A framework for ranking data sources and query processing sites in database middleware systems
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This dissertation presents a novel approach to the problem of _nding the characteristics of the data sources and query processing sites in a distributed database system. We model the network as a graph with nodes representing data sources and query processing sites, some of which might be replicated. We introduce a heuristic technique inspired in Ant Colony Optimization Theory to dynamically discover, assess, and catalog each data source or query-processing site. Our goal is to _nd and update possible paths to access the computational resources or data provided by the highest quality sites. We de_ne this concept of quality in terms of performance and freshness. We de_ne the possible mathematical models for each one of these measures. We study di_erent techniques to launch the ants from each node to explore the system, based on the idea of rounds. We discuss the development of the \Lazy Ants" approach to send ants to explore the system, which reduce the number of ants in the system but keeps a high quality of the metadata. We discuss our system prototype developed using CSIM and also present performance and freshness studies designed to analyze the quality of paths found by the Ant Colony based approach. These experiments show that our algorithm can quickly discover high quality sites from which data or query processing capabilities can be consumed. Finally, we present a summary of results, contributions, and the future work for this research topic.