aggregate data

基本解釋

Mr. Skibiski says that Sense is interested only in aggregate data and that it’s looking for broad patterns, not the specific behavior of individuals.
斯奇比斯基先生（Skibiski）說(shuō)，感覺(jué)公司( Sense)只對(duì)整合的數(shù)據(jù)感興趣而且他們正在尋求對(duì)群體模式的分析而非某個(gè)個(gè)體行為模式。
If you have used the CouchDB database before, you are likely familiar with map/reduce, as the view engine uses map/reduce functions to filter and aggregate data by default.
如果您以前使用過(guò) CouchDB 數(shù)據(jù)庫(kù)，可能熟悉 map/reduce 函數(shù)，因?yàn)橐晥D引擎默認(rèn)使用 map/reduce 函數(shù)來(lái)篩選和聚合數(shù)據(jù)。
Because their systems are not integrated, most enterprises lack the ability to effectively aggregate data, and therefore, must implement lots of costly custom code to satisfy business requirements.
由于系統(tǒng)沒(méi)有集成，大多數(shù)企業(yè)缺乏有效地聚合數(shù)據(jù)的能力，因此必須實(shí)現(xiàn)許多代價(jià)昂貴的自定義代碼來(lái)滿足業(yè)務(wù)需求。

Some might ask why I focus this blog so much on the aggregate data.
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While there may be individual examples of it, our aggregate data shows the opposite is true.
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Looking at the aggregate data, it is clear that there is still progress to be made.
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聚集數(shù)據(jù)
MOLAP supports multi-views and has high query performance that is attributed to its particular multi-dimensional data structure and aggregate data, which is pre-computed abundantly.
其中,MOLAP通過(guò)基于數(shù)組的多維存儲(chǔ)引擎,支持?jǐn)?shù)據(jù)的多維視圖,具有快速的查詢性能,而這主要得益于它獨(dú)特的、以數(shù)據(jù)立方體形式存儲(chǔ)的多維數(shù)據(jù)結(jié)構(gòu),以及存儲(chǔ)在數(shù)據(jù)立方體中的預(yù)處理程度很高的數(shù)據(jù)(即聚集數(shù)據(jù))。
經(jīng)濟(jì)學(xué)
總量數(shù)據(jù)
綜合資料
而組合資料