312,756 research outputs found
Grid service orchestration using the Business Process Execution Language (BPEL)
Modern scientific applications often need to be distributed across grids. Increasingly
applications rely on services, such as job submission, data transfer or data
portal services. We refer to such services as grid services. While the invocation
of grid services could be hard coded in theory, scientific users want to orchestrate
service invocations more flexibly. In enterprise applications, the orchestration of
web services is achieved using emerging orchestration standards, most notably
the Business Process Execution Language (BPEL). We describe our experience
in orchestrating scientific workflows using BPEL. We have gained this experience
during an extensive case study that orchestrates grid services for the automation of
a polymorph prediction application
APQL: A process-model query language
As business process management technology matures, organisations acquire more and more business process models. The management of the resulting collections of process models poses real challenges. One of these challenges concerns model retrieval where support should be provided for the formulation and efficient execution of business process model queries. As queries based on only structural information cannot deal with all querying requirements in practice, there should be support for queries that require knowledge of process model semantics. In this paper we formally define a process model query language that is based on semantic relationships between tasks in process models and is independent of any particular process modelling notation
Using the Business Process Execution Language for Managing Scientific Processes
This paper describes the use of the Business Process Execution Language for Web Services
(BPEL4WS/BPEL) for managing scientific workflows. This work is result of our attempt to adopt Service Oriented
Architecture in order to perform Web services â based simulation of metal vapor lasers. Scientific workflows can
be more demanding in their requirements than business processes. In the context of addressing these
requirements, the features of the BPEL4WS specification are discussed, which is widely regarded as the de-facto
standard for orchestrating Web services for business workflows. A typical use case of calculation the electric field
potential and intensity distributions is discussed as an example of building a BPEL process to perform distributed
simulation constructed by loosely-coupled services
Conformance Checking Based on Multi-Perspective Declarative Process Models
Process mining is a family of techniques that aim at analyzing business
process execution data recorded in event logs. Conformance checking is a branch
of this discipline embracing approaches for verifying whether the behavior of a
process, as recorded in a log, is in line with some expected behaviors provided
in the form of a process model. The majority of these approaches require the
input process model to be procedural (e.g., a Petri net). However, in turbulent
environments, characterized by high variability, the process behavior is less
stable and predictable. In these environments, procedural process models are
less suitable to describe a business process. Declarative specifications,
working in an open world assumption, allow the modeler to express several
possible execution paths as a compact set of constraints. Any process execution
that does not contradict these constraints is allowed. One of the open
challenges in the context of conformance checking with declarative models is
the capability of supporting multi-perspective specifications. In this paper,
we close this gap by providing a framework for conformance checking based on
MP-Declare, a multi-perspective version of the declarative process modeling
language Declare. The approach has been implemented in the process mining tool
ProM and has been experimented in three real life case studies
Declarative Process Modeling with Business Vocabulary and Business Rules
In the literature, there exist already many languages for declarative process modeling. Each language addresses only one specific business concern. In our work, we define a unified framework for declarative process modeling, consisting of a unified vocabulary, execution model, and business rule types [1]. It can be used both as an expressive informal language for documenting business concerns, and as an ontological foundation to compare and develop declarative languages
Feasibility of EPC to BPEL Model Transformations Based on Ontology and Patterns
Model-Driven Engineering holds the promise of transforming\ud
business models into code automatically. This requires the concept of\ud
model transformation. In this paper, we assess the feasibility of model\ud
transformations from Event-driven Process Chain models to Business\ud
Process Execution Language specifications. To this purpose, we use a\ud
framework based on ontological analysis and workflow patterns in order\ud
to predict the possibilities/limitations of such a model transformation.\ud
The framework is validated by evaluating the transformation of several\ud
models, including a real-life case.\ud
The framework indicates several limitations for transformation. Eleven\ud
guidelines and an approach to apply them provide methodological support\ud
to improve the feasibility of model transformation from EPC to\ud
BPEL
STATE PROPAGATION FOR BUSINESS PROCESS MONITORING ON DIFFERENT LEVELS OF ABSTRACTION
Modeling and execution of business processes is often performed on different levels of abstraction. For example, when a business process is modeled using a high-level notation near to business such as Event-driven Process Chains (EPC), a technical refinement step is required before the process can be executed. Also, model-driven process design allows modeling a process on high-level, while executing it in a more detailed and executable low-level representation such as processes defined in the Business Process Execution Language (BPEL) or as Java code. However, current approaches for graphical monitoring of business processes are limited to scenarios in which the process that is being executed and the process that is being monitored are either one and the same or on the same level of abstraction. In this paper, we present an approach to facilitate business-oriented process monitoring while considering process design on high-level. We propose process views for business process monitoring as projections of activities and execution states in order to support business process monitoring of running process instances on different levels of abstraction. In particular, we discuss state propagation patterns which can be applied to define advanced monitoring solutions for arbitrary graph-based process languages
Service-Oriented Dynamic Software Product Lines
An operational example of controls in a smart home demonstrates the potential of a solution that combines the Common Variability Language and a dynamic extension of the Business Process Execution Language to address the need to manage software system variability at runtime
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
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