dimension 5150

Fact tableEach data warehouse contains one or more fact data tables. Fact data tables may contain business sales data, such as data generated by cash registration firms. Fact data tables usually contain a large number of rows. Fact data tables are mainly characterized by digital data (FACTS) that can be summarized to provide relevant units as historical data, each fact table contains an index composed of multiple parts. This index contains the primary key of the foreign key's correlated latitude

name calculation can be used to generate descriptive Dimension member names, define other user hierarchies, or specify the names of "(all)" members, to improve user-friendly features of the dimension. You can specify the "all"-level Member names of the Attribute Hierarchy based on the "all"-level Member names of each user hierarchy. In tasks under this topic, a user hierarchy is defined in the "product"

Here's a brief talk of SCD.Put two useful link addresses before you speak. The author's two papers explain what SCD is and how it is appliedHttp://www.cnblogs.com/biwork/p/3363749.htmlHttp://www.cnblogs.com/biwork/p/3371338.htmlSlow changing dimension translation comes down to the slowly changing dimension. It is applied to the loading of dimension table data in

We often encounter this problem in the design of the Data Warehouse: If the dimension has only one attribute in the dimension design, is the choice to create a single dimension for this attribute, or will the attribute of that dimension be placed directly in the fact table as part of the fact table?Suppose there is a

Explanation 1: Fact tables are data tables combined by a certain field of analysis.The latitude table is a combination of analysis indicators in this field. Interpretation 2: To put it simply;A fact table is a transaction table.A dimension table is a basic table.Used to explain the specific content of the keyword latitude in a fact table. Explanation 3: Fact data tableThe central table in the data warehouse architecture, which contains the d

Original: http://blog.csdn.net/keith0812/article/details/8901113The support vector machine method is based on the VC dimension Theory of statistical learning theory and the minimum principle of structural risk.Structured riskStructured risk = empirical risk + confidence riskEmpirical risk = error of the classifier on a given sampleConfidence risk = Error of the result that the classifier classifies on unknown textConfidence Risk Factors:The number of

Js one-dimensional array, multi-dimensional array and Object Mixed use method, js dimension The main purpose of this article is to explain the mixed use of JavaScript arrays and objects. Due to the weak check feature of JS, different types of variables can be stored in the JS array at the same time, for example, you can place numbers, strings, characters, objects, and other content in the same array. Objects can also do the same thing. The difference

When we create dimensions in SSAs, it is sometimes possible that one dimension needs to use multiple table fields as dimension attributes, so there is bound to be an association between the multiple tables, but remember that the correlation between the dimension tables and only one cannot have multiple, let's look at an example.Now we have created a

The support vector machine method is based on the VC dimension Theory of statistical learning theory and the minimum structure risk principle.Confidence risk: The classifier classifies the unknown sample and obtains the error. Experiential risk: A well-trained classifier that re-classifies the training samples. That is, sample error structure risk: Confidence risk + empirical risk structure risk minimization is a strategy proposed to prevent overfitti

A typical example is the logical business compared to cubes, product dimensions, time dimensions, position dimensions, respectively, as different axes. The intersection of axes is a detailed fact. This fact table is a table of intersection points of multiple dimensions. A dimension table is a form of factual analysis.First, the star structure in the database structure is introduced, which maintains the data in a single fact table in the center of the

This article mainly introduces four knowledge points, which is also the content of my lecture. 1.PCA Dimension reduction operation; PCA expansion pack of Sklearn in 2.Python; 3.Matplotlib subplot function to draw a child graph; 4. Through the Kmeans to the diabetes dataset clustering, and draw a child map. Previous recommendation:The Python data Mining course. Introduction to installing Python and crawler"Python Data Mining Course" two. Kmeans cluste

Tags: http data problems ad EF Time Database. Net TT Bi Data Warehouse product database storage A typical example is to compare a logical business to a cube. The product dimension, time dimension, and location dimension are different coordinate axes, and the intersection of coordinate axes is a specific fact. That is to say, a fact table is an intersection of m

The primary key in a dimension table usually has two choices: the Natural key (Natural key), which is already present in the business system, usually a character-type marker with a certain business meaning, which uniquely flags each of the dimension tablesRecording. Like whatOrganization's code, abbreviations, time tags, and so on. The other is the surrogate key (surrogate key), which is usuallyA numeric va

Abstract Yes With the development of computers and the increasing number of data islands in information systems, how to use the data is a problem facing every enterprise.ETLYesexponential Data Extraction(Extract), Data Conversion(Transform)And Data Loading(Loading)And plays a key role in the application of data warehouse.ETLUse these data islands to form a data warehouse,It is an extremely important part in building a data warehouse. Slow change dimen