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Neuroshard: towards automatic multi-objective sharding with deep reinforcement learning
Large databases whose data does not fit on a single server need to shard their rows across multiple different database instances. Distributed transactions are significantly more expensive than local transactions, so a popular approach is to collect a ...
Machop: an end-to-end generalized entity matching framework
Real-world applications frequently seek to solve a general form of the Entity Matching (EM) problem to find associated entities. Such scenarios include matching jobs to candidates in job targeting, matching students with courses in online education, ...
GCNSplit: bounding the state of streaming graph partitioning
This paper introduces GCNSplit, a streaming graph partitioning framework capable of handling unbounded streams with bounded state requirements. We frame partitioning as a classification problem and we employ an unsupervised model whose loss function ...
LSI: a learned secondary index structure
Learned index structures have been shown to achieve favorable lookup performance and space consumption compared to their traditional counterparts such as B-trees. However, most learned index studies have focused on the primary indexing setting, where ...
Micro-architectural analysis of a learned index
Since the publication of The Case for Learned Index Structures in 2018 [26], there has been a rise in research that focuses on learned indexes for different domains and with different functionalities. While the effectiveness of learned indexes as an ...
Index Terms
- Proceedings of the Fifth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management