Columbia University

Gail E Kaiser

Salvatore J Stolfo

Suhit Gupta

Lakshminarasimhan Sethumadhavan

Tony Jebara

Fang-Hsiang Su

Systems and methods are presented for content extraction from markup language text. The content extraction process may parse markup language text into a hierarchical data model and then apply one or more filters. Output filters may be used to make the process more versatile. The operation of the content extraction process and the one or more filters may be controlled by one or more settings set by a user, or automatically by a classifier. The classifier may automatically enter settings by classifying markup language text and entering settings based on this classification. Automatic classification may be performed by clustering unclassified markup language texts with previously classified markup language texts.

Systems and methods for content extraction from a mark-up language text accessible at an internet domain

Disclosed are devices, systems, apparatus, methods, products, media, and other implementations, including a method that includes generating for a code segment of a first process an instruction dependency graph representative of behavior of the first process, obtaining respective one or more instruction dependency graphs representative of behaviors of code segments for one or more other processes, and determining, based on the first instruction dependency graph for the first process and the respective one or more instruction dependency graphs for the one or more other processes, a level of similarity between the first process and at least one of the one or more other processes.

Code relatives detection

Systems and methods for content extraction from mark-up language text accessible at an internet domain

A content extraction process may parse markup language text into a hierarchical data model and then apply one or more filters. Output filters may be used to make the process more versatile. The operation of the content extraction process and the one or more filters may be controlled by one or more settings set by a user, or automatically by a classifier. The classifier may automatically enter settings by classifying markup language text and entering settings based on this classification. Automatic classification may be performed by clustering unclassified markup language texts with previously classified markup language texts.

Growing community of inventors

New York, NY, United States of America

Gail E Kaiser