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MOMoC code generator Fujaba is a toolkit to generate Java prototype code, given UML class of activity diagrams GenGED (Generation of Graphical Environment for Design) [107] supports generic description of visual modeling languages for generating graphical editors and simulation of behavior models GenGED is based on AGG as a tool for algebraic graph transformations It provides features for checking graphical constraints It has been applied to generating a number of visual languages Its visual environment supports generation of visual editors working in structured or freehand mode To summarize the features of the above tools, they support syntax de nition and model transformation for visual languages Using these tools, the user can easily generate a graphical editor or compiler for a visual language.

Moreover, some visual modeling tools already provide analysis and veri cation functionality, which is especially important for trustworthy use of visual modeling and visual languages technologies the main area of application of graph grammars and graph transformations Graph compilers have become an inherent part of many programming systems to support various elds of activity As a perspective for future development, in our opinion, the challenge of trustworthy integration of graph compilers and traditional compilers should be considered, since there are real tasks whose parts can be solved either by graphical or by traditional algorithmic methods In this relation, developing graphic extensions for the most popular and powerful software development languages and platforms, Java and C#, could be an interesting task EXERCISES TO CHAPTER 9 91 Design an appropriate representation of a graph grammar, and implement single-pushout and double-pushout approaches described in Section 91 9.

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2 Try to clearly formulate the differences between various approaches to graph transformations (categorical, layered graph grammars, RGGs, hypergraphs, meta-models) summarized in Section 92 and the advantages and drawbacks of each approach 93 Why do you think graph grammars are not so widely spread as might be expected What are the problems of graph grammars 94 What is typical of RGGs What is a super-vertex and a node in graph grammars What are the advantages of RGGs 95 What is the characteristic feature of layered graph grammars Why is the layering de ned 96 What is meta-model approach to graph grammars 97 What is a hypergraph and a hyperedge Why are they introduced 98 Please formulate the major features of DiaGen/DiaMeta diagram editor..

According to one survey, the states that had the highest growth of people age 65 or older in recent years were Nevada, Alaska, Hawaii, Arizona, and Utah Other states reporting growth in older residents are the mid-Atlantic states, Oregon, and many college towns In fact, college towns often are catering to the elderly by offering classes at reduced rates as well as other services..

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Cumulative Index, Vols.

The channel-idle probability of 66% is approached in a similar time scale as the rejection probability reaches its steady-state for voice data, as can be seen in Fig. 13.44. Often, a GSPN model specification and automated generation of the underlying CTMC is much more convenient and less error prone due to possible state space explosion. The GSPN in Fig. 13.45 is equivalent to the CTMC in Fig. 13.41 [STP96]. The GSPN was also solved using the SHARPE package. The results of Fig. 13.46 illustrate the qualitative and quantitative difference in rejection probabilities of voice vs. discrete data. Note that rejection probabilities increase for voice data as channel capacity is increased. This effect is due to the reduced loss rate of discrete data data packets as a function of increased buffer size. Figure 13.47 shows the impact that the arrivals of discrete data packets have on voice rejection probabilities as compared to rejection of discrete data packets themselves. Both steady-state and transient probabilities are presented. Note that voice data is much more sensitive to an increase in X2. 23.2.4.2 Markov Modulated Poisson Processes Markov processes (MMPP) are introduced to represent correlated modulated Poisson arrival streams and

The steady-state probabilities vi > 0, i E S, of an ergodic Markov chain can be obtained by solving the system of linear Eq. (2.15) or, if the (finite) mean recurrence times rni are known, by exploiting the relation: 1 vi = rni vi E s.

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