SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning

SkinnerDB is designed from the ground up for reliable join ordering. It maintains no data statistics and uses no cost or cardinality models. Instead, it uses reinforcement learning to learn optimal join orders on the fly, during the execution of the current query. To that purpose, we divide the execution of a query into many small time slices. Different join orders are tried in different time slices. We merge result tuples generated according to different join orders until a complete result is obtained. By measuring execution progress per time slice, we identify promising join orders as execution proceeds. Along with SkinnerDB, we introduce a new quality criterion for query execution strategies. We compare expected execution cost against execution cost for an optimal join order. SkinnerDB features multiple execution strategies that are optimized for that criterion. Some of them can be executed on top of existing database systems. For maximal performance, we introduce a customized execution engine, facilitating fast join order switching via specialized multi-way join algorithms and tuple representations. We experimentally compare SkinnerDB's performance against various baselines, including MonetDB, Postgres, and adaptive processing methods. We consider various benchmarks, including the join order benchmark and TPC-H variants with user-defined functions. Overall, the overheads of reliable join ordering are negligible compared to the performance impact of the occasional, catastrophic join order choice

Catechol as a New Electron Hot Spot of Carbon Nitride

Graphitic carbon nitride (CNx) is a promising photocatalyst with visible-light sensitivity, attractive band-edge positions, tunable electronic structure, and eco-friendliness. However, their applications are limited by a low catalytic activity due to inefficient charge separation and insufficient visiblelight absorption. Here we show a new method to generate the electron polarization of CNx toward the edge via the chemical conjugation of catechol to CNx for enhanced photochemical activity. The electron-attracting property of catechol/quinone pairs induces the accumulation of photoexcited electrons at the edge of conjugated catechol-CNx hybrid nanostructure (Cat-CNx), , serving as an electron hot spot, as demonstrated by positive open-circuit photovoltage, which increases electron transfer through the conjugated catechol while suppressing charge recombination in the CNx. The catechol conjugation also widens the photoactive spectrum via the larger range delocalization of π-electrons. Accordingly, Cat-CNx reveals a 6.3 higher reductive photocurrent density than CNx. Gold ion reduction dramatically increased due to the enhanced electron transfer activity of Cat-CNx in cooperation with the inherent hydrophilicity and metal chelating property of catechols. Cat-CNx exhibits a 4.3 higher maximum adsorption capacity for gold ions under simulated sun light illumination compared to CNx. This work suggests that the post-modification of CNx’s π-conjugated system is a promising route to handle varied shortcomings and broaden availability of CNx