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Orientador: Daniel Jugend / Banca: José Alcides Gobbo Junior / Banca: Marcio Lopes Pimenta / Resumo: Não disponível / Abstract: Not available / Mestre

Integration flows are increasingly used to specify and execute data-intensive integration tasks between heterogeneous systems and applications. There are many different application areas such as real-time ETL and data synchronization between operational systems. For the reasons of an increasing amount of data, highly distributed IT infrastructures, and high requirements for data consistency and up-to-dateness of query results, many instances of integration flows are executed over time. Due to this high load and blocking synchronous source systems, the performance of the central integration platform is crucial for an IT infrastructure. To tackle these high performance requirements, we introduce the concept of cost-based optimization of imperative integration flows that relies on incremental statistics maintenance and inter-instance plan re-optimization. As a foundation, we introduce the concept of periodical re-optimization including novel cost-based optimization techniques that are tailor-made for integration flows. Furthermore, we refine the periodical re-optimization to on-demand re-optimization in order to overcome the problems of many unnecessary re-optimization steps and adaptation delays, where we miss optimization opportunities. This approach ensures low optimization overhead and fast workload adaptation.

The purpose of this study was to explore whether traditional and nontraditional students who transferred from 2-year to 4-year institutions experienced differences in transfer shock, academic integration, and social integration. A substantial body of knowledge comparing transfer students to native students on transfer shock exists, while only a few qualitative studies have focused on how transfer students experience academic and social integration at the transfer institution. Further, the major studies examining transfer student behavior group all transfer students into a single category by the one thing they share in common- a transition experience. Due to the increasing numbers and diversity of transfer students it is important to examine how their unique characteristics influence transfer and subsequent integration into their new environment. The sample for this study was taken from the Beginning Postsecondary Students Longitudinal Study (BPS: 04/09). The large, nationally representative data set was filtered to include only students who transferred from 2-year institutions to 4-year institutions, creating a much smaller sample of students. The main independent variable in the analyses was age. For this study traditional students were defined as those less than 24 years of age, while nontraditional students were those 24 years or older. Linear regression was used to examine whether traditional and nontraditional students experienced differences in transfer shock, level of academic integration, and level of social integration following transfer. The findings from this study suggest that there is no relationship between age and transfer experiences. Further research is necessary to determine whether the absence of a relationship truly exists or whether the small sample size in this analysis influenced the outcome.

Integration flows are increasingly used to specify and execute data-intensive integration tasks between heterogeneous systems and applications. There are many different application areas such as real-time ETL and data synchronization between operational systems. For the reasons of an increasing amount of data, highly distributed IT infrastructures, and high requirements for data consistency and up-to-dateness of query results, many instances of integration flows are executed over time. Due to this high load and blocking synchronous source systems, the performance of the central integration platform is crucial for an IT infrastructure. To tackle these high performance requirements, we introduce the concept of cost-based optimization of imperative integration flows that relies on incremental statistics maintenance and inter-instance plan re-optimization. As a foundation, we introduce the concept of periodical re-optimization including novel cost-based optimization techniques that are tailor-made for integration flows. Furthermore, we refine the periodical re-optimization to on-demand re-optimization in order to overcome the problems of many unnecessary re-optimization steps and adaptation delays, where we miss optimization opportunities. This approach ensures low optimization overhead and fast workload adaptation.