Flexible Operator Embeddings via Deep Learning release_44gg4wechfdslj47huxfn624um

by Ryan Marcus, Olga Papaemmanouil

Released as a article .

2019  

Abstract

Integrating machine learning into the internals of database management systems requires significant feature engineering, a human effort-intensive process to determine the best way to represent the pieces of information that are relevant to a task. In addition to being labor intensive, the process of hand-engineering features must generally be repeated for each data management task, and may make assumptions about the underlying database that are not universally true. We introduce flexible operator embeddings, a deep learning technique for automatically transforming query operators into feature vectors that are useful for a multiple data management tasks and is custom-tailored to the underlying database. Our approach works by taking advantage of an operator's context, resulting in a neural network that quickly transforms sparse representations of query operators into dense, information-rich feature vectors. Experimentally, we show that our flexible operator embeddings perform well across a number of data management tasks, using both synthetic and real-world datasets.
In text/plain format

Archived Files and Locations

application/pdf  1.2 MB
file_fhqnonasi5cnpa5weug2i2awta
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2019-01-25
Version   v1
Language   en ?
arXiv  1901.09090v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 6acf098b-bc2c-4bfc-bd1d-b110c4d2356b
API URL: JSON