數(shù)據(jù)挖掘概念與技術(shù)chapter3-數(shù)據(jù)倉(cāng)庫(kù)與olap技術(shù)(編輯修改稿)
2024-11-15 19:44 本頁(yè)面
【文章內(nèi)容簡(jiǎn)介】 rand, 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)) 22 用 DMQL定義事實(shí)星座 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 23 三類度量（數(shù)值函數(shù)） ? 分布的 (distributive): 將數(shù)據(jù)劃分為 n個(gè)集合 , 函數(shù)在每一部分上的計(jì)算得到一個(gè)聚集值 . 如果將函數(shù)用于 n個(gè)聚集值得到的結(jié)果 , 與將函數(shù)用于所有數(shù)據(jù)得到的結(jié)果一樣 , 則該函數(shù)可以用分布方式計(jì)算 . ? 例 , count(), sum(), min(), max(). ? 代數(shù)的 (algebraic):如果它能夠由一個(gè)具有 M(其中 , M是一個(gè)整數(shù)界 )個(gè)參數(shù)的代數(shù)函數(shù)計(jì)算 , 而每個(gè)參數(shù)都可以用一個(gè)分布聚集函數(shù)求得 . ? 例 , avg(), min_N(), standard_deviation(). ? 整體的 (holistic):如果描述它的子聚集所需的存儲(chǔ)沒有一個(gè)常數(shù)界 . ? 例 , median(), mode(), rank(). 24 一個(gè)概念分層 : 維 Location all Europe North_America Mexico Canada Spain Germany Vancouver M. Wind L. Chan ... ... ... ... ... ... all region office country Toronto Frankfurt city 25 數(shù)據(jù)倉(cāng)庫(kù)和分層結(jié)構(gòu)視圖 26 多維數(shù)據(jù) ? 多維模型中，數(shù)據(jù)組織成多維，每維包含由概念分層定義的多個(gè)抽象層 ? 銷售量作為 product, month, 和 region的函數(shù) Product Month 維 : Product, Location, Time 的分層結(jié)構(gòu) Industry Region Year Category Country Quarter Product City Month Week Office Day 27 一個(gè)數(shù)據(jù)方的樣本 TV 在 Date Country sum sum TV VCR PC 1Qtr 2Qtr 3Qtr 4Qtr Canada Mexico sum 28 對(duì)應(yīng)于數(shù)據(jù)方的方體 all product date country product,date product,country date, country product, date, country 0D(頂點(diǎn) ) 方體 1D方體 2D方體 3D(基本 )方體 29 瀏覽數(shù)據(jù)方 ? 可視化 ? OLAP 的能力 ? 交互式操作 30 典型的 OLAP操作 ? 上卷 (Roll up)/上鉆 (drillup): 匯總數(shù)據(jù) ? 下鉆 (Drill down)/下卷 (roll down): 上卷的逆操作 ? 切片 (Slice)和切塊 : ? 投影和選擇 ? 轉(zhuǎn)軸 (Pivot)/旋轉(zhuǎn) (rotate): ? 調(diào)整數(shù)據(jù)方 , 目視操作 , 3D 到 2D 平面 . ? 其它操作 ? 鉆過 (drill across): 涉及多個(gè)事實(shí)表 ? 鉆透 (drill through): 通過數(shù)據(jù)方的最底層 , 到它背后的關(guān)系表 (使用 SQL) 31 OLAP 操作 : 上卷 ? 上卷 (Roll up)/上鉆 (drillup): 匯總數(shù)據(jù) ? 通過沿概念分層攀升或通過維歸約 ? 在 location上卷 (由 cities 到 countries) 32 OLAP 操作 : 下鉆 ? 下鉆 (Drill down)/下卷 (roll down): 上卷的逆操作 ? 由較高層的匯總到較低層的匯總或詳細(xì)數(shù)據(jù) , 或者引進(jìn)新的維 ? 在 time下鉆 (由 quarters 到 months) 33 OLAP 操作 :切片 ? 切片 (Slice) : ? 投影和選擇，對(duì)一個(gè)維進(jìn)行選擇，導(dǎo)致子方體 ? 切片條件 : time=“Q
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