In recent years, in the era of Big Data, studying new methods to improve the performance of well-known procedures, such as searching in a Sorted Set, has become crucial in many fields. A new trend emerging in this scenario combines Machine Learning models with Data Structures, generating the so-called Learned Data Structures. In this thesis, we provide an in-depth experimental study of the use of these models, starting from some evidence known to experts in the field but not experimentally investigated concerning the use of very complex models such as Neural Networks. Then, we document a time/space trade-off scenario that is very important for practitioners and designers users. Furthermore, we investigate a comparison well known in the Lite...
The so-called learned sorting, which was first proposed by Google, achieves data sorting by predicti...
This work analyzes and parallelizes LearnedSort, the novel algorithm that sorts using machine learni...
© 2020, VLDB Endowment. All rights reserved. Recent advancements in learned index structures propose...
Machine-learning techniques, properly combined with data structures, have resulted in Learned Static...
Machine Learning Techniques, properly combined with Data Structures, have resulted in Learned Static...
Learned Indexes use a model to restrict the search of a sorted table to a smaller interval. Typicall...
Learned Indexes are a novel approach to search in a sorted table. A model is used to predict an inte...
The new area of Learned Data Structures consists of mixing Machine Learning techniques with those sp...
A recent trend in algorithm design consists of augmenting classic data structures with machine learn...
With the aim of obtaining time/space improvements in classic Data Structures, an emerging trend is t...
The desired output in many machine learning tasks is a structured object, such as tree, clustering, ...
A recent trend in algorithm design consists of augmenting classic data structures with machine learn...
A recent trend in algorithm design consists of augmenting classic data structures with machine learn...
<p align="justify">This article proposes the architecture for a system that uses previously learned ...
Published version of a chapter from the book Pattern Recognition and Machine Intelligence. Also avai...
The so-called learned sorting, which was first proposed by Google, achieves data sorting by predicti...
This work analyzes and parallelizes LearnedSort, the novel algorithm that sorts using machine learni...
© 2020, VLDB Endowment. All rights reserved. Recent advancements in learned index structures propose...
Machine-learning techniques, properly combined with data structures, have resulted in Learned Static...
Machine Learning Techniques, properly combined with Data Structures, have resulted in Learned Static...
Learned Indexes use a model to restrict the search of a sorted table to a smaller interval. Typicall...
Learned Indexes are a novel approach to search in a sorted table. A model is used to predict an inte...
The new area of Learned Data Structures consists of mixing Machine Learning techniques with those sp...
A recent trend in algorithm design consists of augmenting classic data structures with machine learn...
With the aim of obtaining time/space improvements in classic Data Structures, an emerging trend is t...
The desired output in many machine learning tasks is a structured object, such as tree, clustering, ...
A recent trend in algorithm design consists of augmenting classic data structures with machine learn...
A recent trend in algorithm design consists of augmenting classic data structures with machine learn...
<p align="justify">This article proposes the architecture for a system that uses previously learned ...
Published version of a chapter from the book Pattern Recognition and Machine Intelligence. Also avai...
The so-called learned sorting, which was first proposed by Google, achieves data sorting by predicti...
This work analyzes and parallelizes LearnedSort, the novel algorithm that sorts using machine learni...
© 2020, VLDB Endowment. All rights reserved. Recent advancements in learned index structures propose...