THESIS
2020
Abstract
Machine Learning has a plethora of applications in computer science and every-day
life, spanning from image processing to video game AI. The usage of machine learning
models, such as Artificial Neural Networks, has yielded significant benefits in the execution
of tasks, that would otherwise be impractical via conventional algorithms. In this thesis,
we apply machine learning to Spatial Indexing to develop Learned Spatial Indexes (LSI). An
index is a common tool of database systems, that enables speedy retrieval of information.
The goal is to construct an index based on neural networks, which learns the locations of
objects in space, so that the LSI promptly returns the desired data given a location-based
query. We developed several LSIs able to handle the common functions of a s...[
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Machine Learning has a plethora of applications in computer science and every-day
life, spanning from image processing to video game AI. The usage of machine learning
models, such as Artificial Neural Networks, has yielded significant benefits in the execution
of tasks, that would otherwise be impractical via conventional algorithms. In this thesis,
we apply machine learning to Spatial Indexing to develop Learned Spatial Indexes (LSI). An
index is a common tool of database systems, that enables speedy retrieval of information.
The goal is to construct an index based on neural networks, which learns the locations of
objects in space, so that the LSI promptly returns the desired data given a location-based
query. We developed several LSIs able to handle the common functions of a spatial index,
i.e., spatial query processing and data updates. In addition, we compared our proposed
methods to state of the art conventional counterparts. Extensive performance evaluation
verifies that the LSI is highly efficient, and an exciting topic worth researching further.
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