20,003 research outputs found
Anti-alignments in conformance checking: the dark side of process models
Conformance checking techniques asses the suitability of a process model in representing an underlying process, observed through a collection of real executions. These techniques suffer from the wellknown state space explosion problem, hence handling process models exhibiting large or even infinite state spaces remains a challenge. One important metric in conformance checking is to asses the precision of the model with respect to the observed executions, i.e., characterize the ability of the model to produce behavior unrelated to the one observed. By avoiding the computation of the full state space of a model, current techniques only provide estimations of the precision metric, which in some situations tend to be very optimistic, thus hiding real problems a process model may have. In this paper we present the notion of antialignment as a concept to help unveiling traces in the model that may deviate significantly from the observed behavior. Using anti-alignments, current estimations can be improved, e.g., in precision checking. We show how to express the problem of finding anti-alignments as the satisfiability of a Boolean formula, and provide a tool which can deal with large models efficiently.Peer ReviewedPostprint (author's final draft
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
Alignment-free phylogenetic reconstruction: Sample complexity via a branching process analysis
We present an efficient phylogenetic reconstruction algorithm allowing
insertions and deletions which provably achieves a sequence-length requirement
(or sample complexity) growing polynomially in the number of taxa. Our
algorithm is distance-based, that is, it relies on pairwise sequence
comparisons. More importantly, our approach largely bypasses the difficult
problem of multiple sequence alignment.Comment: Published in at http://dx.doi.org/10.1214/12-AAP852 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
- …