Given an OLAP query expressed over multiple source OLAP databases, we study the problem of evaluating the result OLAP target database. The problem arises when it is not possible to derive the result from a single database. The method we use is the linear indirect estimator, commonly used for statistical estimation. We examine two obvious computational methods for computing such a target database, called the "Full-cross-product" (F) and the "Pre-aggregation" (P) methods. We study the accuracy and computational complexity of these methods. While the method F provides a more accurate estimate, it is more expensive computationally than P. Our contribution is in proposing a third new method, called the "Partial-Pre-aggregation" method (PP), which is significantly less expensive than F, but is just as accurate.
Answering Joint Queries from Multiple Aggregate OLAP Databases
2003
Abstract
Given an OLAP query expressed over multiple source OLAP databases, we study the problem of evaluating the result OLAP target database. The problem arises when it is not possible to derive the result from a single database. The method we use is the linear indirect estimator, commonly used for statistical estimation. We examine two obvious computational methods for computing such a target database, called the "Full-cross-product" (F) and the "Pre-aggregation" (P) methods. We study the accuracy and computational complexity of these methods. While the method F provides a more accurate estimate, it is more expensive computationally than P. Our contribution is in proposing a third new method, called the "Partial-Pre-aggregation" method (PP), which is significantly less expensive than F, but is just as accurate.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
