Paper Info
Title
Incremental View Materialization in Deductive Databases
Abstract
This paper presents a unifying approach to processing of (recursive) queries and updates in a deductive database. To improve query performance, a combined top-down and bottom-up evaluation method is used to compile rules into iterative programs that contain relational algebra operators. This method is based on the lemma resolution that retains previous results to guarantee termination. Due to locality in database processing (i.e. repetitive user query patterns), it is desirable to materialize frequently used queries against views of the database. Unfortunately, if updates are allowed, maintaining materialized view tables becomes a major problem. We propose to materialize views incrementally, as queries are being answered. Hence views in our approach are only partially materialized. For such views, we design algorithms to perform updates only when the underlying view tables are actually affected. We compare our approach to two well-known methods for dealing with views: total materialization and query-modification. The first method materializes the entire view when it is defined while the second recomputes the view on the fly without maintaining any physical view tables. We demonstrate that our approach is a compromise between these two methods by determining the conditions under which it performs better.
Year
Venue
Field
1999
COMPUTERS AND ARTIFICIAL INTELLIGENCE
Locality,Deductive database,Computer science,View,Theoretical computer science,Compiler,Relational algebra,Materialized view,Lemma (mathematics),Recursion,Database
DocType
Volume
Issue
Journal
18
3
ISSN
Citations 
PageRank 
0232-0274
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Wang-Chan Wong121.94
Lubomir Bic2332125.18