Construction of Materialized Views in Non-Binary Data Space

Materialized views are heavily used to speed up the query response time of any data centric application. In literature, the construction and dynamic maintenance of materialized views are carried out in a Binary Data Space where all attributes are given the same weight. Considering different weights may be particularly significant when similar queries are posed by multiple users, as taking into account the number of accesses to the different attribute values may reflect into the ability of tuning the materialized views accordingly. The methodology to construct weighted materialized view introduced in this paper is based on the association mining techniques, by applying it in a Non-Binary Data Space. The proposed algorithm has been verified by simulation experiments with two benchmark datasets using practical transactional queries. The experimental results prove the superiority of our proposal in terms of query Hit-Miss ratio and flexibility of view size extendibility according to the requirement of practical applications