Paper Info
Title
Finding patterns in large star schemas at the right aggregation level
Abstract
There are many stand-alone algorithms to mine different types of patterns in traditional databases. However, to effectively and efficiently mine databases with more complex and large data tables is still a growing challenge in data mining. The nature of data streams makes streaming techniques a promising way to handle large amounts of data, since their main ideas are to avoid multiple scans and optimize memory usage. In this paper we propose in detail an algorithm for finding frequent patterns in large databases following a star schema, based on streaming techniques. It is able to mine traditional star schemas, as well as stars with degenerate dimensions. It is able to aggregate the rows in the fact table that relate to the same business fact, and therefore find patterns at the right business level. Experimental results show that the algorithm is accurate and performs better than the traditional approach.
Year
DOI
Venue
2012
10.1007/978-3-642-34620-0_30
MDAI
Keywords
Field
DocType
traditional approach,large data table,data mining,large databases,fact table,traditional star schema,business fact,large star schema,large amount,right aggregation level,traditional databases,data stream,star schema,data streams
Row,Data mining,Data stream mining,Star schema,Fact table,Computer science,Schema (psychology),A* search algorithm
Conference
Citations 
PageRank 
References 
5
0.48
8
Authors
2
Name
Order
Citations
PageRank
Andreia Silva170.85
Cláudia Antunes216116.57