Relevant bibliographies by topics / Event-Chain

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Event-Chain'

Author: Grafiati
Published: 30 November 2024
Last updated: 5 July 2025

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Journal articles on the topic "Event-Chain"

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Heusler, Klaus Felix, Wolfgang Stölzle, and Harald Bachmann. "Supply Chain Event Management." WiSt - Wirtschaftswissenschaftliches Studium 35, no. 1 (2006): 19–24. http://dx.doi.org/10.15358/0340-1650-2006-1-19.

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Nissen, Volker. "Supply Chain Event Management." Wirtschaftsinformatik 44, no. 5 (2002): 477–80. http://dx.doi.org/10.1007/bf03250869.

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Sangeetha, S., R. S. Thakur, and Michael Arock. "Event detection using trigger chain." International Journal of Knowledge Engineering and Data Mining 2, no. 1 (2012): 76. http://dx.doi.org/10.1504/ijkedm.2012.044708.

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Barclay, Lorna M., Rodrigo A. Collazo, Jim Q. Smith, Peter A. Thwaites, and Ann E. Nicholson. "The dynamic chain event graph." Electronic Journal of Statistics 9, no. 2 (2015): 2130–69. http://dx.doi.org/10.1214/15-ejs1068.

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Stölzle, Wolfgang. "Kommentar zum Supply Chain Event Management." Die Unternehmung 65, no. 2 (2011): 130–32. http://dx.doi.org/10.5771/0042-059x-2011-2-130.

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Otto, Andreas. "Supply Chain Event Management: Three Perspectives." International Journal of Logistics Management 14, no. 2 (2003): 1–13. http://dx.doi.org/10.1108/09574090310806567.

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Supply Chain Event Management (SCEM) addresses a fundamental business problem: inter‐organizational processes rarely execute as scheduled, since they happen in an environment prone to failure and disturbance. SCEM attempts to identify, as early as possible, the resulting deviations between the plan and its execution across the multitude of processes and actors in the supply chain to trigger corrective actions according to predefined rules. Despite SCEM's well documented attractiveness for practitioners, it has received little attention as a field of academic research. This paper provides an introduction into SCEM from three complementary perspectives: SCEM as a management concept; as a software solution; and as a software component. Each is analyzed in detail and potential fields of research on SCEM are presented.
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Barclay, Lorna M., Jane L. Hutton, and Jim Q. Smith. "Chain Event Graphs for Informed Missingness." Bayesian Analysis 9, no. 1 (2014): 53–76. http://dx.doi.org/10.1214/13-ba843.

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Smith, Jim Q., and Paul E. Anderson. "Conditional independence and chain event graphs." Artificial Intelligence 172, no. 1 (2008): 42–68. http://dx.doi.org/10.1016/j.artint.2007.05.004.

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Thwaites, Peter, Jim Q. Smith, and Eva Riccomagno. "Causal analysis with Chain Event Graphs." Artificial Intelligence 174, no. 12-13 (2010): 889–909. http://dx.doi.org/10.1016/j.artint.2010.05.004.

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Thwaites, Peter. "Causal identifiability via Chain Event Graphs." Artificial Intelligence 195 (February 2013): 291–315. http://dx.doi.org/10.1016/j.artint.2012.09.003.

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Dissertations / Theses on the topic "Event-Chain"

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Collazo, Rodrigo A. "The dynamic chain event graph." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/91075/.

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The Chain Event Graph (CEG) is a type of tree-based graphical model that accommodates all discrete Bayesian Networks as a particular subclass. It has already been successfully used to capture context-specific conditional independence structures of highly asymmetric processes in a way easily appreciated by domain experts. Being built from a tree, a CEG has a huge number of free parameters that makes the class extremely expressive but also very large. Exploring the enormous CEG model space then makes it necessary to design bespoke algorithms for this purpose. All Bayesian algorithms for CEG model selection in the literature are based on the Dirichlet characterisation of a family of CEGs spanned by a single event tree. Here I generalise this framework for a CEG model space spanned by a collection of different event trees. A new concept called hyper-stage is also introduced and provides us with a framework to design more efficient algorithms. These improvements are nevertheless insufficient to scale up the model search for more challenging applications. In other contexts, recent analyses of Bayes Factor model selection using conjugate priors have suggested that the use of such prior settings tends to choose models that are not sufficiently parsimonious. To sidestep this phenomenon, non-local priors (NLPs) have been successfully developed. These priors enable the fast identification of the simpler model when it really does drive the data generation process. In this thesis, I define three new families of NLPs designed to be applied specifically to discrete processes defined through trees. In doing this, I develop a framework for a CEG model search which appears to be both robust and computationally efficient. Finally, I define a Dynamic Chain Event Graph (DCEG). I develop object-recursive methods to fully analyse a particularly useful and feasibly implementable new subclass of these models called the N Time-Slice DCEG (NT-DCEG). By exploiting its close links with the Dynamic Bayesian Network I show how the NT-DCEG can be used to depict various structural and Granger causal hypotheses about a studied process. I also show how to construct from the topology of this graph intrinsic random variables which exhibit context-specific independences that can then be checked by domain experts. Throughout the thesis my methods are illustrated using examples of multivariate processes describing inmate radicalisation in a prison, and survey data concerning childhood hospitalisation and booking a tourist train.
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Thwaites, Peter. "Chain event graphs : theory and application." Thesis, University of Warwick, 2008. http://wrap.warwick.ac.uk/49194/.

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This thesis is concerned with the Graphical model known as the Chain Event Graph (CEG) [1][60][61], and develops the theory that appears in the currently published papers on this work. Results derived are analogous to those produced for Bayesian Networks (BNs), and I show that for asymmetric problems the CEG is generally superior to the BN both as a representation of the problem and as an analytical tool. The CEG is designed to embody the conditional independence structure of problems whose state spaces are asymmetric and do not admit a natural Product Space structure. In this they differ from BNs and other structures with variable-based topologies. Chapter 1 details researchers' attempts to adapt BNs to model such problems, and outlines the advantages CEGs have over these adaptations. Chapter 2 describes the construction of CEGs. In chapter 3I create a semantic structure for the reading of CEGs, and derive results expressible in the form of context-specific conditional independence statements, that allow us to delve much more deeply into the independence structure of a problem than we can do with BNs. In chapter 4I develop algorithms for the updating of a CEG following observation of an event, analogous to the Local Message Passing algorithms used with BNs. These are more efficient than the BN-based algorithms when used with asymmetric problems. Chapter 5 develops the theory of Causal manipulation of CEGs, and introduces the singular manipulation, a class of interventions containing the set of interventions possible with BNs. I produce Back Door and Front Door Theorems analogous to those of Pearl [42], but more flexible as they allow asymmetric manipulations of asymmetric problems. The ideas and results of chapters 2 to 5 are summarised in chapter 6.
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Freeman, Guy. "Learning and predicting with chain event graphs." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/4529/.

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Graphical models provide a very promising avenue for making sense of large, complex datasets. The most popular graphical models in use at the moment are Bayesian networks (BNs). This thesis shows, however, they are not always ideal factorisations of a system. Instead, I advocate for the use of a relatively new graphical model, the chain event graph (CEG), that is based on event trees. Event trees directly represent graphically the event space of a system. Chain event graphs reduce their potentially huge dimensionality by taking into account identical probability distributions on some of the event tree’s subtrees, with the added benefits of showing the conditional independence relationships of the system — one of the advantages of the Bayesian network representation that event trees lack — and implementation of causal hypotheses that is just as easy, and arguably more natural, than is the case with Bayesian networks, with a larger domain of implementation using purely graphical means. The trade-off for this greater expressive power, however, is that model specification and selection are much more difficult to undertake with the larger set of possible models for a given set of variables. My thesis is the first exposition of how to learn CEGs. I demonstrate that not only is conjugate (and hence quick) learning of CEGs possible, but I characterise priors that imply conjugate updating based on very reasonable assumptions that also have direct Bayesian network analogues. By re-casting CEGs as partition models, I show how established partition learning algorithms can be adapted for the task of learning CEGs. I then develop a robust yet flexible prediction machine based on CEGs for any discrete multivariate time series — the dynamic CEG model — which combines the power of CEGs, multi-process and steady modelling, lattice theory and Occam’s razor. This is also an exact method that produces reliable predictions without requiring much a priori modelling. I then demonstrate how easily causal analysis can be implemented with this model class that can express a wide variety of causal hypotheses. I end with an application of these techniques to real educational data, drawing inferences that would not have been possible simply using BNs.
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Albors, Marques Laura, and Jagathishvar Jayakumar. "Discrete Event Simulation for Aftermarket Supply Chain." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280678.

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The planning of an Aftermarket Supply Chain is a very complex task. This is due to an unpredictable demand which is driven by the need for maintenance and repair. This drive translates to a high variety of lead times, a large number of stock-keeping units (SKUs) and the capacity to deliver spare parts during its full lifecycle. With all these complexities in place, optimizing and parametrizing the planning process is a difficult and time-consuming task. Moreover, the current optimization tool focuses only on one node (each warehouse individually) of the whole Supply Chain, without considering the information such as inventory levels of the other nodes. Hence, the Supply Chain is not completely connected, making it difficult to get a better understanding of the system performance to identify cost draining areas. This leads to capital being tied up in the upper stream of the Supply Chain and later adding unnecessary costs like high inventory costs, rush freight costs, return or scrapping cost. In this study, Discrete Event Simulation (DES) is explored as an additional optimization tool that could analyse and improve the performance of the whole Supply Chain. To do that, the functioning of a node is modelled by replicating the logics behind the flow of material, which includes analysing some manual workflows which are currently present. In Addition, all the information needed from the orders, order lines and parts are mapped. The later part of the study aims to connect all the nodes to form a whole overview of the Supply Chain and further perform optimizations globally.  As an outcome, Multi-Echelon Inventory Optimization has been performed on the whole Supply Chain after connecting all the nodes and thus getting an overview. Furthermore, the impact of different parameters has been studied on the whole model to understand the sensitivity of parameters such as variations in lead time and demand. Finally, different what-if scenarios such as COVID and problems with delay in suppliers were studied, which could help understand the impact of unforeseen situations.<br>Planeringen av en eftermarknadskedja är en mycket komplex uppgift. Detta beror på en oförutsägbar efterfrågan som drivs av behovet av underhåll och reparation. Enheten översätter till många olika ledtider, ett stort antal lagerhållningsenheter (SKU) och kapacitet att leverera reservdelar under hela dess livscykel. Med alla dessa komplexiteter på plats är optimering och parametrering av planeringsprocessen en svår och tidskrävande uppgift. Dessutom fokuserar det nuvarande optimeringsverktyget bara på en nod (varje lager separat) i hela leveranskedjan utan att beakta informationen som lagernivåerna för de andra noderna. Därför är försörjningskedjan inte helt ansluten, vilket gör det svårt att få en bättre förståelse för systemets prestanda för att identifiera kostnadsavtappningsområden. Detta leder till att kapital binds i den övre strömmen i försörjningskedjan och senare lägger till onödiga kostnader som höga lagerkostnader, snabba fraktkostnader, retur- eller skrotningskostnader. I denna studie undersöks Discrete Event Simulation (DES) som ett ytterligare optimeringsverktyg som kan analysera och förbättra prestanda för hela försörjningskedjan. För att göra det modelleras en nods funktion genom att replikera logiken bakom materialflödet, vilket inkluderar analys av några manuella arbetsflöden som för närvarande finns. Dessutom kartläggs all information som behövs från beställningar, orderrader och delar. Den senare delen av studien syftar till att ansluta alla noder för att bilda en hel översikt över försörjningskedjan och ytterligare utföra optimeringar globalt. Som ett resultat har Multi-Echelon Lageroptimering utförts i hela försörjningskedjan efter att alla noder har anslutits och därmed fått en översikt. Dessutom har effekterna av olika parametrar studerats på hela modellen för att förstå känsligheten hos parametrar som variationer i ledtid och efterfrågan. Slutligen studerades olika tänkbara scenarier som COVID och problem med förseningar hos leverantörer, vilket kan hjälpa till att förstå effekterna av oförutsedda situationer.
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Suzuki, Yuya. "Rare-event Simulation with Markov Chain Monte Carlo." Thesis, KTH, Matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138950.

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In this thesis, we consider random sums with heavy-tailed increments. By the term random sum, we mean a sum of random variables where the number of summands is also random. Our interest is to analyse the tail behaviour of random sums and to construct an efficient method to calculate quantiles. For the sake of efficiency, we simulate rare-events (tail-events) using a Markov chain Monte Carlo (MCMC) method. The asymptotic behaviour of sum and the maximum of heavy-tailed random sums is identical. Therefore we compare random sum and maximum value for various distributions, to investigate from which point one can use the asymptotic approximation. Furthermore, we propose a new method to estimate quantiles and the estimator is shown to be efficient.
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Gudmundsson, Thorbjörn. "Rare-event simulation with Markov chain Monte Carlo." Doctoral thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157522.

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Stochastic simulation is a popular method for computing probabilities or expecta- tions where analytical answers are difficult to derive. It is well known that standard methods of simulation are inefficient for computing rare-event probabilities and there- fore more advanced methods are needed to those problems. This thesis presents a new method based on Markov chain Monte Carlo (MCMC) algorithm to effectively compute the probability of a rare event. The conditional distri- bution of the underlying process given that the rare event occurs has the probability of the rare event as its normalising constant. Using the MCMC methodology a Markov chain is simulated, with that conditional distribution as its invariant distribution, and information about the normalising constant is extracted from its trajectory. In the first two papers of the thesis, the algorithm is described in full generality and applied to four problems of computing rare-event probability in the context of heavy- tailed distributions. The assumption of heavy-tails allows us to propose distributions which approximate the conditional distribution conditioned on the rare event. The first problem considers a random walk Y1 + · · · + Yn exceeding a high threshold, where the increments Y are independent and identically distributed and heavy-tailed. The second problem is an extension of the first one to a heavy-tailed random sum Y1+···+YN exceeding a high threshold,where the number of increments N is random and independent of Y1 , Y2 , . . .. The third problem considers the solution Xm to a stochastic recurrence equation, Xm = AmXm−1 + Bm, exceeding a high threshold, where the innovations B are independent and identically distributed and heavy-tailed and the multipliers A satisfy a moment condition. The fourth problem is closely related to the third and considers the ruin probability for an insurance company with risky investments. In last two papers of this thesis, the algorithm is extended to the context of light- tailed distributions and applied to four problems. The light-tail assumption ensures the existence of a large deviation principle or Laplace principle, which in turn allows us to propose distributions which approximate the conditional distribution conditioned on the rare event. The first problem considers a random walk Y1 + · · · + Yn exceeding a high threshold, where the increments Y are independent and identically distributed and light-tailed. The second problem considers a discrete-time Markov chains and the computation of general expectation, of its sample path, related to rare-events. The third problem extends the the discrete-time setting to Markov chains in continuous- time. The fourth problem is closely related to the third and considers a birth-and-death process with spatial intensities and the computation of first passage probabilities. An unbiased estimator of the reciprocal probability for each corresponding prob- lem is constructed with efficient rare-event properties. The algorithms are illustrated numerically and compared to existing importance sampling algorithms.<br><p>QC 20141216</p>
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Fantozzi, Esmè 1968. "A strategic approach to supply chain event management." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/28565.

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Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2003.<br>Includes bibliographical references (p. 35).<br>This thesis project explores the possibility to apply project management techniques, specifically critical path method, and PERT, to supply chain event management. The idea behind the project is to create a framework for putting supply chain events into a broader supply chain context and assessing their criticality. Such a framework can then be utilized as a starting point for supply chain event management software applications. The problem has been approached from a "micro" point of view, with the analysis and PERT modeling of a single order fulfillment process, and from a "macro" point of view, with the analysis and a very simple model of the inventory itself. Finally, there are important factors that can drive the development and adoption of such systems in the future, including a higher level of supply chain informatization, removal of inter-and intra-company communication barriers, and better software integration technologies to effectively link all the element of the supply chain network.<br>by Esmè Fantozzi.<br>M.Eng.in Logistics
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Wiedenbruch, Alexander [Verfasser]. "A Modeling Language for Supply Chain Event Management / Alexander Wiedenbruch." München : Verlag Dr. Hut, 2014. http://d-nb.info/1047994801/34.

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Barclay, Lorna M. "Modelling and reasoning with chain event graphs in health studies." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/63223/.

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The Chain Event Graph (CEG) is a new class of graphical model, first introduced in Smith and Anderson [2008], which is derived from a probability tree by merging vertices whose associated conditional probabilities are the same. It is proving to be a useful framework for modelling asymmetric problems and further generalises the Bayesian Network (BN), by allowing for context-specific dependence structures between the variables of the problem. This thesis provides a first demonstration of the value of using the CEG in real-world applications and the new techniques developed here are motivated by problems that arise from two health studies; the Christchurch Health and Development Study (CHDS) and the UK Cerebral Palsy (UKCP) Cohort Study. A direct comparison of the BN and CEG on the CHDS demonstrates that the CEG can lead to significantly higher scoring models than the BN and further that it enables additional conclusions to be drawn on the health study directly from the topology of its graph. An extension of the CEG, the Ordinal CEG, is developed in this thesis, which further enhances the graphical representation of the CEGs for studies with a binary outcome. Motivated by the UKCP this thesis further investigates how missing data structures can be explicitly represented by a CEG and how its graph can consequently provide a precise understanding of the influence of missingness. Finally, a dynamic version of the CEG is developed and it is demonstrated how this new class of models generalises the Dynamic BN and is further closely linked to (semi-) Markov processes. The expressiveness of this model is illustrated through a fictional example.
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Mendling, Jan, and Gustaf Neumann. "Yet Another Event-driven Process Chain - Modelling Workflow Patterns with yEPCs." Gesellschaft für Informatik e.V, 2005. http://epub.wu.ac.at/6018/1/Mendling_etal_2005_EMISA_Yet%2DAnother%2DEvent%2Ddriven.pdf.

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The 20 workflow patterns proposed by van der Aalst et al. provide a comprehensive benchmark for comparing process modelling languages. In this article, we discuss workflow pattern support of Event-Driven Process Chains (EPCs). Building on this analysis, we propose three extensions to EPCs in order to provide for workflow pattern support. These are the introduction of the so-called empty connector; inclusion of multiple instantiation concepts; and a cancellation construct. As both the latter are inspired by YAWL, we refer to this new class of EPCs as Yet Another Event-driven Process Chain (yEPC). Furthermore, we sketch how a transformation to YAWL can be used to specify the semantics of yEPCs.
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Books on the topic "Event-Chain"

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Hunewald, Christian. Supply Chain Event Management. Deutscher Universitätsverlag, 2005. http://dx.doi.org/10.1007/978-3-322-81251-3.

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Ijioui, Raschid, Heike Emmerich, and Michael Ceyp, eds. Supply Chain Event Management. Physica-Verlag HD, 2007. http://dx.doi.org/10.1007/978-3-7908-1740-9.

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Ijioui, Raschid, Heike Emmerich, and Michael Ceyp, eds. Strategies and Tactics in Supply Chain Event Management. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-73766-7.

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Raschid, Ijioui, Emmerich Heike 1969-, and Ceyp Michael H. 1965-, eds. Strategies and tactics in supply chain event management. Springer, 2008.

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Schmidt, Dirk. RFID im Mobile Supply Chain Event Management: Anwendungsszenarien, Verbreitung und Wirtschaftlichkeit. Betriebswirtschaftlicher Verlag Gabler, 2006.

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Collazo, Rodrigo A., Christiane Görgen, and Jim Q. Smith. Chain Event Graphs. CRC Press, 2018. http://dx.doi.org/10.1201/9781315120515.

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Collazo, Rodrigo A., Christiane Goergen, and Jim Q. Smith. Chain Event Graphs. Taylor & Francis Group, 2018.

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Chain Event Graphs. Taylor & Francis Group, 2018.

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Collazo, Rodrigo A., Christiane Goergen, and Jim Q. Smith. Chain Event Graphs. Taylor & Francis Group, 2018.

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Collazo, Rodrigo A., Christiane Goergen, and Jim Q. Smith. Chain Event Graphs. Taylor & Francis Group, 2018.

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Book chapters on the topic "Event-Chain"

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Hunewald, Christian. "Supply Chain Event Management." In Supply Chain Event Management. Deutscher Universitätsverlag, 2005. http://dx.doi.org/10.1007/978-3-322-81251-3_3.

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Hunewald, Christian. "Einleitung." In Supply Chain Event Management. Deutscher Universitätsverlag, 2005. http://dx.doi.org/10.1007/978-3-322-81251-3_1.

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Hunewald, Christian. "Unternehmen im Wandel." In Supply Chain Event Management. Deutscher Universitätsverlag, 2005. http://dx.doi.org/10.1007/978-3-322-81251-3_2.

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Hunewald, Christian. "SCEM in einem Modellunternehmen der Automobilzuliefererindustrie." In Supply Chain Event Management. Deutscher Universitätsverlag, 2005. http://dx.doi.org/10.1007/978-3-322-81251-3_4.

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Hunewald, Christian. "Schlussbetrachtung." In Supply Chain Event Management. Deutscher Universitätsverlag, 2005. http://dx.doi.org/10.1007/978-3-322-81251-3_5.

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van Bonn, Bernhard. "Mehrwert durch Outsourcing von Supply Chain Event Management (SCEM)-Lösungen." In Supply Chain Event Management. Physica-Verlag HD, 2007. http://dx.doi.org/10.1007/978-3-7908-1740-9_7.

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Davis, Rob. "The Event-Driven Process Chain." In Business Process Modelling with ARIS: A Practical Guide. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0321-9_7.

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S., Sangeetha, R. S. Thakur, and Michael Arock. "Event Detection Using Lexical Chain." In Advances in Natural Language Processing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14770-8_35.

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Liu, Maofu, Wenjie Li, Xiaolong Zhang, and Ji Zhang. "Event-Based Summarization Using Critical Temporal Event Term Chain." In Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00831-3_38.

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Mendling, Jan, Gustaf Neumann, and Markus Nüttgens. "Yet Another Event-Driven Process Chain." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538394_35.

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Conference papers on the topic "Event-Chain"

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Peng, Lei, Dejuan Chen, and Tingfeng Gu. "Application Research of Security Analysis Technology Based on Event Chain." In 2023 14th International Conference on Reliability, Maintainability and Safety (ICRMS). IEEE, 2023. http://dx.doi.org/10.1109/icrms59672.2023.00076.

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Straube, Frank, Stefan Vogeler, and Philipp Bensel. "RFID-based Supply Chain Event Management." In 2007 1st Annual RFID Eurasia. IEEE, 2007. http://dx.doi.org/10.1109/rfideurasia.2007.4368115.

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Lu, Zhongyu, Weiren Yu, Richong Zhang, Jianxin Li, and Hua Wei. "Discovering Event Evolution Chain in Microblog." In 2015 IEEE 17th International Conference on High-Performance Computing and Communications; 2015 IEEE 7th International Symposium on Cyberspace Safety and Security; and 2015 IEEE 12th International Conference on Embedded Software and Systems. IEEE, 2015. http://dx.doi.org/10.1109/hpcc-css-icess.2015.81.

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"CHAIN EVENT GRAPH MAP MODEL SELECTION." In International Conference on Knowledge Engineering and Ontology Development. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0002292403920395.

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Yinhug Qin, Quanguang Guan, and Hong Zheng. "Supply chain event management in multimodal transportation." In 2008 IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2008. http://dx.doi.org/10.1109/soli.2008.4682787.

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Jiao, Yizhu, Ming Zhong, Jiaming Shen, Yunyi Zhang, Chao Zhang, and Jiawei Han. "Unsupervised Event Chain Mining from Multiple Documents." In WWW '23: The ACM Web Conference 2023. ACM, 2023. http://dx.doi.org/10.1145/3543507.3583295.

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Vlachakis, Georgios, and Dimitris Apostolou. "An event-based framework for supply chain management." In 2014 5th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2014. http://dx.doi.org/10.1109/iisa.2014.6878724.

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Saksupawattanakul, Chalika, and Wiwat Vatanawood. "Event-B Formalization of Basic Supply Chain Patterns." In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2018. http://dx.doi.org/10.1109/snpd.2018.8441070.

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Zhang, Xiyang, Muhao Chen, and Jonathan May. "Salience-Aware Event Chain Modeling for Narrative Understanding." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.107.

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He, Donglin, Lingzhu Deng, Yunfeng Sui, Shixuan Zhao, Weiqian Liu, and Zhi Cheng. "Latent Markov Chain Encoding for Abnormal Landing Event Detection." In 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE). IEEE, 2019. http://dx.doi.org/10.1109/eitce47263.2019.9094793.

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Reports on the topic "Event-Chain"

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Lindquist, Joachim, and Henning de Haas. Creating Supply Chain Resilience Through Scenario Planning: How a Digital Twin Can Be Used To Enhance Supply Chain Resilience Through Scenario Planning. Aarhus University Library, 2021. http://dx.doi.org/10.7146/aul.435.

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This book focusses on the concept of supply chain disruptions and how supply chain resilience can contribute to both preparing for and reacting to the event causing disruption. For building a digital twin of a supply chain, a software named Supply Chain Guru has been used. The software is a supply chain design tool which can be used for different kinds of supply chain network optimisation. The book outlines four scenarios: Covid-19 lockdown, Brexit without deal, Conflagration at a dairy and Political regulations on transport. The scenarios all contain a problem that needs to be solved. This problem is considered as the main disruption for the supply chain. Running the scenario in Supply Chain Guru, constraints are added to the AS-IS model. The constraints are identified as implications of the event in the scenarios. By adding the constraints and running the model, Supply Chain Guru identifies suggestions to solve the problems which were described. The solutions within the scenarios are held up against the theory of supply chain resilience, to describe how the scenario planning can be used to enhance supply chain resilience. Finally, the book discuss how scenario planning can be related to supply chain resilience as well as how scenario planning can be used to increase supply chain resilience.
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Rivadeneira, Alex. Public Transportation and Consumer Prices: Chain Stores, Street Vendors and Mom and Pop Stores. Banco de México, 2024. http://dx.doi.org/10.36095/banxico/di.2024.02.

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Improving public transport infrastructure changes local market conditions. In this paper, I examine the impact of the construction and operation of "Metrobus", Mexico City's Bus Rapid Transit (BRT) system on consumer prices in chain stores, street vendors, and small family-owned (mom and pop) stores. I do so through a panel event study design. I consider the construction and operation of BRT as two different phenomena; while the former is associated to street closures, the latter reduces transportation costs. I show that only prices in mom and pop stores respond to changes in local market conditions produced by the introduction of BRT. For these businesses, construction pressures prices downwards; in contrast, operation is associated with partial price recoveries. I cannot reject a null effect in prices from chain stores or street vendors.
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