Multi-relational pattern mining over data streams

29 de novembro de 2015

The data storage paradigm has changed in the last decade, from operational databases to data repositories that make easier to analyze data and mining information. Among those, the primary multidimensional model represents data through star schemas, where each relation denotes an event involving a set of dimensions or business perspectives. Mining data modeled as a star schema presents two major challenges, namely: mining extremely large amounts of data and dealing with several data tables at the same time. In this paper, we describe an algorithm—Star FP Stream, in detail. This algorithm aims for finding the set of frequent patterns in a large star schema, mining directly the data, in their original structure, and exploring the most efficient techniques for mining data streams. Experiments were conducted over two star schemas, in the healthcare and sales domains.

Autores da comunidade :

• 

  A

  Andreia Liliana Perdigão da Silva

  ist155330

• 

  C

  Claudia Martins Antunes

  ist14046

  Afiliações e fontes

  IST > DEI

  Departamento de Engenharia Informática

  De: cienciavitae

  ID: 1738897

  De: dblp

  ID: journals/datamine/SilvaA15

  De: scopus

  ID: 2-s2.0-84942500162

  De: orcid

  ID: 72757199

P - Versão editada

Data Min. Knowl. Discov.

https://link.springer.com/article/10.1007/s10618-014-0394-6

Knowledge and Information Systems

1783

1783

29

6

10.1007/s10618-014-0394-6

computer-and-information-sciences - Ciências da Computação e da Informação

eng - Inglês

http://www.scopus.com/inward/record.url?eid=2-s2.0-84942500162&partnerID=MN8TOARS