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Optimization of complex XQueries combining many XPath steps and joins is currently hindered by the absence of good cardinality estimation and cost models for XQuery. Additionally, the state-ofthe-art of even relational query optimization still struggles to cope with cost model estimation errors that increase with plan size, as well as with the effect of correlated joins and selections.In this research, we propose to radically depart from the traditional path of separating the query compilation and query execution phases, by having the optimizer execute, materialize partial results, and use sampling based estimation techniques to observe the characteristics of intermediates. The proposed technique takes as input a Join Graph where the edges are either equi-joins or XPath steps, and the execution environment provides value-and structural-join algorithms, as well as structural and value-based indices.While run-time optimization with sampling removes many of the vulnerabilities of classical optimizers, it brings its own challenges with respect to keeping resource usage under control, both with respect to the materialization of intermediates, as well as the cost of plan exploration using sampling. Our approach deals with these issues by limiting the run-time search space to so-called "zeroinvestment" algorithms for which sampling can be guaranteed to be strictly linear in sample size. All operators and XML value indices used by ROX for sampling have the zero-investment property.We perform extensive experimental evaluation on large XML datasets that shows that our run-time query optimizer finds good query plans in a robust fashion and has limited run-time overhead.

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We demonstrate ROX, a run-time optimizer of XQueries, that focuses on finding the best execution order of XPath steps and relational joins in an XQuery. The problem of join ordering has been extensively researched, but the proposed techniques are still unsatisfying. These either rely on a cost model which might result in inaccurate estimations, or explore only a restrictive number of plans from the search space. ROX is developed to tackle these problems. ROX does not need any cost model, and defers query optimization to run-time intertwining optimization and execution steps. In every optimization step, sampling techniques are used to estimate the cardinality of unexecuted steps and joins to make a decision which sequence of operators to process next. Consequently, each execution step will provide updated and accurate knowledge about intermediate results, which will be used during the next optimization round. This demonstration will focus on: (i) illustrating the steps that ROX follows and the decisions it makes to choose a good join order, (ii) showing ROX's robustness in the face of data with different degree of correlation, (iii) comparing the performance of the plan chosen by ROX to different plans picked from the search space, (iv) proving that the run-time overhead needed by ROX is restricted to a small fraction of the execution time.