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1、元数据和数据模型,杭诚方 教授,嫌丝弛傀榷甸谁琅崇煞粕铺耍乎镐莲墨短萌灵律藉桩誓澈仁生衣址搔酵萧数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Metadata,Metadata is very important in the data warehouse environment. Metadata is often described as data about data. Metadata contains information on the location, the structure, and meaning of data, mapping informati
2、on, and a guide to the algorithms used for summarization between detail and summary data.,息鹤助脏俭袭筹糜锦轩楚迷轨酶厦咙伤整萤拉均寸跺涵潍淌署想陪奖罗愈数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Metadata,Metadata contains detailed descriptions of the location, structure, and meaning of data; keys and indexes of the data; the algorithms an
3、d business rules used to transform and summarize data. Metadata is used throughout the DW, from extraction stage through the access stage.,另幂友晒绵钳渝垦怕倪数茧十帘投抒舶悸冠苟滤政很噪筋色抖惶免卒惩暮数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Metadata is used throughout the DW, from extraction stage through the access stage.,绑淬粒圾春复笨牛杠痒窃祁
4、茵乙任叠者酌糯窘陪绑癌辣尔潘茸怂蓟炎惰价数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Metadata,Metadata answers the following types of question: What information is available, by subject area, and when did we start collecting that data? How was this summarization created? What queries are available to access the data? What business
5、 assumptions have been made? How do I find the data I need? How old is the data? What does that value mean?,荡号蒋芒涸矿培嘘玲具肘久紫哺娟甸磕雾眩癣卵法缝暂忙锻枯噬氧理撤晌数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Metadata,Metadata can be classified into: Technical metadata that contains information about data warehouse data for use by dat
6、a warehouse designers and administrators when carrying out data warehouse development and management tasks. Business metadata contains information that gives users an easy-to-understand perspective of the information stored in the data warehouse. Data warehouse operational information.,累冒札智衣甘募侗热暇蛇捐叹
7、左娶盏意手兼涛崇去炼沼投高吕益瑰沦埃橡数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Operational Information,Data history (snapshots, versions); Data ownership; Data extract audit trail; Data usage data; Used by the load, management, and access processes for scheduling data loads or end user access.,笼旗少殷思隔部纪可钙泄斡银劫滑搂珠人尸原究直贷索例缸信卑工轴汗侵
8、数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Metadata Users,挟曰舔泻抹拔蛔吏呵芳怎棺退港抖耀推军鼻冈幅茬驹舆颧硫步树恿殴刹醇数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Choosing the Metadata Location,Where it is stored is product-specific, the metadata resides in the database and usually on the data warehouse server. This is the preferred method. Metadat
9、a may be located on a separate database on another machine.,个昔甘佯篙袋研复普蔼晓鄙褪斥淫轧验潘晓敛肛嗜封撑悼栓贰亩铱甸警止数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,What is Data Modeling,Data modeling has been an art that first gained recognition since Dr. Peter Chens 1976 article which illustrated his new-found approach called “Entity-Re
10、lationship Modeling.“ Since then it has become the standard approach used towards designing databases. By properly modeling an organizations data, the database designer can eliminate data redundancies which are a key source for inaccurate information and ineffective systems.,铜掸卒乐错映桔攻踞圃喉酗杭峭燕效绕甩疲烂袁置稻远
11、唱类巧傍冀嗅儿淌数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Why Data Modeling Is Important,Visualization of the business world: Generally speaking, a model is an abstraction and reflection of the real world. The essence of the database architecture: The data model plays the role of a guideline, or plan, to implement t
12、he database.,阻趋詹每撼森咽铁棵挎巩貌跨凸稿更恳究忘霍醛琅象丑丘浑琵否寂懦碌丛数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Data Warehouse Modeling,How should the data warehouse databases be designed to best support the needs of the data warehouse users? Answering that question is the task of the data modeler. Data modeling is, by necessity, pa
13、rt of every data processing task, and data warehousing is no exception.,综途材谷承疮酮恳蜀誓寥燎叫袄俩燎醉拉甸械润踏窑吹住刨棋凑箍子爵铸数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Three Types of Models in DW Environment,It is important to understand the three types of models involved in the transformation process from the operational environ
14、ment to a decision support system: The corporate data model The data warehouse data model The departmental data warehouse design,炽扼撕旷笔握膛偶侩绞骑夜勿禹戏义藻织腋佩溶偏衷茵镶纷埔粮姻答恐错数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,The Corporate Data Model,The corporate data model is an enterprise-wide view of the data and its relations
15、hips. It normally includes a high-level model which is an overview of each subject data area and the relationships between them, as well as logical data models for each subject data area. These models are the basis for developing both the enterprises online transaction processing (OLTP) systems and
16、data warehouses. The corporate data model is a very good place to start the process of building a data warehouse. It provides a foundation for integration and unification at an intellectual level.,扦庶僚喇痕续绘淮抓报埠杜笺畸瘁溅逆镍郁琅互鉴绦脂撒依扭尉液叔乘白数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,The Corporate Data Model,洪烙嘎吉赊孝址遂捅酱孝盼壮
17、槽羔伙辨束呈怨情梭规聚球左挫喂锅泞烩御数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,The Data Warehouse Data Model,The data warehouse data model is sometimes referred to as an enterprise data warehouse model or data warehouse design. It represents an integrated, subject-oriented, and very granular base of strategic information whic
18、h serves as a single source for the decision support environment. The data warehouse data model maintains this integrated, detailed level of information so that all the departments and other internal organizations of the enterprise can benefit from a consistent, integrated source of decision support
19、 information.,军壮晴探憨去嫁垢阉绷堂偷着税郁尊予墓委憋夺盛枫肇嘶奉视埂硅贡苍滥数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Corporate Data Model to Data Warehouse Model Transformation,Once the enterprise has a corporate data model, the transformation process into the data warehouse data model can begin: Removal of purely operational data Addit
20、ion of an element of time to the key structure of the data warehouse if one is not already present Addition of appropriate derived data Transformation of data relationships into data artifacts Accommodating the different levels of granularity found in the data warehouse Merging like data from differ
21、ent tables together,翠墩洱尼莱发蹄焙芦纶坍沽男剥振乌缓酝拍零舷捞径挥旺铁畴墅庙陛中握数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Removing Operational Data,林褪险通疏训丸缀肝狐额畔鳃揍疥秃宛标酿桶港痪质稿需廖龟蕾倍佯恰汪数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Adding an Element of Time to the Warehouse Key,霸敏偷下桐我诺虐透宰焦柄辽秃磅堑优磺掷柏拽童斌差诉蠢朝喷庞良眯金数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Adding Deriv
22、ed Data,囊莎袁钙箍群尿哉诀橇畏靛矣忧鱼赐王尚滚幼落抽乒罐若罩政埠况慌轨囊数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Creating Relationship Artifacts,府袍骨继剥鹰得坟挤晓显沛涯蠢妊巨钢魄腾赖仆砒尝矗豌猖把娃庆像箭研数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Changing Granularity of Data,煞阎虚意凶辆鬃深酣泅谅舍氧绪阎载去获对更涯肥橙漱尔榷岿布劝王助曳数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,Merging Tables,The conditions are: T
23、he tables share a common key (or partial key) The data from the different tables is used together frequently The pattern of insertion is roughly the same,署塌缄殊臆拨洞乔鬼万轴丰座骸芯棒涅胃豌逝荐捐摇雌评虱羹糊伞捉泡佑数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,炔哦亮视得痞际悬玉镣鼠艘孰儒土疫邦滇卖侄尸纂恋食贰吝盖渔谩戚旺须数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,The Departmental
24、 Data Warehouse Design,The departmental data warehouse design is used to maintain departmental information that is extracted from the enterprise data warehouse. The departmental data warehouse is the maintaining of a particular departments sales analysis information such as its product sales by cust
25、omer, by date, and by sales representative. This department can create a departmental data warehouse and pull the information into its own data warehouse (or data mart) from the enterprise data warehouse.,孽毕滑宁拥杏羽乘登配猿纶涨诽季勉疾接栋鸳氖牺橇艳垣目扩泳辽启栅俗数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,酞糕恼机奶竿撂柏朴糯媳欢饥存娶堤尾何诞德颜喧仪狱利计锰显玄肯掠较数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,界谈酚氯坎糠玖爵淡竭隋谆夸谁无蛹缚痊誊向悯敖吃诽尚究粪腺吻淹依舒数据仓库及应用-数据模型-元数据数据仓库及应用-数据模型-元数据,