2、数据仓库和数据挖掘的OLAP技术.ppt

实例：使用DMQL定义雪花模式 define cube sales_snowflake [time, item, branch, location]: dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars), units_sold = count(*) define dimension time as (time_key, day, day_of_week, month, quarter, year) define dimension item as (item_key, item_name, brand, type, supplier(supplier_key, supplier_type)) define dimension branch as (branch_key, branch_name, branch_type) define dimension location as (location_key, street, city(city_key, province_or_state, country)) 实例：使用DMQL定义事实星座模式 define cube sales [time, item, branch, location]: dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars), units_sold = count(*) define dimension time as (time_key, day, day_of_week, month, quarter, year) define dimension item as (item_key, item_name, brand, type, supplier_type) define dimension branch as (branch_key, branch_name, branch_type) define dimension location as (location_key, street, city, province_or_state, country) define cube shipping [time, item, shipper, from_location, to_location]: dollar_cost = sum(cost_in_dollars), unit_shipped = count(*) define dimension time as time in cube sales define dimension item as item in cube sales define dimension shipper as (shipper_key, shipper_name, location as location in cube sales, shipper_type) define dimension from_location as location in cube sales define dimension to_location as location in cube sales 度量的分类 一个数据立方体的度量是一个数值函数，该函数可以对数据立方体的每一个点求值。 (刚才的示例中用的是什么函数？) 度量可以根据其所用的聚集函数分为三类： 分布的(distributive)：将函数用于n个聚集值得到的结果和将函数用于所有数据得到的结果一样。 比如：count()，sum()，min()，max()等 代数的(algebraic)：函数可以由一个带M个参数的代数函数计算（M为有界整数），而每个参数值都可以有一个分布的聚集函数求得。 比如：avg()，min_N()，standard_deviation() 整体的(holistic)：描述函数的子聚集所需的存储没有一个常数界。 比如：median()，mode()，rank() 5-6 王 灿 数据挖掘 sjwj@dlc.zju.edu.cn 0703004 概念分层 (1) 一个概念分层（concept hierarchy）定义一个映射序列，将低层概念映射到更一般的高层概念 E.g. 表示location的概念：杭州?浙江?中国?亚洲 概念分层允许我们在各种抽象级审查和处理数据 概念分层可以由系统用户、领域专家、知识工程师人工的提供，也可以根据数据分布的统计分析自动的产生 概念分层 (2): location维的一个概念分层 all Europe North_America Mex