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Problem statement: Many existing database applications manage time-varying data. These database applications are referred to as temporal databases or time-oriented database applications that are considered as repositories of time-dependent data. Many proposals have been introduced for developing time-oriented database applications, some of which suggest building support for Temporal Database Management Systems (TDBMS) on top of existing non-temporal DBMSs, while others suggest modifying the models of exiting DBMSs or building TDBMS from scratch. Approach: This study addressed several issues concerning developing a technique that enables database designers to understand the way in which time-varying behavior can be modeled and mapped into tabular form. Results: Conventional DBMSs do not have the capability to record and process time-varying aspects of the real world. With growing sophistication of DBMS applications, the lack of temporal support in conventional DBMS raises serious problems when used to develop temporal database. The technique of understanding how to think about time and represent it in formal systems is the topic of this study. We examined how to implement time-varying application in the SQL structured query language by introducing temporal data concepts that need to be simulated in DBMSs which lack temporal supports. We proposed a temporal data model that combines the features of previous temporal models and that reduces the cost of memory storage. Conclusion: We proposed a technique for implementing temporal database on top of exiting non-temporal DBMS. This technique includes five main areas. These areas are temporal database conceptual design, temporal database logical design, integrity constraints preventions in temporal database, modifying and querying temporal database. We proposed a data model for the temporal database based on the data models which are discussed in literature

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Stochastic Symmetric Nets (SSN) are a High-Level Stochastic Petri Net formalism which provides a parametric system description and an efficient analysis technique that exploit system symmetries to automatically aggregate its states. Even if significant reductions can be achieved in highly symmetric models, the reduced state space can still be too large to derive and/or solve the underlying stochastic process, so that Monte Carlo simulation and fluid approximation remain the only viable ways that need to be explored. In this paper, we contribute to this line of research by proposing a new approach based on fluid approximation to automatically derive from an SSN model a set of Ordinary Differential Equations (ODEs) which mimic the system behavior, and by showing how the SSN formalism allows to define an efficient translation method which reduces the size of the corresponding ODE system with an automatic exploitation of system symmetries. Additionally, some case studies are presented to show the effectiveness of the method and the relevance of its application in practical cases.

a b s t r a c tAverage surface roughness value (R a ) is an important measure of the quality of a machined work piece. Lower the R a value, the higher is the work piece quality and vice versa. It is therefore desirable to develop mathematical models that can predict the minimal R a value and the associated machining conditions that can lead to this value. In this paper, real experimental data from an end milling process is used to develop models for predicating minimum R a value. Two techniques, model tree and sequential minimal optimization based support vector machine, which have not been used before to model surface roughness, were applied to the training data to build prediction models. The developed models were then applied to the test data to determine minimum R a value. Results indicate that both techniques reduced the minimum R a value of experimental data by 4.2% and 2.1% respectively. Model trees are found to be better than other approaches in predicting minimum R a value.