Efficient Top-K Problem Solvings for More Users in Tree-Oriented Data Structures

This paper focuses on efficient searching the best K objects in more attributes according to user preferences. User preferences are modelled locally with fuzzy functions and globally with an aggregation function. Because of local preferences, we have used B+-tree for sorting objects according to a fuzzy function. We deal with the usage of TA-algorithm, which uses B+-trees, and MD-algorithm, which is based on multidimensional B-tree. In this paper we develop a new algorithm, MXT-algorithm, which id based on integration of MD-algorithm with more instances of TA-algorithm. We also develop a new tree-oriented data structure based on B+-trees, multidimensional B-tree with lists, in which MXT-algorithm can effectively find the best K objects according to user preferences. Finally, we show that according to the type of object attribute domains, it is possible to choose the best data structure for objects storage and also top-K algorithm for efficient top-K problem solving.