Efficiently updating materialized views
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Maintaining views incrementally
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Answering queries using views (extended abstract)
PODS '95 Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
View maintenance in a warehousing environment
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Incremental maintenance of views with duplicates
SIGMOD '95 Proceedings of the 1995 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
Rewriting aggregate queries using views
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Answering complex SQL queries using automatic summary tables
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Optimizing Queries with Materialized Views
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Performance Issues in Incremental Warehouse Maintenance
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
A Scalable Algorithm for Answering Queries Using Views
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Querying Multiple Features of Groups in Relational Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Spreadsheets in RDBMS for OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Efficient exploitation of similar subexpressions for query processing
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Stream warehousing with DataDepot
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Update propagation in a streaming warehouse
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Preprocessing for fast refreshing materialized views in DB2
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Towards benchmarking stream data warehouses
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
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In many data warehousing environments, it is common to have materialized views (MVs) at different levels of aggregation of one or more dimensions. The extreme case of this is relational OLAP environments, where, for performance reasons, nearly all levels of aggregation across all dimensions may be computed and stored in MVs. Furthermore, base tables and MVs are usually partitioned for ease and speed of maintenance. In these scenarios, updates to the base table are done using Bulk or Partition operations like add, exchange, truncate and drop partition. If changes to base tables can be tracked at the partition level, join dependencies. functional dependencies and query rewrite can be used to optimize refresh of an individual MV. The refresh optimizer, in the presence of partitioned tables and MVs, may recognize dependencies between base table and the MV partitions leading to the generation of very efficient refresh expressions. Additionally, in the presence of multiple MVs, the refresh subsytem can come up with an optimal refresh schedule such that MVs can be refreshed using query rewrite against previously refreshed MVs. This makes the database server more manageable and user friendly since a single function call can optimally refresh all the MVs in the system.