2 research outputs found
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