"Combination; MD-algorithm; Efficient; Solving; Top-K; Problem; according; User's; Preferences"@en . . . . "Combination of TA- and MD-algorithm for Efficient Solving of Top-K Problem according to User's Preferences"@en . "2009-01-01+01:00"^^ . . "Z(MSM0021620838)" . "\u010Cesk\u00E9 vysok\u00E9 u\u010Den\u00ED technick\u00E9 v Praze" . "DATESO 2009" . . "Neuveden" . . "307531" . "12"^^ . . . "Combination of TA- and MD-algorithm for Efficient Solving of Top-K Problem according to User's Preferences" . "[F2EC9F3CA87C]" . . "RIV/00216208:11320/09:00207444!RIV10-MSM-11320___" . . "RIV/00216208:11320/09:00207444" . . "Combination of TA- and MD-algorithm for Efficient Solving of Top-K Problem according to User's Preferences" . . "11320" . "In this article we focus on efficient solving of searching the best K objects in more attributes according to user\u2019s 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\u2019s 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\u2019s 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\u2019s 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."@en . . . "978-80-01-04323-3" . "Combination of TA- and MD-algorithm for Efficient Solving of Top-K Problem according to User's Preferences"@en . "In this article we focus on efficient solving of searching the best K objects in more attributes according to user\u2019s 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\u2019s 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\u2019s 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\u2019s 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." . "Pokorn\u00FD, Jaroslav" . "2"^^ . "000272412300013" . "Ondrei\u010Dka, Mat\u00FA\u0161" . . . . . . . . "1"^^ . .