Title

Searching for Maximal Holes in Databases

Presenter Information

Joseph Lemley

Document Type

Oral Presentation

Location

SURC 137B

Start Date

21-5-2015

End Date

21-5-2015

Keywords

Maximal Empty Rectangle, Maximal Cuboid, Big Data

Abstract

The problem of finding maximal empty rectangles in a set of points in 2D and 3D space has been well studied, and efficient algorithms exist to identify maximal rectangles in 2D space. Unfortunately, such efficiency is lacking in higher dimensions where the problem has been shown to be NP complete when the dimensions are included in the input. We compare existing methods and suggest a novel technique to discover interesting maximal empty hyper-rectangles in cases where dimensionality and input size would otherwise make analysis impractical. Applications include big data analysis, recommender systems, automatic knowledge discovery, and query optimization.

Faculty Mentor(s)

Razvan Andonie

Department/Program

Computer Science

Additional Mentoring Department

Computer Science

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May 21st, 9:10 AM May 21st, 9:30 AM

Searching for Maximal Holes in Databases

SURC 137B

The problem of finding maximal empty rectangles in a set of points in 2D and 3D space has been well studied, and efficient algorithms exist to identify maximal rectangles in 2D space. Unfortunately, such efficiency is lacking in higher dimensions where the problem has been shown to be NP complete when the dimensions are included in the input. We compare existing methods and suggest a novel technique to discover interesting maximal empty hyper-rectangles in cases where dimensionality and input size would otherwise make analysis impractical. Applications include big data analysis, recommender systems, automatic knowledge discovery, and query optimization.