3,339 research outputs found
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
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
What Automated Planning Can Do for Business Process Management
Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle
An Entry Point for Formal Methods: Specification and Analysis of Event Logs
Formal specification languages have long languished, due to the grave
scalability problems faced by complete verification methods. Runtime
verification promises to use formal specifications to automate part of the more
scalable art of testing, but has not been widely applied to real systems, and
often falters due to the cost and complexity of instrumentation for online
monitoring. In this paper we discuss work in progress to apply an event-based
specification system to the logging mechanism of the Mars Science Laboratory
mission at JPL. By focusing on log analysis, we exploit the "instrumentation"
already implemented and required for communicating with the spacecraft. We
argue that this work both shows a practical method for using formal
specifications in testing and opens interesting research avenues, including a
challenging specification learning problem
Declarative process modeling in BPMN
Traditional business process modeling notations, including the standard Business Process Model and Notation (BPMN), rely on an imperative paradigm wherein the process model captures all allowed activity flows. In other words, every flow that is not specified is implicitly disallowed. In the past decade, several researchers have exposed the limitations of this paradigm in the context of business processes with high variability. As an alternative, declarative process modeling notations have been proposed (e.g., Declare). These notations allow modelers to capture constraints on the allowed activity flows, meaning that all flows are allowed provided that they do not violate the specified constraints. Recently, it has been recognized that the boundary between imperative and declarative process modeling is not crisp. Instead, mixtures of declarative and imperative process modeling styles are sometimes preferable, leading to proposals for hybrid process modeling notations. These developments raise the question of whether completely new notations are needed to support hybrid process modeling. This paper answers this question negatively. The paper presents a conservative extension of BPMN for declarative process modeling, namely BPMN-D, and shows that Declare models can be transformed into readable BPMN-D models. © Springer International Publishing Switzerland 2015
Parametric Trace Slicing
A program trace is obtained and events of the program trace are traversed. For each event identified in traversing the program trace, a trace slice of which the identified event is a part is identified based on the parameter instance of the identified event. For each trace slice of which the identified event is a part, the identified event is added to an end of a record of the trace slice. These parametric trace slices can be used in a variety of different manners, such as for monitoring, mining, and predicting
COST Action IC 1402 ArVI: Runtime Verification Beyond Monitoring -- Activity Report of Working Group 1
This report presents the activities of the first working group of the COST
Action ArVI, Runtime Verification beyond Monitoring. The report aims to provide
an overview of some of the major core aspects involved in Runtime Verification.
Runtime Verification is the field of research dedicated to the analysis of
system executions. It is often seen as a discipline that studies how a system
run satisfies or violates correctness properties. The report exposes a taxonomy
of Runtime Verification (RV) presenting the terminology involved with the main
concepts of the field. The report also develops the concept of instrumentation,
the various ways to instrument systems, and the fundamental role of
instrumentation in designing an RV framework. We also discuss how RV interplays
with other verification techniques such as model-checking, deductive
verification, model learning, testing, and runtime assertion checking. Finally,
we propose challenges in monitoring quantitative and statistical data beyond
detecting property violation
Predictive Monitoring of Business Processes
Modern information systems that support complex business processes generally
maintain significant amounts of process execution data, particularly records of
events corresponding to the execution of activities (event logs). In this
paper, we present an approach to analyze such event logs in order to
predictively monitor business goals during business process execution. At any
point during an execution of a process, the user can define business goals in
the form of linear temporal logic rules. When an activity is being executed,
the framework identifies input data values that are more (or less) likely to
lead to the achievement of each business goal. Unlike reactive compliance
monitoring approaches that detect violations only after they have occurred, our
predictive monitoring approach provides early advice so that users can steer
ongoing process executions towards the achievement of business goals. In other
words, violations are predicted (and potentially prevented) rather than merely
detected. The approach has been implemented in the ProM process mining toolset
and validated on a real-life log pertaining to the treatment of cancer patients
in a large hospital
Semantical Vacuity Detection in Declarative Process Mining
A large share of the literature on process mining based on declarative process modeling languages, like declare, relies on the notion of constraint activation to distinguish between the case in which a process execution recorded in event data “vacuously” satisfies a constraint, or satisfies the constraint in an “interesting way”. This finegrained indicator is then used to decide whether a candidate constraint supported by the analyzed event log is indeed relevant or not. Unfortunately, this notion of relevance has never been formally defined, and all the proposals existing in the literature use ad-hoc definitions that are only applicable to a pre-defined set of constraint patterns. This makes existing declarative process mining technique inapplicable when the target constraint language is extensible and may contain formulae that go beyond pre-defined patterns. In this paper, we tackle this hot, open challenge and show how the notion of constraint activation and vacuous satisfaction can be captured semantically, in the case of constraints expressed in arbitrary temporal logics over finite traces. We then extend the standard automata-based approach so as to incorporate relevance-related information. We finally report on an implementation and experimentation of the approach that confirms the advantages and feasibility of our solution
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