Incremental View Maintenance For Collection Programming
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Abstract:
In the context of incremental view maintenance (IVM), delta query derivation is an essential technique for speeding up the processing of large, dynamic datasets. The goal is to generate delta queries that, given a small change in the input, can update the materialized view more efficiently than via recomputation.Keywords:
Materialized view
Materialized views have been found to be very effective at speeding up queries, and are increasingly being supported by commercial databases and data warehouse systems. However, whereas the amount of data entering a warehouse and the number of materialized views are rapidly increasing, the time window available for maintaining materialized views is shrinking. These trends necessitate efficient techniques for the maintenance of materialized views.
Materialized view
Database query
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Materialized view
Online analytical processing
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Implement of materialized view is a key technology and efficient solution of advancing system response time.But the maintenance problem of materialized view is involved.Because source data is unsteadiness,the changes of data must be spread to materialized view and make it be consistency the changes of materialized view and source data.Otherwise the freshness of data in materialized view will be fall down and the authenticity and consistency of OLAP will be affected.For the maintenance problem of materialized view,an algorithm is presented and it can make the cost of materialized view recomputation be minimized.
Materialized view
Online analytical processing
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Because the presence of views enhances query performance, materialized views are increasingly being supported by commercial database/data warehouse systems. Whenever the data warehouse is updated, the materialized views must also be updated. However, whereas the amount of data entering a warehouse, the query loads, and the need to obtain up-to-date responses are all increasing, the time window available for making the warehouse up-to-date is shrinking. These trends necessitate efficient techniques for the maintenance of materialized views. In this paper, we show how to find an efficient plan for maintenance of a {\em set} of views, by exploiting common subexpressions between different view maintenance expressions. These common subexpressions may be materialized temporarily during view maintenance. Our algorithms also choose subexpressions/indices to be materialized permanently (and maintained along with other materialized views), to speed up view maintenance. While there has been much work on view maintenance in the past, our novel contributions lie in exploiting a recently developed framework for multiquery optimization to efficiently find good view maintenance plans as above. In addition to faster view maintenance, our algorithms can also be used to efficiently select materialized views to speed up workloads containing queries.
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An effective method to reduce the time of response time about query is to materialize views in data warehouse. Current strategies mainly consider the methods of the primary selection of materialized views. Due to the insufficient consideration of the dynamic refreshment, we address a new dynamic method which can wash out materialized views and add new materialized views in the set of current materialized views on the basis of the greedy algorithm.
Materialized view
Response time
Speedup
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Data Warehouse is a repository of large amount of data collected from multiple data sources. It is mainly used for processing of queries and detailed analysis of data that is useful for decision makers. Hence to make this data available in less amount of time is essential. Here comes the concept of Materialize view. Materialized view stores result of queries which improve query performance. It is not possible to materialize all the views since to materialize a view, requires cost for maintenance and also storage space. Hence proper materialized views selection is one of the important decisions in designing a data warehouse to get best possible results. So it is required to select materialized view which minimizes response time of query and cost of maintenance. In this paper, detailed analysis of miscellaneous methods of materialized view selection and maintenance is done.
Materialized view
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A query processing is a critical issue in data warehouse environment since queries on data warehouses often involve hundreds of complex operations over large volumes of data. Data warehouses therefore build a large number of materialized views to increase the system performance. Which views to materialized is an important factor on the view maintenance cost as well as the query performance. The goal of materialized view selection problem is to select an optimal set of views that minimizes total query response time in addition to the view maintenance cost. In this paper, we present an efficient solution for the materialized view selection problem. Although the optimal selection of materialized views is NP-hard problem, we developed a feasible solution by utilizing the characteristics of relational operators such as join, selection, and grouping.
Materialized view
Online analytical processing
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Materialized view
Representation
Data redundancy
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