Inderpal Singh Mumick

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We present incremental evaluation algorithms to compute changes to materialized views in relational and deductive database systems, in response to changes (insertions, deletions, and updates) to the relations. The view definitions can be in SQL or Datalog, and may use UNION, negation, aggregation (<italic>e.g.</italic> SUM, MIN), linear recursion, and(More)
A data warehouse stores materialized views derived from one or more sources for the purpose of eeciently implementing decision-support or OLAP queries. One of the most important decisions in designing a data warehouse is the selection of materialized views to be maintained at the warehouse. The goal is to select an appropriate set of views that minimizes(More)
The Bulletin of the Technical Committee on Data Engineering is published quarterly and is distributed to all TC members. Its scope includes the design, implementation, modelling, theory and application of database systems and their technology. Letters, conference information, and news should be sent to the Editor-in-Chief. Papers for each issue are(More)
Materialized views and view maintenance are important for data warehouses, retailing, banking, and billing applications. We consider two related view maintenance problems: 1) how to maintain views after the base tables have already been modified, and 2) how to minimize the time for which the view is inaccessible during maintenance.Typically, a view is(More)
Materialized views and view maintenance are becoming increasingly important in practice. In order to satisfy different data currency and performance requirements, a number of view maintenance policies have been proposed. Immediate maintenance involves a potential refresh of the view after every update to the deriving tables. When staleness of views can be(More)
A data warehouse stores materialized views over data from one or more sources in order to provide fast access to the integrated data, regardless of the availability of the data sources. Warehouse views need to be maintained in response to changes to the base data in the sources. Except for very simple views, maintaining a warehouse view requires access to(More)
of Duplicates and Aggregates We present a formal treatment of multi-sets (that arise when duplicates are not eliminated) and aggregate operators for deduc-tive and relational databases. We define the semantics rigorously and extend the Magic-Sets technique to programs containing multi-sets and aggregates. The work presented here is an important step in(More)
Integration of data from multiple databases is an important problem. We consider a model for data integration wherein data from multiple databases is combined into an integrated view that is materialized and stored in a database. All queries on the view are then answered directly from the view, without having to go to the diierent databases. Such a model is(More)
We investigate the problem of checking whether the number of derivation trees of a Datalog program with duplicate semantics is nite or not. We show that given a safe stratiied query and an edb, it is possible to check, in polynomial time, whether the query has a nite number of derivation trees. However, it is undecidable to check whether a safe stratiied(More)
Data warehouses contain large amounts of information, often collected from a variety of independent sources. Decision-support functions in a warehouse, such as <italic>on-line analytical processing</italic> (OLAP), involve hundreds of complex aggregate queries over large volumes of data. It is not feasible to compute these queries by scanning the data sets(More)