Extending Fagin's Algorithm for More Users Based on Multidimensional B-Tree

We discuss the issue of searching the best K objects in more attributes for more users. Every user prefers objects in different ways. User preferences are modelled locally with a fuzzy function and globally with an aggregation function. Also, we discuss the issue of searching the best K objects without accessing all objects. We deal with the use of local preferences when computing Fagin’s algorithms. We created a new model of lists for Fagin’s algorithms based on B-trees. Furthermore, we use a multidimensional B-tree (MDB-tree)for searching the best K objects. We developed an MD-algorithm, which can effectively find the best K objects in a MDB-tree in accordance with user’s preferences and without accessing all the objects. We show that MD-algorithm achieves better results in the number of accessed objects than Fagin’s algorithms.