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Computing multiple related group-bys and aggregates is one of the core operations of On-Line Analytical Processing (OLAP) applications. Recently, Gray et al. [GBLP95] proposed the “Cube” operator, which computes group-by aggregations over all possible subsets of the specified dimensions. The rapid acceptance of the importance of this operator(More)
To fulfill the requirement of fast interactive multidimensional data analysis, database systems precompute aggregate views on some subsets of dimensions and their corresponding hierarchies. However, the problem of what to precompute is difficult and intriguing. The leading existing algorithm, BPUS, has a running time that is polynomial in the number of(More)
At the heart of all OLAP or multidimensional data analysis applications is the ability to simultaneously aggregate across many sets of dimensions. Computing multidimensional aggregates is a performance bottleneck for these applications. This paper presents fast algorithms for computing a collection of group bys. We focus on a special case of the(More)
Caching has been proposed (and implemented) by OLAP systems in order to reduce response times for multidimensional queries. Previous work on such caching has considered table level caching and query level caching. Table level caching is more suitable for static schemes. On the other hand, query level caching can be used in dynamic schemes, but is too coarse(More)
We extend the OLAP data model to represent data ambiguity, specifically imprecision and uncertainty , and introduce an allocation-based approach to the semantics of aggregation queries over such data. We identify three natural query properties and use them to shed light on alternative query semantics. While there is much work on representing and querying(More)
Recent work proposed extending the OLAP data model to support data ambiguity, specifically imprecision and uncertainty. A process called allocation was proposed to transform a given imprecise fact table into a form, called the Extended Database, that can be readily used to answer OLAP aggregation queries.In this work, we present scalable, efficient(More)
Implementing a CRM Analytics solution for a business involves many steps including data extraction, populating the extracted data into a warehouse, and running an appropriate mining algorithm. We propose a CRM Analytics Framework that provides an end-to-end framework for developing and deploying prepackaged predictive modeling business solutions, intended(More)
To speed up multidimensional data analysis, database systems frequently precompute aggregates on some subsets of dimensions and their corresponding hierarchies. This improves query response time. However, the decision of what and how much to precompute is a dii-cult one. It is further complicated by the fact that precomputation in the presence of(More)
Database researchers have made significant progress on several research issues related to multidimensional data analysis, including the development of fast cubing algorithms, efficient schemes for creating and maintaining precomputed group-bys, and the design of efficient storage structures for multidimensional data. However, to date there has been little(More)