Finding a happiness maximizing set (HMS) from a database, ie, selecting a small subset of
tuples that preserves the best score with respect to any nonnegative linear utility function, is …

Selecting key data subsets for model training is an effective way to improve training
efficiency. Existing methods generally utilize a well-trained model to evaluate samples and …

Submodular maximization has attracted extensive attention due to its numerous applications
in machine learning and artificial intelligence. Many real-world problems require maximizing …

Regret minimization queries are important methods to extract representative tuples from
databases. They have been extensively investigated in the last decade due to wide …

Multi-criteria decision-making typically requires selecting a concise, representative set from
large databases. Regret minimization set (RMS) queries have emerged as a solution to …

Multi-criteria decision-making often involves selecting a small representative set from a
database. A recently proposed method is the regret minimization set (RMS) queries. It aims …

The happiness or regret based query has been another important tool in multi-dimensional
decision-making besides the top-k and skyline queries. To avoid the happiness ratio being …

Kernel is a kind of data summary which is elaborately extracted from a large dataset. Given a
problem, the solution obtained from the kernel is an approximate version of the solution …

Finding a small subset representing a large dataset is an important functionality in many real
applications such as data mining, recommendation and web search. The average …