3,191 research outputs found
Generating Multi-objective Optimized Business Process Enactment Plans
Declarative business process (BP) models are increasingly
used allowing their users to specify what has to be done instead of how.
Due to their flexible nature, there are several enactment plans related to
a specific declarative model, each one presenting specific values for different
objective functions, e.g., completion time or profit. In this work, a
method for generating optimized BP enactment plans from declarative
specifications is proposed to optimize the performance of a process considering
multiple objectives. The plans can be used for different purposes,
e.g., providing recommendations. The proposed approach is validated
through an empirical evaluation based on a real-world case study.Ministerio de Ciencia e Innovación TIN2009-1371
Generating optimized configurable business process models in scenarios subject to uncertainty
Context: The quality of business process models (i.e., software artifacts that capture the relations
between the organizational units of a business) is essential for enhancing the management of business
processes. However, such modeling is typically carried out manually. This is already challenging and time
consuming when (1) input uncertainty exists, (2) activities are related, and (3) resource allocation has to
be considered. When including optimization requirements regarding flexibility and robustness it
becomes even more complicated potentially resulting into non-optimized models, errors, and lack of
flexibility.
Objective: To facilitate the human work and to improve the resulting models in scenarios subject to
uncertainty, we propose a software-supported approach for automatically creating configurable business
process models from declarative specifications considering all the aforementioned requirements.
Method: First, the scenario is modeled through a declarative language which allows the analysts to specify
its variability and uncertainty. Thereafter, a set of optimized enactment plans (each one representing a
potential execution alternative) are generated from such a model considering the input uncertainty.
Finally, to deal with this uncertainty during run-time, a flexible configurable business process model is
created from these plans.
Results: To validate the proposed approach, we conduct a case study based on a real business which is
subject to uncertainty. Results indicate that our approach improves the actual performance of the business
and that the generated models support most of the uncertainty inherent to the business.
Conclusions: The proposed approach automatically selects the best part of the variability of a declarative
specification. Unlike existing approaches, our approach considers input uncertainty, the optimization of
multiple objective functions, as well as the resource and the control-flow perspectives. However, our
approach also presents a few limitations: (1) it is focused on the control-flow and the data perspective
is only partially addressed and (2) model attributes need to be estimated.Ministerio de Ciencia e Innovación TIN2009-1371
Flexible runtime support of business processes under rolling planning horizons
This work has been motivated by the needs we discovered when analyzing real-world processes from the
healthcare domain that have revealed high flexibility demands and complex temporal constraints. When trying
to model these processes with existing languages, we learned that none of the latter was able to fully address
these needs. This motivated us to design TConDec-R, a declarative process modeling language enabling the
specification of complex temporal constraints. Enacting business processes based on declarative process models,
however, introduces a high complexity due to the required optimization of objective functions, the handling of
various temporal constraints, the concurrent execution of multiple process instances, the management of crossinstance
constraints, and complex resource allocations. Consequently, advanced user support through optimized
schedules is required when executing the instances of such models. In previous work, we suggested a method for
generating an optimized enactment plan for a given set of process instances created from a TConDec-R model.
However, this approach was not applicable to scenarios with uncertain demands in which the enactment of
newly created process instances starts continuously over time, as in the considered healthcare scenarios. Here,
the process instances to be planned within a specific timeframe cannot be considered in isolation from the ones
planned for future timeframes. To be able to support such scenarios, this article significantly extends our previous
work by generating optimized enactment plans under a rolling planning horizon. We evaluate the approach
by applying it to a particularly challenging healthcare process scenario, i.e., the diagnostic procedures required
for treating patients with ovarian carcinoma in a Woman Hospital. The application of the approach to this sophisticated
scenario allows avoiding constraint violations and effectively managing shared resources, which
contributes to reduce the length of patient stays in the hospital.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Ciencia e Innovación PID2019-105455 GB-C3
Generating Multi-objective Optimized Configurable Business Process Models
The manual specification of imperative business
process (BP) models can be very complex and time-consuming,
potentially leading to non-optimized models or even errors. To
support process analysts in the definition of these models, a
method for generating optimized configurable BP models from a
constraint-based specification by considering multiple objectives
is described. A constraint-based specification typically allows
for several different ways of executing it leading to several
enactment plans which can, however, vary greatly in respect
to how well different performance objective functions can be
achieved.We therefore automatically generate different plans and
select the ones which fit best the objectives of the company. The
generated plans are then merged into an optimized configurable
BP model to support the model expert in choosing the most
appropriate plan depending on the importance of each objective
at configuration time.Ministerio de Ciencia e Innovación TIN2009-1371
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 Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-RED
Time Prediction on Multi-perspective Declarative Business Processes
Process-aware information systems (PAISs) are increasingly used to provide flexible
support for business processes. The support given through a PAIS is greatly enhanced
when it is able to provide accurate time predictions which is typically a very challenging
task. Predictions should be (1) multi-dimensional and (2) not based on a single process
instance. Furthermore, the prediction system should be able to (3) adapt to changing
circumstances and (4) deal with multi-perspective declarative languages (e.g., models
which consider time, resource, data and control flow perspectives). In this work, a novel
approach for generating time predictions considering the aforementioned characteristics is
proposed. For this, first, a multi-perspective constraint-based language is used to model the
scenario. Thereafter, an optimized enactment plan (representing a potential execution
alternative) is generated from such a model considering the current execution state of the
process instances. Finally, pre-dictions are performed by evaluating a desired function over
this enactment plan. To evaluate the applicability of our approach in practical settings we
apply it to a real process scenario. Despite the high complexity of the considered problems,
results indicate that our approach produces a satisfactory number of good predictions in a
reasonable time.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Economía y Competitividad TIN2015-71618-
Towards a new Tool for Managing Declarative Temporal Business Process Models
Business processes which require a high flexibility are com- monly specified in a declarative (e.g., constraint-based) way. In general, offering operational support (e.g., generating possible execution traces) to declarative business process models entails more complexity when compared to imperative modeling alternatives. Such support becomes even more complex in many real scenarios where the management of complex temporal relations between the process activities is crucial (i.e., the temporal perspective should be managed). Despite the needs for enabling process flexibility and dealing with temporal constraints, most existing tools are unable to manage both. In a previous work, we then proposed TConDec-R, which is a constraint-based process modeling lan- guage which allows for the specification of temporal constraints. In this paper we introduce the basis and a prototype of a constraint-based tool with a client/server architecture for providing operational support to TConDec-R process models.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-
A canonical theory of dynamic decision-making
Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering
Automatic Generation of Questionnaires for Supporting Users during the Execution of Declarative Business Process Models
When designing an imperative business process (BP) model,
analysts have to face many design requirements (e.g., managing uncertainty,
optimizing conflicting objective functions). To facilitate such
design, declarative BP models are increasingly used. However, how to
execute a given declarative model can be quite challenging since there are
typically several variants related to such model, each one presenting
different degree of goodness. To support users working on declarative
models while a high flexibility is maintained, we propose removing the
worst variants from the source declarative model at design time while
keeping the best variants. This way, the variants which are kept are narrowed
down incrementally during run-time. For managing these variants
during run-time we suggest to build upon configurable BP models. To
configure such models, we additionally propose to automatically generate
questionnaires. The results over a real case study are promising
Conformance checking and diagnosis for declarative business process models in data-aware scenarios
A business process (BP) consists of a set of activities which are performed in coordination in an organizational
and technical environment and which jointly realize a business goal. In such context, BP management
(BPM) can be seen as supporting BPs using methods, techniques, and software in order to design,
enact, control, and analyze operational processes involving humans, organizations, applications, and
other sources of information. Since the accurate management of BPs is receiving increasing attention,
conformance checking, i.e., verifying whether the observed behavior matches a modelled behavior, is
becoming more and more critical. Moreover, declarative languages are more frequently used to provide
an increased flexibility. However, whereas there exist solid conformance checking techniques for imperative
models, little work has been conducted for declarative models. Furthermore, only control-flow perspective
is usually considered although other perspectives (e.g., data) are crucial. In addition, most
approaches exclusively check the conformance without providing any related diagnostics. To enhance
the accurate management of flexible BPs, this work presents a constraint-based approach for conformance
checking over declarative BP models (including both control-flow and data perspectives). In addition,
two constraint-based proposals for providing related diagnosis are detailed. To demonstrate both
the effectiveness and the efficiency of the proposed approaches, the analysis of different performance
measures related to a wide diversified set of test models of varying complexity has been performed.Ministerio de Ciencia e Innovación TIN2009-1371
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