即時分析處理（OLAP）已經成功地被用來分析行銷、金融、保險、工業製造等領域的資料。OLAP提供了良好的分析概念來分析資料庫當中的多維度資料。當收集到來自應用端的大量資料時，企業或組織使用OLAP的動作來分析資料庫中資料。將即時分析組裡的概念結合MapReduce的分散式運算框架，我們可以在具擴展性的分散式運算環境中透過OLAP的動作來分析資料庫中的巨量資料。然而，當前的OLAP分析系統的處理流程無法滿足所有OLAP分析動作中的代數運算，使得針對即時分析處理的核心－「即時」的表現仍然有成長的空間；現今的資料倉儲中，Dimension Attribute或Dependent Attribute資料數量也持續的增大，無法滿足「即時」需求的問題會變得更嚴重。為了能夠將巨量資料進行OLAP分析動作的處理，本篇論文提出一個有效的方法來滿足即時分析處理的核心－「即時」需求。我們針對OLAP分析動作的兩種基本數學代數運算式配合不同的資料量大小下進行了實驗，結果顯示效能皆優於其他系統。我們相信仍然有方法可以持續提升我們系統的處理效能，期許未來能夠繼續精進系統中各個元件的方法，成為一個更完整且更實用的系統。

On-line analytical processing (OLAP) provides analysis of multi-dimensional data stored in a database and achieves great success in many applications such as sales, marketing, manufacturing, insurance and financial data analysis. OLAP operation is a dominant part of data analysis for enterprises and organizations, especially when addressing a large amount of data collected from these applications. With the emergence of the MapReduce paradigm, OLAP operation can be processed on big data that resides in scalable, distributed storage. However, current MapReduce implementations of OLAP operation processing have a major performance drawback caused by improper processing procedure. This is crucial when dimension or dependent attributes are large, which is a common case for most data warehouses hold nowadays. To tackle this issue, this paper proposes a powerful methodology to accelerate the performance of OLAP operation processing on big data. We have conducted the experiments on both of the basic algebra of OLAP operations with different data sizes to demonstrate the effectiveness of our system.

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