Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like

of these operators. This paper defines that operator, called the data cube or simply cube. The cube operator generalizes the histogram, crosstabulation, roll-up, drill-down, and sub-total constructs found in most report writers. The novelty is that cubes are relations. Consequently, the cube operator can be imbedded

Range sum queries on data cubes are a powerful tool for analysis. A range sum query applies an aggregation operation (e.g., SUM, AVERAGE) over all selected cells in a data cube, where the selection is specified by providing ranges of values for numeric dimensions. We present the Dynamic Data Cube

Abstract. The multidimensional analysis based on data cube has been growing interest. However existing data cube model usually does not have the semantics of attributes and hence the analysis usually provides results with raw numbers and ignores the real meanings of these numbers. An example result

. Data cubes provide aggregate information to support the analysis of the contents of data warehouses and databases. An important tool to analyze data in data cubes is the range query. For range queries that summarize large regions of massive data cubes, computing the query result on-the-fly can

We present a new model for OLAP, called the nested data cube (NDC) model. Nested data cubes are a generalization of other OLAP models such as f-tables [3], and hypercubes [2], but also of classical structures such as sets, bags, and relations. The model we propose adds to the previous models

Abstract. This paper presents a general methodology for the e cient parallelization of existing data cube construction algorithms. We describe two di erent partitioning strategies, one for top-down and one for bottomup cube algorithms. Both partitioning strategies assign subcubes to individual

A range query applies an aggregation operation over all selected cells of an OLAP data cube where the selection is specified by providing ranges of values for numeric dimensions. We present fast algorithms for range queries for two types of aggregation operations: SUM and MAX. These two operations

Abstract. Applications like Online Analytical Processing depend heavily on the ability to quickly summarize large amounts of information. Techniques were proposed recently that speed up aggregate range queries on MOLAP data cubes by storing pre-computed aggregates. These approaches try to handle

The design of an OLAP system for supporting real-time queries is one of the major research issues. One approach is to use data cubes, which are materialized precomputed multidimensional views of data in a data warehouse. We can derive a set of data cubes to answer each frequently asked query