Mining temporal patterns in longitudinal event data using discrete event matrices and sparse coding

US8583586B2 · kind B2 · utility

Assignee

• International Business Machines Corporation · US

Inventors

• Shahram Ebadollahi · White Plains, US
• Jianying Hu · Hoboken, US
• Martin S. Kohn · East Hills, US
• Noah Lee · Berlin, DE
• Robert K. Sorrentino · Middletown, US
• Jimeng Sun · Hawthorne, US
• Fei Wang · Ossining, US

Key dates

• Filing date: Jan 21, 2011
• Grant date: Nov 12, 2013
• Expiry date: Jan 6, 2032

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/00
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Methods and systems for event pattern mining are shown that include representing longitudinal event data in a measurable geometric space as a temporal event matrix representation (TEMR) using spatial temporal shapes, wherein event data is organized into hierarchical categories of event type and performing temporal event pattern mining with a processor by locating visual event patterns among the spatial temporal shapes of said TEMR using a constraint sparse coding framework.