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1 Mar 2012 ... I have a WPF 4.0 applications with a WebBrowser Control . When I navigate to a PDF on the pc, network or on the web it prompts me to open or ... data model depicting all of the data that fuels processes and that are outputs from processes is essential to precise management Being able to associate speci c data to speci c processes is essential to precise enterprise performance management Understanding the impact of business rules on processes and data is essential to precise enterprise management We reviewed our progress with Dan Appleton and he suggested that we consider a paper written by Mark Fox and Michael Gruninger called Enterprise Modeling that was published by the American Association for Arti cial Intelligence nearly 10 years ago [16] Why a 10 year-old paper, you might ask Sometimes work advances in spurts, and we think we have been marking time for 10 years when it comes to executives attending the subject So we will review what was said with the intent to see what needs to be updated and to share wisdom that should be preserved and applied Enterprise Modeling Here is an analysis of the subject paper as we were in communication with Mark Fox, Professor of Industrial Engineering at the Department of Computer Science at the University of Toronto Validating some of our ideas, the paper begins by acknowledging the need for competitive enterprise to be exceedingly agile and integrated Our difference on this view might be with the term integrated as we have discussed because it sometimes implies a degree of rigidity that con icts with the notion of agility The article intends to promote the need for enterprise models and introduces the ideas that models may be generic and deductive A generic enterprise model implies that businesses of a certain type, such as manufacturing, for instance, may appear to have a common construct shared among others in the same industry classi cation A deductive model is one that is derived from analysis and reasoning about how the enterprise operates We will consider these ideas further in this discussion as does the paper Something striking ccurred to us as we considered the notion of generic models: enterprise resource planning (ERP) software packages that have been popular for the past 10 years are based on the idea that industries of common types basically perform the same way, or furthermore, that they should perform the same way as software vendors develop solutions that are based on best practices Then again, one must ask: Does not all of that sameness undermine the notion of competitive advantage Wherein does competitive difference appear To answer these questions, one must (1) have a model of the enterprise processes fully attributed with inputs, outputs, control, and mechanisms; (2) have an enterprise data model; and (3) have additional information about work ow and timing states Herein, we are in agreement with Fox and Gruninger According to the authors, an enterprise model is a computational representation of the structure, activities, processes, information, resources, people, behavior, goals, and constraints of a business, government, or other enterprise We surely agree with that de nition but are attracted to the term computational . pdf viewer control without acrobat reader installed c#: Opening a PDF File from Asp . net page - Geekswithblogs.net how to open pdf file in web browser c# ASP . NET PDF Viewer - Stack Overflow
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In this section we discuss a generally applicable up- and downcrossing technique that, in onjunction with the PASTA property, can be used to establish relations between customer-average and time-average probabilities in queueing systems To illustrate this, we consider the so-called GI /M/1 queue In this single-server system, customers arrive according to a renewal process and the service times of the customers have a common exponential distribution The single server can handle only one customer at a time and there is ample waiting room for customers who nd the server busy upon arrival The service times of the customers are independent of each other and are also independent of the arrival process Denoting by the average arrival rate (1/ = the mean interarrival time) and by the service rate (1/ = the mean service time), it is assumed that < The continuous-time stochastic process {X(t), t 0} and the discrete-time stochastic process {Xn , n = 1, 2, } are de ned by X(t) = the number of customers present at time t, and Xn = the number of customers present just prior to the nth arrival epoch The stochastic processes {X(t)} and {Xn } are both regenerative The regeneration epochs are the epochs at which an arriving customer nds the system empty It is stated without proof that the assumption of / < 1 implies that the processes have a nite mean cycle length Thus we can de ne the time-average and the customer-average probabilities pj and j by pj = the long-run fraction of time that j customers are present and j = the long-run fraction of customers who nd j other customers present upon arrival for j = 0, 1, Time averages are averages over time, and customer averages t are averages over customers To be precise, pj = limt (1/t) 0 Ij (u) du and j = limn (1/n) n Ik (j ), where Ij (t) = 1 if j customers are present at k=1 time t and Ij (t) = 0 otherwise, and In (j ) = 1 if j other customers are present just before the nth arrival epoch and In (j ) = 0 otherwise The probabilities pj and j are related to each other by j 1 = pj , j = 1, 2, (271). 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