由於建築資訊模型(Building Information Modeling, BIM)涵蓋營建工程各領域之設計屬性資料，因此代表著其可產生許多加值應用，例如對累積的BIM資料進行統計與分析。而以現況來說，對大量BIM資料進行統計與分析是相當不容易的，在現今的商用BIM軟體皆是基於特定檔案格式的專案管理模式，其檔案皆須透過該BIM軟體開啟方能查看三維視覺化模型與各領域之設計屬性資料。此外，雖然現今BIM已有通用檔案格式工業基礎類別(Industry Foundation Classes, IFC)，但由於商用BIM軟體間對BIM之邏輯與定義不盡相同，因此由不同商用BIM軟體所匯出的IFC，匯出的資訊格式與內容很難保持一致性，進而導致BIM之部分資訊遺失，而即使是同一款商用BIM軟體，不同的軟體版本亦很有可能有以上之問題產生。再者，現今商用BIM軟體皆採取單一檔案式的管理模式，意即一份檔案對應一個BIM專案，而單一檔案便涵蓋以上所述之缺點，若欲對一BIM之集合進行集中管理與統計分析，也許當集合內僅有少數BIM專案時尚可執行，但如果當集合內BIM數量龐大，根據上述之狀況，肯定具備相當程度的困難性。
本研究為解決此問題，將透過一能夠對巨量BIM資料進行儲存與集中管理的系統－Cloud BIM進行延伸擴充，透過結合Cloud BIM系統與本研究之系統架構，將可輕易地對巨量BIM資料進行統計與分析。此外，本研究亦加入含BIM建物空間內環境狀態與設施使用歷史紀錄之動態資料，透過加入動態資料，使BIM產生更多元的加值應用。而巨量BIM資料之統計與分析則結合與Cloud BIM緊密結合的MapReduce運行，以大幅提升巨量資料分析之效率。

Regarding the storage and management of massive BIM data, a system which utilizes Cloud technology to establish a data center to store and manage multiple BIMs simultaneously has been developed. This BIM data center can not only handle the big data of massive BIMs using multiple servers in distributed manner, but also can be accessed by using various online devices anywhere anytime for information sharing and visualization. Traditional BIM includes only static information, such as the geometric parameters, physical properties, and spatial relations, for modeling of the physical space. In this study, BIM was extended to dynamic BIM which also includes dynamic data, such as the historical records from monitoring of facility environment and usage, as well as the user experience parameters from continuous observation of the interaction between users and the space. Due to such extension, dynamic BIM become a parametric model which can be used to simulate user behaviors. Consequently, the attribute data of dynamic BIM is not only massive, but also keeps increasing with continuous monitoring of space and users. In addition, information should be effectively retrieved from these large data sets for applications. Regarding the applications of dynamic BIM big data, this study proposes a Cloud-based system framework to effectively retrieve required information for various applications by big data analysis based on parallel processing of large data sets. In addition, this study also do preliminary study on the application of data mining techniques to find hidden dependencies or regularities in the processed data sets for the influences of the states of various facilities on indoor environment, energy consumption, and user behaviors. The results of data mining can serve as the references rules for decision support on facilities control.

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