MULTIDIMENSIONAL INDEXING STRUCTURE FOR USE WITH LINEAR OPTIMIZATION QUERIES

Patent №

US 6,408,300

Granted

2002-06-18

Filed 1999

Owner

INTERNATIONAL BUSINESS MACHINES CORPORATION

AI components

4

ml · kr · planning · hardware

Assignment

Recorded

Dataset

AIPD

2023_r1 edition

Application

09360366

Linear optimization queries, which usually arise in various decision support and resource planning applications, are queries that retrieve top N data records (where N is an integer greater than zero) which satisfy a specific optimization criterion. The optimization criterion is to either maximize or minimize a linear equation. The coefficients of the linear equation are given at query time. Methods and apparatus are disclosed for constructing, maintaining and utilizing a multidimensional indexing structure of database records to improve the execution speed of linear optimization queries. Database records with numerical attributes are organized into a number of layers and each layer represents a geometric structure called convex hull. Such linear optimization queries are processed by searching from the outer-most layer of this multi-layer indexing structure inwards. At least one record per layer will satisfy the query criterion and the number of layers needed to be searched depends on the spatial distribution of records, the query-issued linear coefficients, and N, the number of records to be returned. When N is small compared to the total size of the database, answering the query typically requires searching only a small fraction of all relevant records, resulting in a tremendous speedup as compared to linearly scanning the entire dataset.

Machine learningKnowledge representationPlanningAI hardwareG06F 16/2264Y10S 707/99934Y10S 707/99942Y10S 707/99945Y10S 707/99948

AI classification

Planning0.99
AI hardware0.98
Knowledge representation0.95
Machine learning0.60
Vision0.05
Evolutionary computation0.04
Natural language0.00
Speech0.00

Ownership

INTERNATIONAL BUSINESS MACHINES CORPORATION

assignment · 102650818

Assignors

BERGMAN, LAWRENCE DAVID, CASTELLI, VITTORIO, CHANG, YUAN-CHI, LI, CHUNG-SHENG, SMITH, JOHN RICHARD

On an employer assignment, the assignors are typically the inventors.

From the same owner

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