Tunable Bounding Volumes for Monte Carlo Applications

Multi-criteria decision making problem has been well studied for many years. One popular query for multi-criteria decision making is top-k queries which require each user to specify an exact utility function. In many cases, the utility function of a user is probabilistic and finding the distribution on the utility functions has been widely explored in the machine learning areas, such as user's recommender systems, Bayesian learning models and user's preference elicitation, for improving user's experience. Motivated by this, we propose a new type of queries called k-hit queries, which has not been studied before. Given a set D of tuples in the database, the distribution Θ on utility functions and a positive integer k, we would like to select a set of k tuples from D in order to maximize the probability that at least one of tuples in the selection set is the favorite of a user. All applications for top-k queries can naturally be used in k-hit queries. In this paper, we present various interesting properties of k-hit queries. Besides, based on these properties, we propose a novel algorithm called k-hit_Alg for k-hit queries. Finally, we conducted comprehensive experiments to show that the performance of our proposed method, k-hit_Alg, is superior compared with other existing algorithms which were originally used to answer other existing queries.

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Implementing Hybrid Pde Solvers

Vavalis

2014

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Tunable Bounding Volumes for Monte Carlo Applications

Cited by 3 publications

References 10 publications

Solid Angle Sampling of Disk and Cylinder Lights

Solid Angle Sampling of Disk and Cylinder Lights

k-Hit Query

Implementing Hybrid Pde Solvers

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