draftsight dimension

The original: "Bi thing-the art of data" understanding Dimension Data Warehouse-fact table, dimension table, aggregation tableFact tableIn a multidimensional data warehouse, a table that holds a detailed value or fact for a measure is called a fact table. A fact table that stores sales and sales by state, product, and month has 5 columns that are conceptually similar to the following example.

(v) Advanced technology 16. Segmented dimension This article describes the implementation technology of the piecewise dimension. A segmented dimension contains a fragment of a contiguous value. For example, the annual Sales order segmentation dimension may contain three files called "Low", "medium", "High", and each f

tables will have different measures. A sales data warehouse may contain these two measure columns: Sales and sales. A field information Data Warehouse may contain 3 measure columns: Total, number of minutes, and number of defects. When you create a report, you can think of a measure as an extra dimension. That is, you can use sales and sales as side-by-side column headings, or you can use them as row headings. In the fact table, however, each measure

Directory Objective Types of dimension tables Usage Scenarios for dimension tables Keys and properties for dimension tables Summary Preface From the macroscopic point of view, we tell you 7 questions. So from the microscopic point of view, concrete changes to how to build a consistent

Fast Change Dimension (QCD) is relative to the slow change dimension (SCD), such as "member dimension" in the "membership level" of the change is not very frequent dimension attributes belong to SCD, and such as "Age", "last deal date" such a change of the dimension can not

Dimensional modeling of the Data Warehouse, there is a concept called slowly changing Dimensions, Chinese is generally translated into "slow change dimension", often abbreviated to SCD. The slow change dimension is proposed because in the real world, the attributes of the dimension are not static, and it changes slowly over time. This time-varying

A concept in a data warehouse for dimensional modeling is slowly changing dimensions, which is often abbreviated as SCD. The slow change dimension is proposed because in the real world, dimension attributes are not static and will change slowly with the loss of time. This time-varying dimension is generally called a slow-changing

We often need to know the array dimensions that were previously defined, or to iterate over them, or something else. This is especially true when we show initialization of an array without specifying its dimensions: int is[]={1,2,3}; Readers with the C language development experience may often use the following methods to implement: int dimension=sizeof(is)/sizeof(is[0]) This works very well in most cases. Just knocking on the keyboard a bit more

Fractal Box Dimension texture features In texture feature extraction, texture fragment dimension feature (FD) is an important description of texture. The more complex and delicate the image texture, the greater the fragment dimension. There are many methods to extract fractal dimension features, with different theoreti

Mysql generates a time dimension table. mysql generates a dimension table. Generate a time dimension table in mysql Using mysql common date functions to generate a time dimension table is the most efficient and simple, without the support of other tools. Example of result generation: # time spanSET @d0 = "2012-01-01";

= =2)) { //Delete the objects in the group and then delete the group, then delete the group directly. foreach(ObjectId IDinchPartgroup.getallentityids ()) {Entity ent=(Entity) ID. GetObject (Openmode.forwrite); Ent. Erase (); Ent. Dispose (); } partgroup.upgradeopen (); Partgroup.erase (true); Partgroup.downgradeo

Android Learning has been a long time, feel a lot of knowledge has been forgotten, now began to slowly organize it, refueling!Let's start the Android Foundation tour today!First, Android's system frameworkAndroid system architecture is divided into 4 layers1, application application layer;2. Framework application frame layer;3, libraries system run time and System Class library layer (c + +);4, Linux kernel and hardware drive layer;Ii. Introduction of Android Application development system1. Fou

The manually created dimension is as follows. Dimdevice. Use[J_itvdw] IfObject_id ('Dimdevice')Is Not Null Drop TableDimdevice Go Create Table[DBO]. [dimdevice] ( [DeviceID] [Int]Not Null Primary Key Identity(1, 1 ), [Devicename] [nvarchar] (50)Null, [Platformid] [Int]Null, [Platformname] [nvarchar] (50)Null, [Orderkey]Int Default(0 ), [Createtime] [datetime]Null Default(Getdate ()) ) Set Identity_insert[Dimdevice]On

= array(); $level = 0; $tfifo = $array; do{ $fifo = $tfifo; $tfifo = array(); $count[$level] = 0; foreach($fifo as $item){ $count[$level]++; if(is_array($item)) { foreach($item as $subitem){ $tfifo[] = $subitem; } } } $level++; }while(count($tfifo)>0); return $count;} ARR{X}{Y}[Z]The third dimension of the number of elements is not arr{x}{y}.length it?And so

Talking about one-dimension array of DP algorithm (I) solving the 01 knapsack problem and the dp dimension Introduction to DP algorithm (I) -- How to use a one-dimensional array to solve the 01 knapsack problem Dynamic Programming (also known as Dynamic planning) is one of the most classic algorithms. This document introduces the familiar number tower problem and discusses in depth the solution of the 001

( 'sydney', 'melbourne' )) );Returns 3, 5, 14 I think you should only use recursion to make such statistics. // $ Array: array to be counted. $ I indicates the dimension. $ count indicates the upper-layer function count_array ($ array, $ I = 1, $ count = array ()) {$ n = 0; if (isset ($ count [$ I]) {$ n = $ count [$ I];} $ count [$ I] = count ($ array) + $ n; $ I + = 1; foreach ($ array as $ item) {if (is_array ($ item )) {$ count

( 'sydney', 'melbourne' )) );Returns 3, 5, 14 I think you should only use recursion to make such statistics. // $ Array: array to be counted. $ I indicates the dimension. $ count indicates the upper-layer function count_array ($ array, $ I = 1, $ count = array ()) {$ n = 0; if (isset ($ count [$ I]) {$ n = $ count [$ I];} $ count [$ I] = count ($ array) + $ n; $ I + = 1; foreach ($ array as $ item) {if (is_array ($ item )) {$ count

absrtact: This paper first introduces the 2 basic elements of the dimension table and fact table in the dimension model, and secondly, introduces the 4 basic steps of the design dimension model, and thirdly, around the need of a bank to realize the integration of the business value chain data, Introduces 3 key concepts in Multidimensional architectures: Data Ware

Define dimension granularity in a measurement value group : Users may need to define fact data table dimensions of different granularities or specificity for different purposes. For example, sales data for distributors or Internet sales can be recorded once a day, while sales quota information may be recorded on a monthly or quarterly basis. In these cases, you may need time dimensions to have different granularities or levels of detail for these dif

what it is.Now, I assume that through the above mentioned two articles about NF-HIPAC, you already know the multi-dimensional interval matching process, then we can be separated from the specific scene, it is abstracted into a general problem, first look at the abstract, in this diagram, I ignored the size of the interval, ignoring the rule of the permutation problem ( In the end I will return to this question):In this diagram, we see a lot of "?" , which means that we don't know what ruleset i