Sanjay Agrawal, Surajit Chaudhuri, and Vivek Narasayya. Automated selection of materialized views and indexes for SQL databases. In Proceeding of International Conference on Very Large Data Bases (VLDB), Cairo, Egypt, pages 496--505. Morgan Kaufmann Publishers Inc., San Francisco, CA, September 2000.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....to keep the stand alone cost model consistent with the cost model used by the query optimizer. More recent work proposed to use the query optimizer for cost estimation, and to use a search algorithm to enumerate possible configurations. Such work include [4, 5, 8] in the area of index selection, [2] in the area of This section is contributed by Dr. Zhiyuan Chen, Microsoft Research, zhchen microsoft.com. view selection, and [6] in the area of parallel database design. In the area of system parameter tuning, there is plenty of work giving practical guidelines [3] However, there is ....

Sanjay Agrawal, Surajit Chaudhuri, and Vivek R. Narasayya. Automated selection of materialized views and indexes in SQL databases. In VLDB'2000.

....beans should have, when to use BMP or CMP entity bean, and when to use session bean. Those EJB design strategies need to be studied, formalized, and applied in the EJB generation. 7 Related Work and Conclusions There are tools and methods to migrate EJB applications from one platform to another [1][20] Our work differs from those approaches in that we are migrating the database applications to EJB architecture instead of moving EJB applications between different platforms. There are also tools for mapping database schemata or UML models to entity beans[18] 17] However, these do not ....

Sanjay Agrawal, Surajit Chaudhuri, and Vivek Narasayya. Automated selection of materialized views and indexes in Microsoft SQL Server. VLDB 2000.

....However, we also have the optimizer recommend good candidate indexes, significantly improving the quality of candidates and reducing the search space. In the next release, we will be extending the Index Advisor to also recommend simultaneously Materialized Query Tables (i.e. materialized views) [2] and partitioning of tables in a shared nothing environment [8] This combined tool will be called the Design Advisor, headquarters for all design assistance. 3.3 Maintenance A Maintenance Advisor, available in the next release, will utilize database statistics to determine which objects need ....

Sanjay Agrawal, Surajit Chaudhuri, and Vivek R. Narasayya. "Automated Selection of Materialized Views and Indexes for SQL Databases," Proceedings, 26th International Conference on Very Large Databases, 2000, pp. 496-505.

....schema. On the other hand, when an optimizer uses standard estimation heuristics, we show that the number of necessary views and the expression size of each view are polynomially bounded. 1 Introduction The problem of view selection has received signi cant attention in recent literature [ACN00, CG00, Gup97, GM99, TS97, YKL97, BPT97, GHRU97, HRU96, KM99] Broadly speaking, the problem is the following: given a database schema R, storage space B and a workload of queries Q, choose a set of views V over R to materialize, whose combined size is at most B. The Permission to copy without fee ....

....commercial database systems which support incremental updates of materialized views and now use materialized views to speed up query evaluation. Therefore, choosing an appropriate set of views to materialize in the database is crucial in order to obtain performance bene ts from these new features [ACN00] The view selection problem and its generalizations will play an even greater role in contexts where data needs to be placed intelligently over a wide area network. In these contexts, users are spread over a network, and each location may have di erent types of query characteristics and or ....

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Sanjay Agrawal, Surajit Chaudhuri, and Vivek Narasayya. Automated selection of materialized views and indexes in Microsoft SQL Server. In Proc. of VLDB, pages 496{ 505, Cairo, Egypt, 2000.

....because the number of views (i.e. sources) tends to be much larger, and the sources need not contain the complete extensions of the views. In the area of data warehouse design we need to choose a set of views (and indexes on the views) to materialize in the warehouse [HRU96, TS97, YKL97, GHRU97, ACN00, CG00] Similarly, in web site design, the performance of a web site can be signi cantly improved by choosing a set of views to materialize [FLSY99] In both of these problems, a rst step in determining the utility of a choice of views is to ensure that the views are sucient for answering the ....

Sanjay Agrawal, Surajit Chaudhuri, and Vivek Narasayya. Automated selection of materialized views and indexes in Microsoft SQL Server. In Proc. of the Int. Conf. on Very Large Data Bases (VLDB), Cairo, Egypt, 2000.

.... materialized views at any time without affecting the validity of applications ffl A materialized view consumes storage space ffl The contents of the materialized view must be maintained when the underlying tables are updated (modified) All data warehousing products support materialized views [21, 63]. Two major problems related to materialized views are: 1) the view selection problem, and (2) the view maintenance problem. 4.1 View Selection We initiated a discussion in Section 1 as to which views should be materialized at the data warehouse. To aid answering the queries efficiently, we ....

....huge. Heuristic rules (concerning the benefit and cost of supporting views and indices) to reduce the complexity of A are suggested. 6 Interaction between Indexes and Views Conceptually, both materialized views and indices are physical structures that can significantly accelerate performance [63]. An effective physical database design tool must therefore take into account that the interaction between indexes and materialized views by considering them together to optimize the physical design for the queries on the system [14, 63] An effective physical database design tool must therefore ....

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A. Sanjay, C. Surajit, and V. R. Narasayya. Automated selection of materialized views and indexes in microsoft sql serve. To appear in VLDB'2000, September 2000.

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Sanjay Agrawal, Surajit Chaudhuri, and Vivek Narasayya. Automated selection of materialized views and indexes for SQL databases. In Proceeding of International Conference on Very Large Data Bases (VLDB), Cairo, Egypt, pages 496--505. Morgan Kaufmann Publishers Inc., San Francisco, CA, September 2000.

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Sanjay Agrawal, Surajit Chaudhuri and Vivek R. Narasayya, Automated Selection of Materialized Views and Indexes in SQL Databases, Proceedings of 26th International Conference on Very Large Data Bases, 2000: 496-505.

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Sanjay Agrawal, Surajit Chaudhuri, and Vivek Narasayya. Automated selection of materialized views and indexes in Microsoft SQL Server. In Proc. of VLDB, pages 496-505, Cairo, Egypt, 2000.

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