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

Finding a small set of representative tuples from a large database is an important
functionality for supporting multi-criteria decision making. Top-queries and skyline queries …

Multi-criteria decision-making often requires finding a small representative set from the
database. A recently proposed method is the regret minimization set (RMS) query. RMS …

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 …

Multi-criteria decision-making usually requires finding a small representative set from the
database. popular method, the regret minimization set (RMS) query, returns a size subset of …

Let P be a set of n points in R 2. For a parameter ε∈(0, 1), a subset C⊆ P is an ε-kernel of P
if the projection of the convex hull of C approximates that of P within (1-ε)-factor in every …

A top-k query retrieves the k tuples with highest scores according to a user preference,
defined as a scoring function. It is difficult for a user to precisely specify the scoring function …

Given a point set $ P $ in the plane, we seek a subset $ Q\subseteq P $, whose convex hull
gives a smaller and thus simpler representation of the convex hull of $ P $. Specifically, let …

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 …

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 …