Multi-table joins through bitmapped join indices
ACM SIGMOD Record
Research problems in data warehousing
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A general framework for the view selection problem for data warehouse design and evolution
Proceedings of the 3rd ACM international workshop on Data warehousing and OLAP
Building the Data Warehouse,3rd Edition
Building the Data Warehouse,3rd Edition
View relevance driven materialized view selection in data warehousing environment
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
Achieving scalability in OLAP materialized view selection
Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP
A formal perspective on the view selection problem
The VLDB Journal — The International Journal on Very Large Data Bases
Materialized Views Selection in a Multidimensional Database
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Including Group-By in Query Optimization
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Improving query response time in scientific databases using data aggregation -a case study
DEXA '96 Proceedings of the 7th International Workshop on Database and Expert Systems Applications
Selection of Views to Materialize in a Data Warehouse
IEEE Transactions on Knowledge and Data Engineering
Algorithm for selection of materialized views: based on a costs model
ENC '07 Proceedings of the Eighth Mexican International Conference on Current Trends in Computer Science
Data mining-based materialized view and index selection in data warehouses
Journal of Intelligent Information Systems
Materialised view construction in data warehouse for decision making
International Journal of Business Information Systems
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Materialized views aim to improve the response time of analytical queries posed on a data warehouse. This entails that they contain information that provides answers to most future queries. The selection of such information from the data warehouse is referred to as view selection. View selection deals with selection of appropriate sets of views to improve the query response time. Several view selection algorithms exist in literature, most of them being greedy based. The greedy algorithm HRUA, which selects top-k views from a multidimensional lattice, is considered the most fundamental greedy based algorithm. It selects views having the highest benefit, computed in terms of size, for materialization. Though the views selected using HRUA are beneficial with respect to size, they may not account for a large number of future queries and may hence become an unnecessary overhead. This problem is addressed by the Query Answering Greedy Algorithm (QAGA) proposed in this paper. QAGA uses both the size of the view, and the frequency of previously posed queries answered by each view, to compute the profits of all views in each iteration. Thereafter it selects, from among them, the most profitable view for materialization. QAGA is able to select views which are beneficial with respect to size and have a greater likelihood of answering future queries. Further, experimental results show that QAGA, as compared to HRUA, is able to select views capable of answering greater number of queries. Though HRUA incurs a lower total cost of evaluating all the views, QAGA has a lower total cost of answering all the queries leading to an improvement in the average query response time. This in turn facilitates decision making.