Titlebook: Data Warehousing and Knowledge Discovery; First International Mukesh Mohania,A Min Tjoa Conference proceedings 1999 Springer-Verlag Berlin

inconceivable

https://doi.org/10.1057/9780230596610ing is adopted to ensure that compression/decompression overheads are limited, and that data reorganisations are of controlled complexity and can be carried out incrementally. The basic architecture is described and experimental results on the TPC-D and other datasets show the performance of our system.

ureter

Nancy E. Bockstael,Kathleen Belle. However, it helps to reduce storage and computing efforts. Additionally, the secondary space itself reveals much about the set’s structure and can facilitate data mining. We make a proposal for adding the property of a dimension to a metric and show how to determine the real (in general fractal) dimension of the underlying data set.

avulsion

Solving Upstream-Downstream Problemsraction of a large rule set is computationally expensive, we propose an algorithm to perform an incremental recomputation of the output rule set. This algorithm is based on the detection of containment relationships between mining queries.

含鐵

受人支配

向宇宙

Incremental Refinement of Mining Queriesraction of a large rule set is computationally expensive, we propose an algorithm to perform an incremental recomputation of the output rule set. This algorithm is based on the detection of containment relationships between mining queries.

等待

The Design and Implementation of Modularized Wrappers/Monitors in a Data Warehouse, we also deveIop a toolkit to generate a corresponding monitor. By the method, we can reduce much effort to code the monitor component. We also develop a method to map the object-relational schema into relational one. The mapping method helps us make an uniform interface between a wrapper and an integrator.

抗生素

OLAP-based Scalable Profiling of Customer Behaviorcalable and flexible profiling engine. We define profiles by probability distributions, and compute them using OLAP operations on multidimensional and multilevel data cubes. Our experience has revealed the simplicity and power of OLAP-based solutions to scalable profiling and pattern analysis.

反叛者

行乞