Materialized views and indexes are physical structures for accelerating data access that are casually used in data warehouses. However, these data structures generate some maintenance overhead. They also share the same storage space. Most existing studies about materialized view and index selection consider these structures separately. In this paper, we adopt the opposite stance and couple materialized view and index selection to take view-index interactions into account and achieve efficient storage space sharing. Candidate materialized views and indexes are selected through a data mining process. We also exploit cost models that evaluate the respective benefit of indexing and view materialization, and help select a relevant configuration of indexes and materialized views among the candidates. Experimental results show that our strategy performs better than an independent selection of materialized views and indexes.
Another approach determines a trade-off between storage space allotted to indexes and materialized views, depending on query definition @cite_48 . According to the authors, the key factors to leverage query optimization is aggregation level, defined by the attribute list of Group by clauses in SQL queries, and the selectivity of attributes present in Where and Having clauses. View materialization indeed provides a great benefit for queries involving coarse granularity aggregations (few attributes in the Group by clause) because they produce few groups among a large number of tuples. On the other hand, indexes provide their best benefit with queries containing high selectivity attributes. Thus, queries with fine aggregations and high selectivity stimulate indexing, while queries with coarse aggregations and weak selectivity encourage view materialization.
@cite_48
1496130826
View materialization and indexing are the most effective techniques adopted in data warehouses to improve query performance. Since both materialization and indexing algorithms are driven by a constraint on the disk space made available for each, the designer would greatly benefit from being enabled to determine a priori which fractions of the global space available must be devoted to views and indexes, respectively, in order to optimally tune performances. In this paper we first present a comparative evaluation of the benefit (saving per disk page) brought by view materialization and indexing for a single query expressed on a star scheme. Then, we face the problem of determining an effective trade-off between the two space fractions for the core workload of the warehouse. Some experimental results are reported, which prove that the estimated trade-off is satisfactorily near to the optimal one.