Maintenance of top-k materialized views

Abstract

In this paper we present results on the problem of maintaining materialized top-k views and provide results in two directions. The first problem we tackle concerns the maintenance of top-k views in the presence of high deletion rates. We provide a principled method that complements the inefficiency of the state of the art independently of the statistical properties of the data and the characteristics of the update streams. The second problem we have been concerned with has to do with the efficient maintenance of multiple top-k views in the presence of updates to their base relation. To this end, we provide theoretical guarantees for the nucleation (practically, inclusion) of a view with respect to another view and the reflection of this property to the management of updates. We also provide algorithmic results towards the maintenance of a large number of views, via their appropriate structuring in hierarchies of views.

Threshold-Based Distributed Continuous Top-k Query Processing for Minimizing Communication Overhead

“…LPTA based on linear programming determines whether the existing views are sufficient to respond to a new top-k query. In [10], the authors proposed algorithms of maintenance of a large number of top-k materialized views for 2-dimensional data. The existing views are indexed in hierarchy paths for efficient view updates, and views to be updated are judged by using those hierarchy paths.…”

Efficient Continual Top-k Keyword Search in Relational Databases

“…To ensure the correctness of a query result, a materialized view must be up-to-date whenever it is accessed by a query. Materialized views maintenance has received considerable attention in the research community over the last two decades [17], [18], [19], [20]. References [18] and [19] address the problem of maintaining the top-k views that are defined on a relation, while Ref.…”

“…Although the result scores do not change once computed in the scenarios of Refs. [18], [19], [20], database updates in our context can change the relevance scores of existing results. Thus, a more sophisticated approach for incremental view updates is required.…”