ACM Transactions on Database Systems (TODS)
Maintaining views incrementally
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Improvements on a heuristic algorithm for multiple-query optimization
Data & Knowledge Engineering
Incremental maintenance of views with duplicates
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Rapid bushy join-order optimization with Cartesian products
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Algorithms for deferred view maintenance
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Maintenance of data cubes and summary tables in a warehouse
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Consistency Algorithms for Multi-Source Warehouse View Maintenance
Distributed and Parallel Databases - Special issue on parallel and distributed information systems
Shrinking the warehouse update Window
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient and extensible algorithms for multi query optimization
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Materialized view selection and maintenance using multi-query optimization
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Database System Implementation
Database System Implementation
A heuristic approach to attribute partitioning
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
An Improved Algorithm for the Incremental Recomputation of Active Relational Expressions
IEEE Transactions on Knowledge and Data Engineering
Using Common Subexpressions to Optimize Multiple Queries
Proceedings of the Fourth International Conference on Data Engineering
ATTRIBUTE PARTITIONING IN A SELF-ADAPTIVE RELATIONAL DATA BASE SYSTEM
ATTRIBUTE PARTITIONING IN A SELF-ADAPTIVE RELATIONAL DATA BASE SYSTEM
Parallel multisource view maintenance
The VLDB Journal — The International Journal on Very Large Data Bases
Multiversion-based view maintenance over distributed data sources
ACM Transactions on Database Systems (TODS)
Asymmetric Batch Incremental View Maintenance
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
GPIVOT: Efficient Incremental Maintenance of Complex ROLAP Views
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Building the Data Warehouse
Incremental maintenance for non-distributive aggregate functions
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Challenges and conflicts integrating heterogeneous data warehouses in virtual organisations
International Journal of Networking and Virtual Organisations
FedDW global schema architect: UML-based design tool for the integration of data mart schemas
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
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In the data warehouse environment, the concept of a materialized view is common and important for efficient support of OLAP query processing. Materialized views are generally derived from several relations. These materialized views need to be updated when source relations change. Since the propagation of updates to the views may impose a significant overhead, it is essential to update the warehouse views efficiently. Though various view maintenance strategies have been discussed in the past, optimizations on the total accesses to relations have not been sufficiently investigated. In this paper we propose an efficient incremental view maintenance method called optimal delta evaluation that can minimize the total accesses to relations. We first present the delta evaluation expression and a delta evaluation tree which are core concepts of the method. Then, a dynamic programming algorithm that can find the optimal delta evaluation tree is proposed. We also present various experimental results that show the usefulness and efficiency of our proposed method.