數(shù)據(jù)倉(cāng)庫(kù)與數(shù)據(jù)挖掘綜述-閱讀頁(yè)
2025-08-03 17:46本頁(yè)面
【正文】 Legacy External RDBMS RDBMS 帶 ODS的體系結(jié)構(gòu) Source Databases Hub Data Extraction, Transformation, load Warehouse Admin. Tools Extract, Transform and Load Data Modeling Tool Central Metadata Architected Data Marts Data Access and Analysis Central Data Ware house and ODS Central Data Warehouse Mid Tier RDBMS Data Mart Mid Tier RDBMS Data Mart Local Metadata Local Metadata Local Metadata Metadata Exchange ODS OLTP Tools Data Cleansing Tool Relational Appl. Package Legacy External MDB EndUser DW Tools 現(xiàn)實(shí)環(huán)境 —異質(zhì)性 [Douglas Hackney ,2022] Custom Marketing Data Warehouse Packaged Oracle Financial Data Warehouse Packaged I2 Supply Chain Non Architected Data Mart Subset Data Marts Oracle Financials i2 Supply Chain Siebel CRM 3rd Party eCommerce 聯(lián)合型數(shù)據(jù)倉(cāng)庫(kù) /數(shù)據(jù)集市體系結(jié)構(gòu) Real Time ODS Federated Financial Data Warehouse Subset Data Marts Common Staging Area Oracle Financials i2 Supply Chain Siebel CRM 3rd Party Federated Packaged I2 Supply Chain Data Marts Analytical Applications eCommerce Real Time Data Mining and Analytics Real Time Segmentation, Classification, Qualification, Offerings, etc. Federated Marketing Data Warehouse ETL tools amp。 reengineering tools Demanddriven data acquisition amp。 data mining tools, Analysis templates Analytic application development tools amp。 Reporting Supply Chain Analytics amp。 Reporting Business information amp。 actions Financial Analytics amp。 Reporting 閉環(huán)的聯(lián)合型 BI體系結(jié)構(gòu) 數(shù)據(jù)倉(cāng)庫(kù)的焦點(diǎn)問(wèn)題 數(shù)據(jù)的獲得、存儲(chǔ)和使用 Relational Package Legacy External source Data Clean Tool Data Staging Enterprise Data Warehouse Datamart Datamart RDBMS ROLAP RDBMS EndUser Tool EndUser Tool MDB EndUser Tool EndUser Tool ? 數(shù)據(jù)倉(cāng)庫(kù)和集市的加載能力至關(guān)重要 ? 數(shù)據(jù)倉(cāng)庫(kù)和集市的查詢輸出能力至關(guān)重要 ETL工具 ? 去掉操作型數(shù)據(jù)庫(kù)中的不需要的數(shù)據(jù) ? 統(tǒng)一轉(zhuǎn)換數(shù)據(jù)的名稱和定義 ? 計(jì)算匯總數(shù)據(jù)和派生數(shù)據(jù) ? 估計(jì)遺失數(shù)據(jù)的缺省值 ? 調(diào)節(jié)源數(shù)據(jù)的定義變化 ETL工具體系結(jié)構(gòu) 元數(shù)據(jù)庫(kù)及元數(shù)據(jù)管理 ? 元數(shù)據(jù)分類：技術(shù)元數(shù)據(jù)；商業(yè)元數(shù)據(jù)；數(shù)據(jù)倉(cāng)庫(kù)操作型信息 。 包括： ? 數(shù)據(jù)源信息 ? 轉(zhuǎn)換描述（從操作數(shù)據(jù)庫(kù)到數(shù)據(jù)倉(cāng)庫(kù)的映射方法，以及轉(zhuǎn)換數(shù)據(jù)的算法） ? 目標(biāo)數(shù)據(jù)的倉(cāng)庫(kù)對(duì)象和數(shù)據(jù)結(jié)構(gòu)定義 ? 數(shù)據(jù)清洗和數(shù)據(jù)增加的規(guī)則 ? 數(shù)據(jù)映射操作 ? 訪問(wèn)權(quán)限，備份歷史，存檔歷史，信息傳輸歷史，數(shù)據(jù)獲取歷史，數(shù)據(jù)訪問(wèn)，等等 元數(shù)據(jù)庫(kù)及元數(shù)據(jù)管理 ? 商業(yè)元數(shù)據(jù) ? 給用戶易于理解的信息，包括： ? 主題區(qū)和信息對(duì)象類型，包括查詢、報(bào)表、圖像、音頻、視頻等 ? Inter主頁(yè) ? 支持?jǐn)?shù)據(jù)倉(cāng)庫(kù)的其它信息，例如對(duì)于信息傳輸系統(tǒng)包括預(yù)約信息、調(diào)度信息、傳送目標(biāo)的詳細(xì)描述、商業(yè)查詢對(duì)象，等 ? 數(shù)據(jù)倉(cāng)庫(kù)操作型信息 ? 例如，數(shù)據(jù)歷史（快照，版本），擁有權(quán)，抽取的審計(jì)軌跡，數(shù)據(jù)用法 元數(shù)據(jù)庫(kù)及元數(shù)據(jù)管理 ? 元數(shù)據(jù)庫(kù)（ metadata repository） 和工具 — [Martin Stardt， 2022] 數(shù)據(jù)訪問(wèn)和分析工具 ? 報(bào)表 ? OLAP ? 數(shù)據(jù)挖掘 提綱 ? 數(shù)據(jù)倉(cāng)庫(kù)概念 ? 數(shù)據(jù)倉(cāng)庫(kù)體系結(jié)構(gòu)及組件 ? 數(shù)據(jù)倉(cāng)庫(kù)設(shè)計(jì) ? 數(shù)據(jù)倉(cāng)庫(kù)技術(shù)（與數(shù)據(jù)庫(kù)技術(shù)的區(qū)別） ? 數(shù)據(jù)倉(cāng)庫(kù)性能 ? 數(shù)據(jù)倉(cāng)庫(kù)應(yīng)用 ? 數(shù)據(jù)挖掘應(yīng)用概述 ? 數(shù)據(jù)挖掘技術(shù)與趨勢(shì) ? 數(shù)據(jù)挖掘應(yīng)用平臺(tái)（科委申請(qǐng)項(xiàng)目） 數(shù)據(jù)倉(cāng)庫(kù)設(shè)計(jì) ? 自上而下（ TopDown） ? 自底而上（ Bottom Up） ? 混合的方法 ? 數(shù)據(jù)倉(cāng)庫(kù)建模 Topdown Approach ? Build Enterprise data warehouse ? Common central data model ? Data reengineering performed once ? Minimize redundancy and inconsistency ? Detailed and history data。 Sons , 1999 提綱 ? 數(shù)據(jù)倉(cāng)庫(kù)概念 ? 數(shù)據(jù)倉(cāng)庫(kù)體系結(jié)構(gòu)及組件 ? 數(shù)據(jù)倉(cāng)庫(kù)設(shè)計(jì) ? 數(shù)據(jù)倉(cāng)庫(kù)技術(shù)（與數(shù)據(jù)庫(kù)技術(shù)的區(qū)別） ? 數(shù)據(jù)倉(cāng)庫(kù)性能 ? 數(shù)據(jù)倉(cāng)庫(kù)應(yīng)用 ? 數(shù)據(jù)挖掘應(yīng)用概述 ? 數(shù)據(jù)挖掘技術(shù)與趨勢(shì) ? 數(shù)據(jù)挖掘應(yīng)用平臺(tái)（科委申請(qǐng)項(xiàng)目） 數(shù)據(jù)挖掘應(yīng)用綜述 ? 數(shù)據(jù)挖掘應(yīng)用概述 ? 數(shù)據(jù)挖掘技術(shù)與趨勢(shì) ? 數(shù)據(jù)挖掘應(yīng)用平臺(tái) 數(shù)據(jù)挖掘應(yīng)用概述 ? 應(yīng)用比例 ? Data Mining Upsides ? Data Mining Downsides ? Data Mining Use ? Data Mining Industry and Application ? Data Mining Costs 應(yīng)用比例 Clustering 22% Direct Marketing 14% CrossSell Models 12% m 2022/6/11 News ? Discovery of previously unknown relationships, trends, anomalies, etc. ? Powerful petitive weapon ? Automation of repetitive analysis ? Predictive capabilities Data Mining Upsides ? Knowledge discovery technology immature ? Long learning and tuning cycles for some technologies ? “Black box” technology minimizes confidence ? VLDB (Very Large Data Base) requirements Data Mining Downsides Data Mining Uses ? Discover anomalies, outliers and exceptions in process data ? Discover behavior and predict outes of customer relationships ? Churn management ? Target marketing (market of one) ? Promotion management ? Fraud detection ? Pattern ID
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