设计数据仓库、事实表和维度表的逻辑模型
你好，我是数据仓库的新手，作业要求我实现逻辑设计、物理和实现。你会如何在数据仓库中对此进行建模：
提供与棒球联盟相关的统计数据的答案
我希望设计数据仓库，为球员
进攻中：
•击球手击球次数。
•得分是多少。
•多少次安打、双打和三打。
•打出了多少本垒打。
•许多打点员。
•许多球垒
•防守中
：▪ 双打需要多少出局
▪ 有多少次助攻
▪ 导致投手失误的次数：
▪ 输了多少场比赛
▪ 赢得了很多比赛
▪ 已保存的游戏数量
▪ 有多少完整游戏领先
▪ 已经开始了多少场比赛 多次漂白
▪ 击出多少安打、收到两安打、收到三安打、收到本垒打
至于球员的数据，重要的是可以将其视为 季节，有人知道 DW 设计的具体部分的一些参考吗？有什么想法吗？
多谢。
Hi i'm newbie in Datawarehousing,For homework ask me realize the logical design, physical and implementation.How would you model this in a Data Warehouse:
i wish design the Data Warehouse which give the answers of statistics relating to a baseball league
For players
in offensive:
•How many times has a batter to bat.
•How many runs scored is.
•How many hits,doubles hit and triples hits.
•How many homeruns did.
•many RBI.
•many base on balls
in Defensive:
▪ How many outs, double play takes
▪ How many assists has
▪ How many errors lead or Pitcher:
▪ How many games has lost
▪ has won many games
▪ How many saved games
▪ How many complete games leads
▪ How many games have started many
times it has bleaching▪ How many hit, double hit received, received triple hit, received homerun
As for the data of the players, it is important that this can be viewed as
season, Does anyone know some references on that precise part of DW design ? Any ideas?
Thanks a lot.
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设计任何数据仓库的第一步是选择业务流程。您已经通过根据棒球统计数据确定报告要求来做到这一点，这已经是一个非常明确的流程（基于比赛规则）。
第二步是识别谷物。粒度是您在报告查询中需要表示的最低级别的详细信息。对于您的棒球比赛情况，这很可能是一个球场。
接下来，您将确定描述颗粒所需的尺寸。这些都很容易识别——比赛、投手、击球手和比赛日期都是显而易见的。
最后，您将确定与这些维度相关的度量的事实。这涵盖了您问题中的许多衡量标准，包括跑动是否得分 - 这将在任何维度组合上相加，即在局数、比赛、球队、球员或赛季级别。
The first step in designing any data warehouse is to choose a business process. You have already done so by identifying reporting requirements based on baseball statistics, which is already a very well defined process (based on the rules of the game).
The second step is to identify the grain. The grain is the lowest level of detail you need to represent in reporting queries. For your baseball game situation, this would most likely be a pitch.
Next, you would identify the dimensions required to describe your grain. These are all easily identifiable - game, pitcher, batter and played date are obvious ones to start with.
Finally, you would identify the facts that hold measures relating to these dimensions. This encompasses many of the measures from your question, including whether a run was scored - which would be additive across any combination of dimensions, i.e. at inning, game, team, player, or season levels.
IBM 红皮书有几个切题的标题。
仓储
智能环境
The IBM Redbooks have a couple of titles that are on point.
Warehousing
Intelligence Environment