R* optimizer validation and performance evaluation for local queries
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
An overview of query optimization in relational systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Efficient refreshment of materialized views with multiple sources
Proceedings of the eighth international conference on Information and knowledge management
Towards self-tuning data placement in parallel database systems
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Iterative dynamic programming: a new class of query optimization algorithms
ACM Transactions on Database Systems (TODS)
Proceedings of the ninth international conference on Information and knowledge management
Materialized view selection and maintenance using multi-query optimization
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Selection of Views to Materialize in a Data Warehouse
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Materialized Views Selection in a Multidimensional Database
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Materialized View Selection for Multidimensional Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
VLDB '98 Proceedings of the 24rd 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
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Algorithms for Materialized View Design in Data Warehousing Environment
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Recommending Materialized Views and Indexes with IBM DB2 Design Advisor
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
On solving the view selection problem in distributed data warehouse architectures
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
Load balancing and data placement for multi-tiered database systems
Data & Knowledge Engineering
Lazy maintenance of materialized views
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Tagmark: reliable estimations of RFID tags for business processes
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Extended derivation cube based view materialization selection in distributed data warehouse
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Power efficiency through tuple ranking in wireless sensor network monitoring
Distributed and Parallel Databases
Access control to materialized views: an inference-based approach
Proceedings of the 2011 Joint EDBT/ICDT Ph.D. Workshop
Modeling view selection as a constraint satisfaction problem
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
A materialized-view based technique to optimize progressive queries via dependency analysis
Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research
A survey of view selection methods
ACM SIGMOD Record
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Materialized views (MV) can significantly improve the query performance of relational databases. In this paper, we consider MVs to optimize complex scenarios where many heterogeneous nodes with different resource constraints (e.g., CPU, IO and network bandwidth) query and update numerous tables on different nodes. Such problems are typical for large enterprises, e.g., global retailers storing thousands of relations on hundreds of nodes at different subsidiaries. Choosing which views to materialize in a distributed, complex scenario is NP-hard. Furthermore, the solution space is huge, and the large number of input factors results in non-monotonic cost models. This prohibits the straightforward use of brute-force algorithms, greedy approaches or proposals from organic computing. For the same reason, all solutions for choosing MVs we are aware of do not consider either distributed settings or update costs. In this paper we describe an algorithmic framework which restricts the sets of considered MVs so that a genetic algorithm can be applied. In order to let the genetic algorithm converge quickly, we generate initial populations based on knowledge on database tuning, and devise a selection function which restricts the solution space by taking the similarity of MV configurations into account. We evaluate our approach both with artificial settings and a real-world RFID scenario from retail. For a small setting consisting of 24 tables distributed over 9 nodes, an exhaustive search needs 10 hours processing time. Our approach derives a comparable set of MVs within 30 seconds. Our approach scales well: Within 15 minutes it chooses a set of MVs for a real-world scenario consisting of 1,000 relations, 400 hosts, and a workload of 3,000 queries and updates.