Adapting ADTrees for Improved Performance on Large Datasets with High Arity Features

The ADtree, a data structure useful for caching sufficient statistics, has been successfully adapted to grow lazily when memory is limited and to update sequentially with an incrementally updated dataset. However, even these modified forms of the ADtree still exhibit inefficiencies in terms of both space usage and query time, particularly on datasets with very high dimensionality and with high arity features. We propose five modifications to the ADtree, each of which can be used to improve size and query time under specific types of datasets and features. These modifications also provide an increased ability to precisely control how an ADtree is built and to tune its size given external memory or speed requirements.

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Van Dam, Robert D.

• ADtree
• NLP
• high arity
• Penn Treebank
• data structure
• caching sufficient statistcs
• Computer Sciences

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/1529

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-2528