Materialized views are heavily used to speed up the query response time of any data centric application. In the 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 fired from multiple sites in a distributed environment, 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 in distributed environments. The allocation of the views in the operating sites is also considered to a suitable use in distributed databases. Experimental results prove the superiority of proposed methodology on three bench mark datasets in terms of query Hit-Miss ratio and regulation of the view size with varying requirement of practical applications. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.