when logical specifications
are built manually. This approach has gone some way towards supporting,
hopefully enhancing our understanding of, the deduction-based formal
verification of workflow-oriented models.Comment: International Journal of Applied Mathematics and Computer Scienc
Corpus Statistics for Measuring Business Process Similarity
In a rapidly changing environment, organizations must adapt their business processes continuously. While numerous methods enable enterprises to conceptualize and analyze their organizational structure, the task of business process modeling remains complex and time-consuming. However, by reusing and adapting existing process models, enterprises can reduce the task’s complexity while improving the quality of results. To facilitate the identification of adaptable processes, several techniques of business process similarity (BPS) have been proposed in recent years. Although most approaches produce sound results in controlled evaluations, this paper argues that their applicability is limited when analyzing real-world processes, which do not fully comply with notational labeling specifications. Consequently, we aim to enhance existing BPS techniques by using corpus statistics to account for the explanatory power of words within labels of process models. Results from our evaluation suggest that corpus statistics can improve BPS computations and can positively influence the quality of practical implications
25 DesafÃos de la Modelación de Procesos Semánticos
Process modeling has become an essential part of many organizations for documenting, analyzing and redesigning their business operations and to support them with suitable information
systems. In order to serve this purpose, it is important for process models to be well grounded in for- mal and precise semantics. While behavioural semantics of process models are well understood, there is a considerable gap of research into the semantic aspects of their text labels and natural lan- guage descriptions. The aim of this paper is to make this research gap more transparent. To this end, we clarify the role of textual content in process models and the challenges that are associated with
the interpretation, analysis, and improvement of their natural language parts. More specifically, we
discuss particular use cases of semantic process modeling to identify 25 challenges. For each cha- llenge, we identify prior research and discuss directions for addressing themEl modelado de procesos se ha convertido en una parte esencial de muchas organizaciones para documentar, analizar, y rediseñar sus operaciones de negocios y apoyarlos con información apropiada. Para cumplir este fin, es importante para estos que estén completos dentro de una semántica formal y precisa. Mientras la semántica del comportamiento del modelado de procesos se entiende bien, hay una considerable laguna en la investigación entre los aspectos semánticos de sus rótulos textuales, y las descripciones en lenguaje natural. El objetivo de este artÃculo es hacer esta laguna en la investigación más transparente. Con este fin, clarificamos el papel del contenido textual en los modelos de proceso, y los retos relacionados con la interpretación, el análisis, y desarrollo de sus partes en lenguaje natural. De forma más especÃfica, debatimos los casos particulares del uso del modelado de procesos semánticos para identificar 25 retos. Para cada reto, identificamos antes de la investigación y debatimos las direcciones para dirigirnos a ellos
OC-PM: Analyzing Object-Centric Event Logs and Process Models
Object-centric process mining is a novel branch of process mining that aims
to analyze event data from mainstream information systems (such as SAP) more
naturally, without being forced to form mutually exclusive groups of events
with the specification of a case notion. The development of object-centric
process mining is related to exploiting object-centric event logs, which
includes exploring and filtering the behavior contained in the logs and
constructing process models which can encode the behavior of different classes
of objects and their interactions (which can be discovered from object-centric
event logs). This paper aims to provide a broad look at the exploration and
processing of object-centric event logs to discover information related to the
lifecycle of the different objects composing the event log. Also, comprehensive
tool support (OC-PM) implementing the proposed techniques is described in the
paper
Advancements and Challenges in Object-Centric Process Mining: A Systematic Literature Review
Recent years have seen the emergence of object-centric process mining
techniques. Born as a response to the limitations of traditional process mining
in analyzing event data from prevalent information systems like CRM and ERP,
these techniques aim to tackle the deficiency, convergence, and divergence
issues seen in traditional event logs. Despite the promise, the adoption in
real-world process mining analyses remains limited. This paper embarks on a
comprehensive literature review of object-centric process mining, providing
insights into the current status of the discipline and its historical
trajectory
A visual analysis of the process of process modeling
The construction of business process models has become an important requisite
in the analysis and optimization of processes. The success of the analysis and
optimization efforts heavily depends on the quality of the models. Therefore, a
research domain emerged that studies the process of process modeling. This
paper contributes to this research by presenting a way of visualizing the
different steps a modeler undertakes to construct a process model, in a
so-called process of process modeling Chart. The graphical representation
lowers the cognitive efforts to discover properties of the modeling process,
which facilitates the research and the development of theory, training and tool
support for improving model quality. The paper contains an extensive overview
of applications of the tool that demonstrate its usefulness for research and
practice and discusses the observations from the visualization in relation to
other work. The visualization was evaluated through a qualitative study that
confirmed its usefulness and added value compared to the Dotted Chart on which
the visualization was inspired
25 Challenges of Semantic Process Modeling
Process modeling has become an essential part of many organizations for documenting, analyzing and redesigning their business operations and to support them with suitable information systems. In order to serve this purpose, it is important for process models to be well grounded in formal and precise semantics. While behavioural semantics of process models are well understood, there is a considerable gap of research into the semantic aspects of their text labels and natural language descriptions. The aim of this paper is to make this research gap more transparent. To this end, we clarify the role of textual content in process models and the challenges that are associated with the interpretation, analysis, and improvement of their natural language parts. More specifically, we discuss particular use cases of semantic process modeling to identify 25 challenges. For each challenge, we identify prior research and discuss directions for addressing them
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