Statistical user model supported by R-Tree structure

A Lightweight Tree Structure to Model User Preferences

Traditionally, search engines treat the keywords entered by users as a text string, without trying to understand the sense behind the user’s need. There has been some recent work done in trying to understand the user better and model her interests to improve the relevancy of the results retrieved by the search engine. In this paper, we present a new hierarchical modeling of a user’s interests w...

Tree-Structure Based User Scheduling Algorithm

Middle-R - A User Level Middleware for Statistical Computing

In this work we present middle-R, a user-level middleware for statistical computing. It has been developed to distribute R (a language and environment for statistics) in commodity computers running Microsoft Windows. The solution is simple because it uses Alchemi, a grid middleware developed using Microsoft .NET technology in the University of Melbourne. The implemented solution uses Alchemi as...

Hilbert R - tree : An improved R - tree

We propose a new R-tree structure that outperforms all the older ones. The heart of the idea is to facilitate the deferred splitting approach in R-trees. This is done by proposing an ordering on the R-tree nodes. This ordering has to be 'good', in the sense that it should group 'similar' data rectangles together, to minimize the area and perimeter of the resulting minimum bounding rectangles (M...

A tree-to-tree model for statistical machine translation

In this thesis, we take a statistical tree-to-tree approach to solving the problem of machine translation (MT). In a statistical tree-to-tree approach, first the source-language input is parsed into a syntactic tree structure; then the source-language tree is mapped to a targetlanguage tree. This kind of approach has several advantages. For one, parsing the input generates valuable information ...