Traditional query optimizers rely on the accuracy of estimated statistics to choose good execution plans. This design often leads to suboptimal plan choices for complex queries, since errors in estimates for intermediate subexpressions grow exponentially in the presence of skewed and correlated data distributions. Reoptimization is a promising technique to cope with such mistakes. Current re-optimizers first use a traditional optimizer to pick a plan, and then react to estimation errors and resulting suboptimalities detected in the plan during execution. The effectiveness of this approach is limited because traditional optimizers choose plans unaware of issues affecting reoptimization. We address this problem using proactive reoptimization, a new approach that incorporates three techniques: i) the uncertainty in estimates of statistics is computed in the form of bounding boxes around these estimates, ii) these bounding boxes are used to pick plans that are robust to deviations of actual values from their estimates, and iii) accurate measurements of statistics are collected quickly and efficiently during query execution. We present an extensive evaluation of these techniques using a prototype proactive re-optimizer named Rio. In our experiments Rio outperforms current re-optimizers by up to a factor of three.

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Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking), harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Then, we show that our proposed active learning solution is capable of matching the performance of a fully supervised baseline by using just 5% of the labels. This solution mimics a typical real-life situation in which a limited number of labels can be acquired through manual annotation by experts.

Abstract-Commercial applications usually rely on precompiled parameterized procedures to interact with a database. Unfortunately, executing a procedure with a set of parameters different from those used at compilation time may be arbitrarily suboptimal. Parametric query optimization (PQO) attempts to solve this problem by exhaustively determining the optimal plans at each point of the parameter space at compile time. However, PQO is likely not cost-effective if the query is executed infrequently or if it is executed with values only within a subset of the parameter space. In this paper, we propose instead to progressively explore the parameter space and build a parametric plan during several executions of the same query. We introduce algorithms that, as parametric plans are populated, are able to frequently bypass the optimizer but still execute optimal or near-optimal plans.

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