Publication at Faculty of Mathematics and Physics |
2008
Abstract
Methods of top-k search with no random access can be used to find k best objects using sorted lists of attributes that can be read only by sorted access. Such methods usually need to work with a large number of candidates during the computation.
In this paper we propose new methods of no random access top-k search that can be used to compute k best objects using small memory. We present results of experiments showing improvement in speed depending on ratio of memory size and data size.
Our system outperforms other also when the total number of attributes is much bigger than number of query attributes (varying with user).