Efficiently Computing the Top N Averages in Iceberg Cubes
نویسندگان
چکیده
منابع مشابه
Star-Cubing: Computing Iceberg Cubes by Top-Down and Bottom-Up Integration
Data cube computation is one of the most essential but expensive operations in data warehousing. Previous studies have developed two major approaches, top-down vs. bottomup. The former, represented by the MultiWay Array Cube (called MultiWay) algorithm [25], aggregates simultaneously on multiple dimensions; however, it cannot take advantage of Apriori pruning [2] when computing iceberg cubes (c...
متن کاملCross Table Cubing: Mining Iceberg Cubes from Data Warehouses
All of the existing (iceberg) cube computation algorithms assume that the data is stored in a single base table, however, in practice, a data warehouse is often organized in a schema of multiple tables, such as star schema and snowflake schema. In terms of both computation time and space, materializing a universal base table by joining multiple tables is often very expensive or even unaffordabl...
متن کاملBUC Algorithm for Iceberg Cubes: Implementation and Sensitivity Analysis
The Iceberg-Cube problem restricts the computation of the data cube to only those group-by partitions satisfying a minimum threshold condition defined on a specified measure. In this paper, we implement the Bottom-Up Computation (BUC) algorithm for computing Iceberg cubes and conduct a sensitivity analysis of BUC with respect to the probability density function of the data. The distributions un...
متن کاملIceberg-cubes with Entropy Query for Data Compression Processing
As the increasing usage of data generating devices like cameras, mobile phones, AutoID technologies, and so on, huge amount of data are created. Data compression is very important. In order to use the suitable compression methods on reducing the data volume, this paper uses iceberg-cubes to compress the data based on the entropy query mechanism. Using the definition of iceberg-cubes, this paper...
متن کاملComputing Complex Iceberg Cubes by Multiway Aggregation and Bounding
Iceberg cubing is a valuable technique in data warehouses. The efficiency of iceberg cube computation comes from efficient aggregation and effective pruning for constraints. In advanced applications, iceberg constraints are often non-monotone and complex, for example, “Average cost in the range [δ1, δ2] and standard deviation of cost less than β”. The current cubing algorithms either are effici...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2003