olap 多維分析

In the previous article of OLAP, we have seen various applications of OLAP, Various types of OLAP, advantages, and disadvantages of OLAP. In this article, we will learn about the,

在OLAP的上一篇文章中，我們了解了OLAP的各種應(yīng)用，各種類(lèi)型的OLAP，OLAP的優(yōu)缺點(diǎn) 。 在本文中，我們將了解

OLAP Cube

OLAP多維數(shù)據(jù)集

OLAP Cube Operations

OLAP多維數(shù)據(jù)集操作

1)OLAP多維數(shù)據(jù)集 (1) OLAP Cube)

An OLAP cube may be a multi-dimensional array of knowledge. The online analytical process (OLAP) may be a computer-based technique of analyzing knowledge to appear for insights. The term cube here refers to a multi-dimensional dataset, that is additionally known as a Hypercube if the quantity of dimensions is larger than three. A cube is also thought of as a multi-dimensional generalization of a two- or three-dimensional program. For instance, a corporation may like to summarize monetary knowledge by product, by time-period, and by the town to check actual and budget expenses. Product, time, cities and situation (actual and budget) are the data's dimensions as shown in the figure. A cube isn't a "cube" within the strict mathematical sense, as all the edges aren't essentially equal.

OLAP多維數(shù)據(jù)集可以是知識(shí)的多維數(shù)組。 在線(xiàn)分析過(guò)程(OLAP)可能是一種基于計(jì)算機(jī)的技術(shù)，用于分析知識(shí)以獲取見(jiàn)解。 術(shù)語(yǔ)“多維數(shù)據(jù)集”在這里是指多維數(shù)據(jù)集，如果維的數(shù)量大于3，則又稱(chēng)為“超多維數(shù)據(jù)集”。 多維數(shù)據(jù)集也被認(rèn)為是二維或三維程序的多維概括。 例如，公司可能希望按產(chǎn)品，時(shí)間段和鎮(zhèn)來(lái)匯總貨幣知識(shí)，以檢查實(shí)際和預(yù)算支出。 產(chǎn)品，時(shí)間，城市和情況(實(shí)際和預(yù)算)是數(shù)據(jù)的尺寸，如圖所示。 在嚴(yán)格的數(shù)學(xué)意義上，多維數(shù)據(jù)集不是“多維數(shù)據(jù)集”，因?yàn)樗械倪厡?shí)際上都不相等。

Fig 1: OLAP Cube

圖1：OLAP多維數(shù)據(jù)集

2)OLAP多維數(shù)據(jù)集操作 (2) OLAP Cube Operations)

Since OLAP servers work on data of multidimensional view, we are going to discuss OLAP operations in multidimensional information.

由于OLAP服務(wù)器可以處理多維視圖的數(shù)據(jù)，因此我們將在多維信息中討論OLAP操作。

Here is the list of OLAP operations,

這是OLAP操作的列表，

• Roll-up

  卷起

• Drill-down

  下鉆

• Slice

  片

• Dice

  骰子

• Pivot (rotate)

  樞軸旋轉(zhuǎn)

2.1。 卷起 (2.1. ROLL UP)

Roll-up is performed by rising up a planning hierarchy for the dimension location. Initially the conception hierarchy was "street < town < province < country". On rolling up, aggregation of data is done by ascending the hierarchy of location from the level of street to the level of the country. A roll-up involves summarizing the information on a dimension. The summarization rule may be Associate in Nursing mixture operate, like computing totals on a hierarchy or applying a collection of formulas like "profit = sales - expenses". General aggregation functions are also pricey to cipher once rolling up: if they can not be determined from the cells of the cube, they need to be computed from the bottom information, either computing them on-line (slow) or precomputing them for attainable rollouts (large space). Aggregation functions that will be determined from the cells square measure referred to as complex aggregation functions, and permit economical computation.

匯總是通過(guò)提升維度位置的計(jì)劃層次結(jié)構(gòu)來(lái)執(zhí)行的。 最初，概念層次是“街道<城鎮(zhèn)<省<國(guó)家”。 匯總時(shí)，數(shù)據(jù)的匯總是通過(guò)將位置層次結(jié)構(gòu)從街道級(jí)別提升到國(guó)家/地區(qū)級(jí)別來(lái)完成的。 匯總涉及匯總有關(guān)維度的信息。 匯總規(guī)則可以是“護(hù)理中的助理人員”混合操作，例如在層次結(jié)構(gòu)上計(jì)算總計(jì)或應(yīng)用諸如“利潤(rùn)=銷(xiāo)售-費(fèi)用”之類(lèi)的公式集合。 通用聚合函數(shù)一旦匯總就需要付出昂貴的代價(jià)：如果無(wú)法從多維數(shù)據(jù)集的單元中確定它們，則需要從底層信息中進(jìn)行計(jì)算，要么在線(xiàn)(緩慢)計(jì)算它們，要么對(duì)其進(jìn)行預(yù)先計(jì)算以實(shí)現(xiàn)可擴(kuò)展性(大空間)。 由單元平方確定的聚合函數(shù)稱(chēng)為復(fù)雜聚合函數(shù)，可以經(jīng)濟(jì)地進(jìn)行計(jì)算。

For example, it is easy to calculate COUNT, MAX, MIN, and SUM in OLAP, as this can be computed for each cell of the OLAP cube and then rolled up, since on overall sum (or count, etc.) is the sum of sub-sums, but it is difficult to support MEDIAN, as that must be computed for every view separately: the median of a collection isn't the median of medians of subsets.

例如，很容易在OLAP中計(jì)算COUNT，MAX，MIN和SUM，因?yàn)榭梢詫?duì)OLAP多維數(shù)據(jù)集的每個(gè)單元格進(jìn)行計(jì)算，然后匯總，因?yàn)榭偤?或計(jì)數(shù)等)是總和子和，但很難支持MEDIAN，因?yàn)楸仨毞謩e針對(duì)每個(gè)視圖進(jìn)行計(jì)算：集合的中位數(shù)不是子集的中位數(shù)。

Let us understand it with a diagrammatic flow,

讓我們以圖解流程了解它，

Fig 2.1 : Roll up Operation

圖2.1：匯總操作

2.2。 向下鉆取 (2.2. DRILL DOWN)

Drill-down is performed by stepping down the hierarchy for the dimension time. Initially, the conception hierarchy was "day < month < quarter < year." On drilling down, the dimension of time exists in descended from i.e. from the extent of the quarter to the extent of the month. When drill-down is performed, one or additional dimensions from the information cube are supplemental. It navigates knowledge from less elaborate information to extremely elaborate data.

通過(guò)在維度時(shí)間上降低層次結(jié)構(gòu)來(lái)執(zhí)行向下鉆取。 最初，概念層次結(jié)構(gòu)是“天<月<季度<年”。 在向下鉆取時(shí)，時(shí)間的維數(shù)是從四分之一的范圍降到一個(gè)月的范圍。 執(zhí)行向下鉆取時(shí)，信息多維數(shù)據(jù)集中的一個(gè)或其他維度是補(bǔ)充。 它可以將知識(shí)從不太復(fù)雜的信息導(dǎo)航到極其復(fù)雜的數(shù)據(jù)。

Fig 2.2 : Drill Down Operation

圖2.2：下鉆操作

2.3。 OLAP切片 (2.3. OLAP SLICING)

The slice operation selects one specific dimension from a given cube and provides a replacement sub-cube.

切片操作從給定的多維數(shù)據(jù)集中選擇一個(gè)特定的維度，并提供替換子多維數(shù)據(jù)集。

Slice is that the act of selecting an oblong set of a cube by selecting one worth for one in all its dimensions, making a replacement cube with one fewer dimension.

切片是指通過(guò)在其所有維度中為一個(gè)立方體選擇一個(gè)值來(lái)選擇一個(gè)長(zhǎng)方形的立方體的動(dòng)作，從而使替換的立方體的尺寸減少了一個(gè)。

The picture shows a slicing operation.

圖片顯示了切片操作。

Slicing is performed for the dimension "time" using the criteria time = "Q1".

使用標(biāo)準(zhǔn)時(shí)間=“ Q1”對(duì)維“時(shí)間”執(zhí)行切片。

Subcube is formed by using or selecting one or two dimensions.

通過(guò)使用或選擇一維或二維來(lái)形成子多維數(shù)據(jù)集。

Fig 2.3 : OLAP Slicing

圖2.3：OLAP切片

2.4。 OLAP骰子 (2.4. OLAP DICING)

Dice selects 2 or additional dimensions from a given cube and provides a brand new sub-cube. think about the subsequent diagram that shows the dice operation. The dice operation produces a subcube by permitting the analyst to choose specific values of multiple dimensions. the image shows a dicing operation. The dicing operation on the cube which involves three dimensions is based on the following selection criteria,

Dice從給定的多維數(shù)據(jù)集中選擇2個(gè)或其他維度，并提供一個(gè)全新的子多維數(shù)據(jù)集。 考慮下面顯示骰子操作的圖。 骰子操作通過(guò)允許分析人員選擇多維的特定值來(lái)生成子多維數(shù)據(jù)集。 該圖顯示了切塊操作。 涉及三個(gè)維度的多維數(shù)據(jù)集上的切塊操作基于以下選擇標(biāo)準(zhǔn)，

• (location = "Toronto" or "Vancouver")

  (位置=“多倫多”或“溫哥華”)

• (time = "Q1" or "Q2")

  (時(shí)間=“ Q1”或“ Q2”)

• (item =" Mobile" or "Modem")

  (項(xiàng)目=“移動(dòng)”或“調(diào)制解調(diào)器”)

Fig 2.4 : Cube Dicing

圖2.4：立方體切丁

2.5。 OLAP PIVOT (2.5. OLAP PIVOT)

The pivot operation is additionally referred to as rotation. It rotates the info axes seeable to produce an alternate presentation of knowledge. take into account the subsequent diagram that shows the pivot operation. Pivot permits AN analyst to rotate the cube in the area to check its varied faces. As an example, cities may well be organized vertically and product horizontally whereas viewing information for a selected quarter. Pivoting may replace the product with periods to check information across time for one product. The picture shows a pivoting operation: the full cube is turned, giving another perspective on the data.

樞轉(zhuǎn)操作另外稱(chēng)為旋轉(zhuǎn)。 它旋轉(zhuǎn)可見(jiàn)的信息軸以產(chǎn)生替代的知識(shí)表示。 請(qǐng)考慮以下顯示樞軸操作的圖表。 Pivot允許分析人員旋轉(zhuǎn)該區(qū)域中的多維數(shù)據(jù)集以檢查其變化的面。 例如，在查看選定季度的信息時(shí)，很可能垂直組織城市，水平組織產(chǎn)品。 數(shù)據(jù)透視可能會(huì)用句點(diǎn)替換產(chǎn)品，以跨時(shí)間檢查一種產(chǎn)品的信息。 該圖顯示了樞軸操作：旋轉(zhuǎn)了整個(gè)立方體，從而為數(shù)據(jù)提供了另一個(gè)視角。

Fig 2.5 : Olap Pivot

圖2.5：Olap樞軸

翻譯自: https://www.includehelp.com/basics/olap-online-analytical-processing-olap-cube-and-operations.aspx

olap 多維分析

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