2,391 research outputs found
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges
The Internet of Things (IoT) refers to a network of connected devices
collecting and exchanging data over the Internet. These things can be
artificial or natural, and interact as autonomous agents forming a complex
system. In turn, Business Process Management (BPM) was established to analyze,
discover, design, implement, execute, monitor and evolve collaborative business
processes within and across organizations. While the IoT and BPM have been
regarded as separate topics in research and practice, we strongly believe that
the management of IoT applications will strongly benefit from BPM concepts,
methods and technologies on the one hand; on the other one, the IoT poses
challenges that will require enhancements and extensions of the current
state-of-the-art in the BPM field. In this paper, we question to what extent
these two paradigms can be combined and we discuss the emerging challenges
Analysis and Verification of Service Interaction Protocols - A Brief Survey
Modeling and analysis of interactions among services is a crucial issue in
Service-Oriented Computing. Composing Web services is a complicated task which
requires techniques and tools to verify that the new system will behave
correctly. In this paper, we first overview some formal models proposed in the
literature to describe services. Second, we give a brief survey of verification
techniques that can be used to analyse services and their interaction. Last, we
focus on the realizability and conformance of choreographies.Comment: In Proceedings TAV-WEB 2010, arXiv:1009.330
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
Scalable Online Conformance Checking Using Incremental Prefix-Alignment Computation
Conformance checking techniques aim to collate observed process behavior with
normative/modeled process models. The majority of existing approaches focuses
on completed process executions, i.e., offline conformance checking. Recently,
novel approaches have been designed to monitor ongoing processes, i.e., online
conformance checking. Such techniques detect deviations of an ongoing process
execution from a normative process model at the moment they occur. Thereby,
countermeasures can be taken immediately to prevent a process deviation from
causing further, undesired consequences. Most online approaches only allow to
detect approximations of deviations. This causes the problem of falsely
detected deviations, i.e., detected deviations that are actually no deviations.
We have, therefore, recently introduced a novel approach to compute exact
conformance checking results in an online environment. In this paper, we focus
on the practical application and present a scalable, distributed implementation
of the proposed online conformance checking approach. Moreover, we present two
extensions to said approach to reduce its computational effort and its
practical applicability. We evaluate our implementation using data sets
capturing the execution of real processes
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