【文章內(nèi)容簡介】 er city_key city state_or_province country city 事實星座模式實例 time_key day day_of_the_week month quarter year time location_key street city province_or_state country location Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_type item branch_key branch_name branch_type branch Shipping Fact Table time_key item_key shipper_key from_location to_location dollars_cost units_shipped shipper_key shipper_name location_key shipper_type shipper 一種數(shù)據(jù)挖掘查詢語言 : DMQL ? DMQL首先包括定義數(shù)據(jù)倉庫和數(shù)據(jù)集市的語言原語，這包括兩種原語定義：一種是立方體定義，一種是維定義 ? 立方體定義 (事實表 ) define cube cube_name [dimension_list]: measure_list ? 維定義 (維表 ) define dimension dimension_name as (attribute_or_subdimension_list) ? 特殊案例 (共享維表的定義 ) ? 第一次作為維表定義 “ cube definition” ? 然后： define dimension dimension_name as dimension_name_first_time in cube cube_name_first_time 實例：使用 DMQL定義星型模式 define cube sales_star [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) 實例：使用 DMQL定義雪花模式 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)) 實例：使用 DMQL定義事實星座模式 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 度量的分類 ? 一個數(shù)據(jù)立方體的度量是一個 數(shù)值 函數(shù)，該函數(shù)可以對數(shù)據(jù)立方體的每一個點求值。 ? (剛才的示例中用的是什么函數(shù)？ ) ? 度量可以根據(jù)其所用的聚集函數(shù)分為三類： ? 分布的 (distributive)：將函數(shù)用于 n個聚集值得到的結(jié)果和將函數(shù)用于所有數(shù)據(jù)得到的結(jié)果一樣。 ? 比如： count()， sum()， min()， max()等 ? 代數(shù)的 (algebraic)：函數(shù)可以由一個帶 M個參數(shù)的代數(shù)函數(shù)計算（M為有界整數(shù)），而每個參數(shù)值都可以有一個分布的聚集函數(shù)求得。 ? 比如： avg()， min_N()， standard_deviation() ? 整體的 (holistic)：描述函數(shù)的子聚集所需的存儲沒有一個常數(shù)界。 ? 比如： median()， mode()， rank() 概念分層 (1) ?一個概念分層（ concept hierarchy）定義一個映射序列，將低層概念映射到更一般的高層概念 ? . 表示 location的概念：南寧 ?廣西 ?中國 ?亞洲 ? 概念分層允許我們在各種抽象級審查和處理數(shù)據(jù) ?概念分層可以由系統(tǒng)用戶、領(lǐng)域?qū)＜?、知識工程師人工的提供，也可以根據(jù)數(shù)據(jù)分布的統(tǒng)計分析自動的產(chǎn)生 概念分層 (2): location維的一個概念分層 all Europe North_America Mexico Canada Spain Germany Vancouver M. Wind L. Chan ... ... ... ... ... ... all region office country Toronto Frankfurt city 許多概念分層的定義隱含在數(shù)據(jù)庫的模式中。比如： location維的定義，officecitycountryregion；這些屬性 按一個全序相關(guān)，形成一個層次結(jié)構(gòu)： year day quarter month week 維的屬性也可以組成一個偏序，形成一個格： 概念分層 (3) —— 使用 ?概念分層為不同級別上的數(shù)據(jù)匯總提供了一個良好的基礎(chǔ) ?綜合概念分層和多維數(shù)據(jù)模型的潛力，可以對數(shù)據(jù)獲得更深入的洞察力 ? 通過在多維數(shù)據(jù)模型中，在不同的維上定義概念分層，使得用戶在不同的維上從不同的層次對數(shù)據(jù)進行觀察成為可能。 ?多維數(shù)據(jù)模型（數(shù)據(jù)立方體）使得從不同的角度對數(shù)據(jù)進行觀察成為可能，而概念分層則提供了從不同層次對數(shù)據(jù)