MF-Retarget: Aggregate Awareness in Multiple Fact Table Schema Data Warehouses.

TLDR: MF-Retarget is presented, a query retargeting mechanism that deals with both conventional star schemas and multiple facttable (MFT) schemas that is often used to implement a DW using distinct, but interrelated Data Marts.

Abstract: . Performance is a critical issue in Data Warehouse systems (DWs),due to the large amounts of data manipulated, and the type of analysisperformed. A common technique used to improve performance is the use ofpre-computed aggregate data, but the use of aggregates must be transparent forDW users. In this work, we present MF-Retarget, a query retargetingmechanism that deals with both conventional star schemas and multiple facttable (MFT) schemas. This type of multidimensional schema is often used toimplement a DW using distinct, but interrelated Data Marts. The paper presentsthe retargeting algorithm and initial performance tests. 1 Introduction Data warehouses (DW) are analytical databases aimed at providing intuitive access toinformation useful for decision-making processes. A Data Mart (DM), often referredto as a subject-oriented DW, represents a subset of the DW, comprised of relevantdata for a particular business function (e.g. marketing, sales). DW/DM handle largevolumes of data, and they are often designed using a star schema, which containsrelatively few tables and well-defined join paths. On-line Analytical Processing(OLAP) systems are the predominant front-end tools used in DW environments,which typically explore this multidimensional data structure [3, 13]. OLAP operations(e.g. drill down, roll up, slice and dice) typically result in SQL queries in whichaggregation functions (e.g. SUM, COUNT) are applied to fact table attributes, usingdimension table attributes as grouping columns (group by clause).A