876 research outputs found
Decomposed process discovery and conformance checking
Decomposed process discovery and decomposed conformance checking are the corresponding variants of the two monolithic fundamental problems in process mining (van der Aalst 2011): automated process discovery, which considers the problem of discovering a process model from an event log (Leemans 2009), and conformance checking, which addresses the problem of analyzing the adequacy of a process model with respect to observed behavior (Munoz-Gama 2009), respectively.
The term decomposed in the two definitions is mainly describing the way the two problems are tackled operationally, to face their computational complexity by splitting the initial problem into smaller problems, that can be solved individually and often more efficiently.Postprint (author's final draft
Passages in Graphs
Directed graphs can be partitioned in so-called passages. A passage P is a
set of edges such that any two edges sharing the same initial vertex or sharing
the same terminal vertex are both inside or are both outside of P. Passages
were first identified in the context of process mining where they are used to
successfully decompose process discovery and conformance checking problems. In
this article, we examine the properties of passages. We will show that passages
are closed under set operators such as union, intersection and difference.
Moreover, any passage is composed of so-called minimal passages. These
properties can be exploited when decomposing graph-based analysis and
computation problems.Comment: 8 page
On interoperability and conformance assessment in service composition
The process of composing a service from other services typically involves multiple models. These models may represent the service from distinct perspectives, e.g., to model the different roles of systems involved in the service, and at distinct abstraction levels, e.g., to model the service’s capability, interface or the orchestration that implements the service. The consistency among these models needs to be maintained in order to guarantee the correctness of the composition process. Two types of consistency relations are distinguished: interoperability, which concerns the ability of different roles to interoperate, and conformance, which concerns the correct implementation of an abstract model by a more concrete model. This paper discusses the need for and use of techniques to assess interoperability and conformance in a service composition process. The paper shows how these consistency relations can be described and analysed using concepts from the COSMO framework. Examples are presented to illustrate how interoperability and conformance can be assessed
Finding suitable activity clusters for decomposed process discovery
Event data can be found in any information system and provide the starting point for a range of process mining techniques. The widespread availability of large amounts of event data also creates new challenges. Existing process mining techniques are often unable to handle "big event data" adequately. Decomposed process mining aims to solve this problem by decomposing the process mining problem into many smaller problems which can be solved in less time, using less resources, or even in parallel. Many decomposed process mining techniques have been proposed in literature. Analysis shows that even though the decomposition step takes a relatively small amount of time, it is of key importance in finding a high-quality process model and for the computation time required to discover the individual parts. Currently there is no way to assess the quality of a decomposition beforehand. We define three quality notions that can be used to assess a decomposition, before using it to discover a model or check conformance with. We then propose a decomposition approach that uses these notions and is able to find a high-quality decomposition in little time. Keywords: decomposed process mining, decomposed process discovery, distributed computing, event lo
Decomposing conformance checking on Petri nets with data
Process mining techniques relate observed behavior to modeled behavior, e.g., the automatic discovery of a Petri net based on an event log. Process mining is not limited to process discovery and also includes conformance checking. Conformance checking techniques are used for evaluating the quality of discovered process models and to diagnose deviations from some normative model (e.g., to check compliance). Existing conformance checking approaches typically focus on the control flow, thus being unable to diagnose deviations concerning data. This paper proposes a technique to check the conformance of data-aware process models. We use so-called "data Petri nets" to model data variables, guards, and read/write actions. Additional perspectives such as resource allocation and time constraints can be encoded in terms of variables. Data-aware conformance checking problem may be very time consuming and sometimes even intractable when there are many transitions and data variables. Therefore, we propose a technique to decompose large data-aware conformance checking problems into smaller problems that can be solved more efficiently. We provide a general correctness result showing that decomposition does not influence the outcome of conformance checking. Moreover, two decomposition strategies are presented. The approach is supported through ProM plug-ins and experimental results show that significant performance improvements are indeed possible
A recursive paradigm for aligning observed behavior of large structured process models
The alignment of observed and modeled behavior is a crucial problem in process mining, since it opens the door for conformance checking and enhancement of process models. The state of the art techniques for the computation of alignments rely on a full exploration of the combination of the model state space and the observed behavior (an event log), which hampers their applicability for large instances. This paper presents a fresh view to the alignment problem: the computation of alignments is casted as the resolution of Integer Linear Programming models, where the user can decide the granularity of the alignment steps. Moreover, a novel recursive strategy is used to split
the problem into small pieces, exponentially reducing the complexity of the ILP models to be solved. The contributions of this paper represent a promising alternative to fight the inherent complexity of computing alignments for large instances.Peer ReviewedPostprint (author's final draft
Conformance checking: A state-of-the-art literature review
Conformance checking is a set of process mining functions that compare
process instances with a given process model. It identifies deviations between
the process instances' actual behaviour ("as-is") and its modelled behaviour
("to-be"). Especially in the context of analyzing compliance in organizations,
it is currently gaining momentum -- e.g. for auditors. Researchers have
proposed a variety of conformance checking techniques that are geared towards
certain process model notations or specific applications such as process model
evaluation. This article reviews a set of conformance checking techniques
described in 37 scholarly publications. It classifies the techniques along the
dimensions "modelling language", "algorithm type", "quality metric", and
"perspective" using a concept matrix so that the techniques can be better
accessed by practitioners and researchers. The matrix highlights the dimensions
where extant research concentrates and where blind spots exist. For instance,
process miners use declarative process modelling languages often, but
applications in conformance checking are rare. Likewise, process mining can
investigate process roles or process metrics such as duration, but conformance
checking techniques narrow on analyzing control-flow. Future research may
construct techniques that support these neglected approaches to conformance
checking
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