Comparing Data Distribution Using Fading Histograms

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

Raquel Sebastiao (Autor) Outra; Joao Gama (Autor) FEP; Teresa Mendonca (Autor) FCUP

Ata de Conferência Internacional

Título: 21ST EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI 2014)

Páginas: 1095-1096

21st European Conference on Artificial Intelligence (ECAI)

Prague, CZECH REPUBLIC, AUG 18-22, 2014

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 2 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-00A-6P6

DOI: 10.3233/978-1-61499-419-0-1095

Abstract (EN): The emergence of real temporal applications under non-stationary scenarios has drastically altered the ability to generate and gather information. Nowadays, under dynamic scenarios, potentially unbounded and massive amounts of information are generated at high-speed rate, known as data streams. Dealing with evolving data streams imposes the online monitoring of data in order to detect changes. The contribution of this paper is to present the advantage of using fading histograms to compare data distribution for change detection purposes. In an windowing scheme, data distributions provided by the fading histograms are compared using the Kullback-Leibler divergence. The experimental results support that the detection delay time is smaller when using fading histograms to represent data instead of standard histograms.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 2