Incremental Maintenance of a Materialized view in Data Warehousing : An Effective Approach

A view is a derived relation defined in terms of base relations. A view can be materialized by storing its extent in the database. An index can be made of these views and access to materialized view is much faster that recomputing the view from scratch. A Data Warehouse stores large amount of information collected from a different data sources. In order to speed up query processing, warehouse usually contains a large number of materialized views. When the data sources are updated, the views need to be updated. The process of keeping view up to date called as materialize view maintenance. Accessing base relations for view maintenance can be difficult, because the relations may be being used by users. Therefore materialize view maintenance in data warehousing is an important issue. For these reasons, the issue of self-maintainability of the view is an important issue in data warehousing. In this paper we have shown that a materialized view can be maintained without accessing the view itself by materializing additional relations at the data warehouse site. We have developed a cost effective approach to reduce the burden of view maintenance and also proved that proposed approach is optimum as compared to other approaches. Here incremental evaluation algorithm to compute changes to materialized views in relational is presented

Transaction Chains: Achieving Serializability with Low Latency in Geo-distributed Storage Systems. In:

Abstract Currently, users of geo-distributed storage systems face a hard choice between having serializable transactions with high latency, or limited or no transactions with low latency. We show that it is possible to obtain both serializable transactions and low latency, under two conditions. First, transactions are known ahead of time, permitting an a priori static analysis of conflicts. Second, transactions are structured as transaction chains consisting of a sequence of hops, each hop modifying data at one server. To demonstrate this idea, we built Lynx, a geo-distributed storage system that offers transaction chains, secondary indexes, materialized join views, and geo-replication. Lynx uses static analysis to determine if each hop can execute separately while preserving serializability-if so, a client needs wait only for the first hop to complete, which occurs quickly. To evaluate Lynx, we built three applications: an auction service, a Twitter-like microblogging site and a social networking site. These applications successfully use chains to achieve low latency operation and good throughput

Querying and Updating XML Data based on Node Labeling Schemes

Ph.DDOCTOR OF PHILOSOPH

Asymmetric batch incremental view maintenance

Incremental view maintenance has found a growing number of applications recently, including data warehousing, continuous query processing, publish/subscribe systems, etc. Batch processing of base table modifications, when applicable, can be much more efficient than processing individual modifications one at a time. In this paper, we tackle the problem of finding the most efficient batch incremental maintenance strategy under a refresh response time constraint; that is, at any point in time, the system, upon request, must be able to bring the view up to date within a specified amount of time. The traditional approach is to process all batched modifications relevant to the view whenever the constraint is violated. However, we observe that there often exists natural asymmetry among different components of the maintenance cost; for example, modifications on one base table might be cheaper to process than those on another base table because of some index. We exploit such asymmetries using an unconventional strategy that selectively processes modifications on some base tables while keeping batching others. We present a series of analytical results leading to the development of practical algorithms that approximate an “oracle algorithm” with perfect knowledge of the future. With experiments on a TPC-R database, we demonstrate that our strategy offers substantial performance gains over traditional deferred view maintenance techniques. 1