cube olap

One of the previous articles-the Data warehouse multidimensional data model already provides a brief description of the definition and structure of an overly-dimensional model, as well as the concept of the fact table and the dimension table (Dimension table). Multidimensional data model, as a new logical model, gives the new organization and storage form of data, and the advantage of the analysis is the effective operation and processing based on the model, which is

analysis, and is of high performance (still dependent on design).The SQL Server integration Service, which is used primarily to extract data incrementally from the original database (SQL Server/oracle/mysql/xml/excel, etc.), is cleaned, consolidated, and evaluated and loaded into the Data warehouse. The integration project can run as a job on a regular basis in SQL Server agent.SQL Server Analysis Service, which is used primarily to analyze data in a data warehouse. The analysis development is

The topic of today's blog is: parameter linkage solution. Environment: Data Warehouse + OLAP (cube) + Reporting Service. Effect: multiple parameters (1, 2 ,......) Achieve interaction. First, let's talk about the principle. A data warehouse consists of fact tables and dimensions. I understand that fact tables are actually a table composed of multiple dimensions related to a topic and the values of these dim

directory view that shows a relational cube and a cube in an analytic workspace. The cursor is located on the relational multi-dimensional dataset (Audio Product Sales). Details are displayed on the right panel. Object View The object view provides a graphical navigator for the objects contained in the analysis workspace. Unlike the OLAP directory view, this

OLAP introduction (combined with personal work), olap introduction combined with personalOLTP and OLAPTraditional database systems are OLTP and can only provide raw data operations. Analysis is not supported. OLTP system: Performs online transaction and query processing. General supermarket invoicing system, functions: Registration, accounting, inventory and sales records, etc,

The history and basic concepts of OLAPOLAP is all called online analytical applications, and is a solution for multidimensional data analysis queries. Typical OLAP applications include sales, marketing, management and other business statements, budget accounts, economic statements, and so on.The earliest OLAP query tool was the Express published in 1970, but the complete

information in the analytic workspace. Analytic workspace is an interface required for user session interaction with the Oracle OLAP engine. It is also a container for dimension and cube objects. In Oracle OLAP, the analytic workspace concept is similar to the schema in the database and is also a set of related objects. However, analytic workspace uses a table

hierarchy, a multi-dimensional dataset layer, and a fact object of a multi-dimensional dataset. For each dimension of a multi-dimensional dataset, only one hierarchy can be defined for one cube. However, in a multi-dimensional dataset model, a dimension can have multiple hierarchies. You can use a multi-dimensional dataset to optimize the multi-dimensional dataset model so that it can be more adapted to the most active and important multi-dimensional

OLAP ClientFlexolapview User ManualOverview flexolapview component is a method for multidimensional analysis.FLASHcontrol, available through theXMLAstandard protocol connected toOLAPservers, such as: Microsoft'sSQL ServerAnalysis Server (MS ssas2005/2008) andPentahoAnalysis Services (Mondrianand so on, it can use the pivot table and a variety of charts to display multidimensional Analysis data for real-time business analysis. This article describes

type of product, is clearly differentiated from the same host Transaction Processing (OLTP. It is a bit esoteric, but not complex. There are only three basic concepts of OLAP: multi-dimensional observation, data drilling, and cube operations. Multi-dimensional Perspective: we encounter various problems in our daily work. When we analyze the problem, we will consider the same phenomenon from multiple pers

clearly differentiated from the same host Transaction Processing (OLTP. It is a bit esoteric, but not complex. There are only three basic concepts of OLAP: multi-dimensional observation, data drilling, and cube operations. Multi-dimensional Perspective: we encounter various problems in our daily work. When we analyze the problem, we will consider the same phenomenon from multiple perspectives, sometimes

How to monitor and optimize OLAP databases and monitor olap Databases It is very important to optimize the performance of online analysis and processing. Fortunately, some tools can help monitor and improve the operation of OLAP databases. The Microsoft SQL Server Analysis Service (SSAS) provides a powerful engine used to create and manage data mining applicatio

HBase, key is the combined value of each dimension member, the structure of key under different cuboid is not the same, for example cuboid={brand,product,year} The following key may be brand= ' Nike ', product= ' shoe ', year=2015, then this key can be written as nike:shoe:2015, but if you use this way there will be a lot of repetition, So normally we take all the members of a dimension and then save it in an array, using the array's subscript combination as a key, which can save the key storag

segmentation of dimensions for a particular application is highly flexible and improves the efficiency of data (especially sparse data) analysis. Six, OLAP multidimensional data analysis1. Slicing and dicing (Slice and Dice)In the multidimensional data structure, by two-dimensional slicing, three-dimensional cutting, you can get the required data. such as in the ldquo; city, product, time rdquo; three-dimensional

, you can use multiple tables to describe this star mode extension called "Snowflake mode ". Molap indicates the implementation of OLAP based on multi-dimensional data organization ). Taking multi-dimensional data as the core, that is, molap uses multi-dimensional arrays to store data. Multi-dimensional data will form a "cube" structure in storage, in molap, "Rotate", "cut", and "slice" of "

Before we saw the data and the preprocessing of the data, where was the data after processing? Put it in a place called "Data Warehouse".Basic concepts of data warehousing: Definition of Data Warehouse-topic-oriented, integrated, time-varying, non-volatile Operational database Systems VS data Warehouses-Why use Data Warehouse analytics data (OLAP vs OLTP) Data Warehouse Architecture-three-tier architecture: Bottom (Data Warehouse serv

multi-dimensional data as the core, that is, molap uses multi-dimensional arrays to store data. Multi-dimensional data will be stored"CubeBlock (cube) structure. In molap, "Rotate", "cut", and "slice" of "cube" are the main technologies used to generate multidimensional data reports. Holap indicates the OLAP implementation (Hybrid

be divided into ROLAP, MOLAP, HOLAP. ROLAP represents an OLAP implementation based on relational databases (relational OLAP). The relational database is the core of the multidimensional data representation and storage. ROLAP divides the multidimensional structure of multidimensional databases into two types of tables: a fact table for storing data and dimension keywords, and a dimension table that uses at

(multidimensional OLAP) based on a multidimensional data organization. The core of multidimensional data organization, that is, MOLAP uses multidimensional arrays to store data. Multidimensional data in the storage will form a "cubic block (cube)" Structure, in MOLAP "cubic block" of "rotation", "cut", "slice" is the main technology to produce multidimensional data reports. HOLAP represents an

into relational OLAP (RELATIONALOLAP, referred to as ROLAP) according to the data storage format of their storage. multidimensional OLAP (MULTIDIMENSIONALOLAP, referred to as MOLAP) and hybrid OLAP (HYBRIDOLAP, HOLAP) three types. 1.ROLAP ROLAP stores the parsed multidimensional data in a relational database and, depending on the needs of the application, def