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We present a complete XPath cost-based optimization and execution framework and demonstrate its effectiveness and efficiency for a variety of queries and datasets. The framework is based on a logical XPath algebra with novel features and operators and a comprehensive set of rewriting rules that together enable us to algebraically capture many existing and novel processing strategies for XPath queries. An important part of the framework is PSA, a very efficient cost-based plan selection algorithm for XPath queries. In the presented experimental evaluation, PSA picked the cheapest estimated query plan in 100% of the cases. Our cost-based query optimization framework is independent of the underlying physical data model, the storage system, and of the available implementations for the logical algebra operators, depending on a set of well-defined APIs. We also present an implementation of those APIs, including primitive access methods, a large pool of physical operators, statistics estimators and cost models, and experimentally demonstrate the effectiveness of our end-to-end query optimization system.