Paper Info
Title
Integrating pattern mining in relational databases
Abstract
Almost a decade ago, Imielinski and Mannila introduced the notion of Inductive Databases to manage KDD applications just as DBMSs successfully manage business applications. The goal is to follow one of the key DBMS paradigms: building optimizing compilers for ad hoc queries. During the past decade, several researchers proposed extensions to the popular relational query language, SQL, in order to express such mining queries. In this paper, we propose a completely different and new approach, which extends the DBMS itself, not the query language, and integrates the mining algorithms into the database query optimizer. To this end, we introduce virtual mining views, which can be queried as if they were traditional relational tables (or views). Every time the database system accesses one of these virtual mining views, a mining algorithm is triggered to materialize all tuples needed to answer the query. We show how this can be done effectively for the popular association rule and frequent set mining problems.
Year
DOI
Venue
2006
10.1007/11871637_43
PKDD
Keywords
Field
DocType
query language,past decade,mining query,virtual mining view,database query optimizer,integrating pattern mining,mining algorithm,relational databases,database system,frequent set mining problem,popular relational query language,key dbms paradigm,optimizing compiler,association rule,relational database
Query optimization,SQL,Data mining,Query language,Relational database,Computer science,Sargable,View,Query by Example,Knowledge extraction
Conference
Volume
ISSN
ISBN
4213
0302-9743
3-540-45374-1
Citations 
PageRank 
References 
19
0.85
9
Authors
3
Name
Order
Citations
PageRank
Toon Calders1133393.66
Bart Goethals2157594.55
Adriana Prado3875.14