Discarding Similar Data with Autonomic Data Killing Framework Based on High-Level Petri Net Rules: An RSS Implementation

Author

Pinheiro, Wallace ; Silva, Marcelino Campos Oliveira ; Rodrigues, Thiago ; Xexeo, Geraldo ; Souza, Jano

Author_Institution

COPPE, Fed. Univ. of Rio de Janeiro, Rio de Janeiro, Brazil

fYear

2010

fDate

7-13 March 2010

Firstpage

110

Lastpage

115

Abstract

This paper describes the evolutions obtained in the autonomic Data Killing framework that was proposed to eliminate undesirable data. The focus now is about discarding similar data. In order to do it, a modeling method is proposed that uses active rules to be applied through High-level Petri nets. Our method focuses in clustering news in groups by its level of similarity, selecting the newest news of the group and discarding the rest. One experiment has been done in order to proof that method is viable.

Keywords

Petri nets; data handling; information resources; pattern clustering; RSS implementation; autonomic data killing; high-level Petri net rules; news clustering; news filtering; Computer science; Data analysis; Data engineering; Extraterrestrial measurements; Feeds; Mathematics; Military computing; Pattern analysis; Petri nets; Testing; Clustering; Data Killing; High Level Petri-nets; News Filtering;

fLanguage

English

Publisher

ieee

Conference_Titel

Autonomic and Autonomous Systems (ICAS), 2010 Sixth International Conference on

Conference_Location

Cancun

Print_ISBN

978-1-4244-5915-5

Type

conf

DOI

10.1109/ICAS.2010.23

Filename

5442593