2,862 research outputs found
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
A multi-agent simulation approach to sustainability in tourism development
In the last decades the increasing facility in moving and the simultaneous fall of the transportation costs have strongly increased the tourist flows. As a consequence, different destinations, especially those which are rich of natural resources, unable or unready to sustain huge tourism flows, present serious problems of sustainability and Tourism Carrying Capacity (TCC). At the present, it is universally recognized that every tourist destination should plan effective and pro-reactive protection policies of its cultural, environmental and social resources. In order to facilitate policies definition it may be useful to measure the Tourist Carrying Capacity, but the literature has highlighted that this is not an easy task for different reasons: among the others, the complexity and the dynamicity of the concept, the absence of a universally accepted definition and the impossibility of assigning an objective scientific value and to apply a rigorous analysis. Thereby, more recently an alternative, or even complementary, interpretation of TCC has developed; it is called LAC, Limit of Acceptable Changes where the focus shifts from: “How much use an area can tolerate?†to “How much change is acceptable?â€, aiming at evaluating the costs and benefits from alternative management tourism actions. The aim of the paper is to present an innovative framework, based on the LAC approach - MABSiT, Mobile Agent Behavior Simulation in Tourism - developed by the authors, which is composed by five modules: elaboration data, DBMS, ad-hoc maps, agents and ontology. Its modular structure allows to easily study the interactions among the components in order to observe the behavior of the single agents. In an aggregate form, it is possible to define group dynamics, where one possible effect is the influence on the variation of agents’ satisfaction perception in comparison to the surroundings environment. The paper will be structured as follows: an introduction will be followed by a literature review; than the methodology and the framework will be presented and applied to a case study: Vieste, a known maritime destination of South of Italy, which is characterized by high problems of seasonality in the summer. Finally, some conclusions and policy recommendations will be drawn.
From zero to hero: A process mining tutorial
Process mining is an emerging area that synergically combines model-based and data-oriented analysis techniques to obtain useful insights on how business processes are executed within an organization. This tutorial aims at providing an introduction to the key analysis techniques in process mining that allow decision makers to discover process models from data, compare expected and actual behaviors, and enrich models with key information about the actual process executions. In addition, the tutorial will present concrete tools and will provide practical skills for applying process mining in a variety of application domains, including the one of software development
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
LTLf and LDLf Monitoring: A Technical Report
Runtime monitoring is one of the central tasks to provide operational
decision support to running business processes, and check on-the-fly whether
they comply with constraints and rules. We study runtime monitoring of
properties expressed in LTL on finite traces (LTLf) and in its extension LDLf.
LDLf is a powerful logic that captures all monadic second order logic on finite
traces, which is obtained by combining regular expressions and LTLf, adopting
the syntax of propositional dynamic logic (PDL). Interestingly, in spite of its
greater expressivity, LDLf has exactly the same computational complexity of
LTLf. We show that LDLf is able to capture, in the logic itself, not only the
constraints to be monitored, but also the de-facto standard RV-LTL monitors.
This makes it possible to declaratively capture monitoring metaconstraints, and
check them by relying on usual logical services instead of ad-hoc algorithms.
This, in turn, enables to flexibly monitor constraints depending on the
monitoring state of other constraints, e.g., "compensation" constraints that
are only checked when others are detected to be violated. In addition, we
devise a direct translation of LDLf formulas into nondeterministic automata,
avoiding to detour to Buechi automata or alternating automata, and we use it to
implement a monitoring plug-in for the PROM suite
A multi-agent simulation approach to sustainability in tourism development
In the last decades the increasing facility in moving and the simultaneous fall of the transportation costs have strongly increased the tourist flows. As a consequence, different destinations, especially those which are rich of natural resources, unable or unready to sustain huge tourism flows, present serious problems of sustainability and Tourism Carrying Capacity (TCC). At the present, it is universally recognized that every tourist destination should plan effective and pro-reactive protection policies of its cultural, environmental and social resources. In order to facilitate policies definition it may be useful to measure the Tourist Carrying Capacity, but the literature has highlighted that this is not an easy task for different reasons: among the others, the complexity and the dynamicity of the concept, the absence of a universally accepted definition and the impossibility of assigning an objective scientific value and to apply a rigorous analysis. Thereby, more recently an alternative, or even complementary, interpretation of TCC has developed; it is called LAC, Limit of Acceptable Changes where the focus shifts from: "How much use an area can tolerate?" to "How much change is acceptable?", aiming at evaluating the costs and benefits from alternative management tourism actions. The aim of the paper is to present an innovative framework, based on the LAC approach - MABSiT, Mobile Agent Behavior Simulation in Tourism - developed by the authors, which is composed by five modules: elaboration data, DBMS, ad-hoc maps, agents and ontology. Its modular structure allows to easily study the interactions among the components in order to observe the behavior of the single agents. In an aggregate form, it is possible to define group dynamics, where one possible effect is the influence on the variation of agents' satisfaction perception in comparison to the surroundings environment. The paper will be structured as follows: an introduction will be followed by a literature review; than the methodology and the framework will be presented and applied to a case study: Vieste, a known maritime destination of South of Italy, which is characterized by high problems of seasonality in the summer. Finally, some conclusions and policy recommendations will be drawn
Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
A characteristic of existing predictive process monitoring techniques is to
first construct a predictive model based on past process executions, and then
use it to predict the future of new ongoing cases, without the possibility of
updating it with new cases when they complete their execution. This can make
predictive process monitoring too rigid to deal with the variability of
processes working in real environments that continuously evolve and/or exhibit
new variant behaviors over time. As a solution to this problem, we propose the
use of algorithms that allow the incremental construction of the predictive
model. These incremental learning algorithms update the model whenever new
cases become available so that the predictive model evolves over time to fit
the current circumstances. The algorithms have been implemented using different
case encoding strategies and evaluated on a number of real and synthetic
datasets. The results provide a first evidence of the potential of incremental
learning strategies for predicting process monitoring in real environments, and
of the impact of different case encoding strategies in this setting
How Current Direct-Acting Antiviral and Novel Cell Culture Systems for HCV are Shaping Therapy and Molecular Diagnosis of Chronic HCV Infection
We have entered a new era of hepatitis C virus (HCV) therapy in which elimination of infection and disease is a real possibility. HCV cell culture models were instrumental for identification of therapeutic targets, testing candidate drugs, and profiling of therapeutic strategies. Here we describe current and novel methods of cell culture systems for HCV that are allowing investigation of HCV life cycle and virus-host interaction required for replication and propagation. The development of protocols to grow infectious virus in culture and generate hepatocyte cell lines from specific individuals hold great promise to investigate the mechanisms exploited by the virus to spread the infection and the host factors critical for HCV replication and propagation, or resistance to infection. Since host factors are presumably conserved and equally interacting with different HCV isolates and genotypes, the development of drugs targeting host factors essential for virus replication holds great promises in further increasing treatment efficacy. Refocusing of therapeutic goals also impacted in vitro diagnosis. The primary goal of anti-HCV therapy is to achieve a sustained virologic response (SVR) defined as " undetectable" HCV RNA genome in the serum or plasma at 12 to 24 weeks following the end of treatment. Use of direct antiviral agents has substantially changed the threshold of the viral load used to define SVR and led to a reassessment, as discussed herein, of result interpretation and requirements of clinically-approved, quantitative molecular assays
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