Performance optimisation of the decision-support queries by the horizontal fragmentation of the data warehouse

Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses

“…In contrast, in [14], to obtain fewer fragments and improve the performance of OLAP queries, Kechar and Nait-Bahloul fragmented horizontally just the fact table considering the selectivity of the predicates, their occurrence amount, and their access frequencies. They proposed to split only the fact relation without using the primary HF of the dimension relations.…”

“…They proposed to split only the fact relation without using the primary HF of the dimension relations. Later, in [15], the authors introduced an improved version of their approach presented in [14].…”

“…[13] Minterm predicates TPC-H benchmark. [14] Cost APB-1 benchmark. [15] Cost SSB (star schema benchmark).…”

“…Additionally, to evaluate FTree, it was compared with [14]; for this, the predicate use matrix of Table 16 taken from [14] was used. The best fragmentation scheme according to [14] is that of the three fragments fr 1 = {p 1 , p 2 , p 3 , p 4 , p 5 }, fr 2 = {p 6 }, and fr 3 = {p 7 }. With FTree, in this case, the maximum number of fragments allowed by the data warehouse manager (W) is four.…”

“…For the scheme produced by [14], the cardinalities of the fragments are card(fr1) = 245, card(fr2) = 35, and card(fr3) = 40. Table 17 shows the cost of the queries in two fragmentation schemes, that obtained by [14] and that of FTree. In the case of the schema generated by FTree, the cardinalities of the first and second fragments are card(fr 1 ) = 180 and card(fr 2 ) = 140.…”