Article In: cienciavitae, dblp, scopus, orcid
Multi-relational pattern mining over data streams
Knowledge and Information Systems
— 2015 — Data Min. Knowl. Discov.
Key information
Authors:
Published in
November 29, 2015
Abstract
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.
Publication details
Authors in the community:
Andreia Liliana Perdigão da Silva
ist155330
Claudia Martins Antunes
ist14046
Publication version
P - Proof
Publisher
Data Min. Knowl. Discov.
Link to the publisher's version
https://link.springer.com/article/10.1007/s10618-014-0394-6
Title of the publication container
Knowledge and Information Systems
First page or article number
1783
Last page
1783
Volume
29
Issue
6
Fields of Science and Technology (FOS)
computer-and-information-sciences - Computer and information sciences
Publication language (ISO code)
eng - English
Alternative identifier (URI)
http://www.scopus.com/inward/record.url?eid=2-s2.0-84942500162&partnerID=MN8TOARS
Rights type:
Restricted access