Combination of TA- and MD-algorithm for Efficient Solving of Top-K Problem according to User's Preferences
Abstract
In this article we focus on efficient solving of searching the best K objects in more attributes according to user?s preferences. Local preferences are modelled with one of four types of fuzzy function.
Global preferences are modelled concurrently with an aggregation function. We focused on searching the best K objects according to various user?s preferences without accessing all objects.
Therefore we deal with the use of TA-algorithm and MD-algorithm. Because of local preferences we used B+-trees during computing of Fagin?s TA-algorithm.
For searching the best K objects MD-algorithm uses multidimensional B-tree, which is also composed of B+-trees. We developed an MXT-algorithm and a new data structure, in which MXT-algorithm can effectively find the best K objects by user?s preferences without accessing all the objects.
We show that MXT-algorithm in some cases achieves better results in the number of accessed objects than TA-algorithm and MD-algorithm.