近年來建築資訊模型(Building Information Modeling, BIM)雖廣泛應用於營建產業中規劃及設計等生命週期之階段，但目前尚未見從使用者的角度來思考BIM於空間內環境與設施之生命週期管理應用，如果將人與建築空間之互動情形等因素考量進BIM的概念中，並整合空間內環境數據及設施使用歷史紀錄，以持續觀察人與建築的互動情形所取得之使用者經驗參數，此BIM將可用來模擬各種使用情境，使BIM變成為一個具經驗參數化的模型，而這些歷史紀錄經過長時間的累積則會成為巨量資料，如何在這些巨量資料中去尋找有用的資訊，並達到營建工程生命週期上營運維護的助益，則具備相當程度的挑戰性。
本研究透過一雲端化之資料儲存與管理系統-Cloud BIM，進行延伸性擴充結合了巨量資料分析流程與資料探勘技術，其利用分散式技術MapReduce對巨量BIM資料進行處理，並整合資料探勘工具Mahout與R語言進行資料探勘。本研究針對一棟兩層樓辦公建築實作一巨量分析應用案例，首先將模型以IFC格式上傳至系統，讓使用者利用該模型進行模擬監測以獲得動態資料，並將大量取得之資料進行上傳與儲存，接著利用MapReduce進行巨量資料之前處理，再透過本研究於系統所擴充的資料探勘功能，進行資料的深度分析與建立各種分析模型，最後藉由模型分析之結果來找出資料之間所隱含的資訊或人與建築空間互動的潛在訊息，以輔助營建工程生命週期中營運維護階段之管理決策，提供營運管理者進行決策時可有一參考依據，經由此案例之實作來證明本研究於系統所整合的巨量資料分析功能於營運維護上之運用的可行性。

In this study, 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 a distributed manner, but can also 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 historical records from the monitoring of the 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 an extension, dynamic BIM became a parametric model which can be used to simulate user behaviors. Regarding the applications of dynamic BIM big data, this study proposed a big data preprocessing framework based on the MapReduce distributed computing for the data of massive BIMs stored in the CloudBIM system. Mahout and R were integrated into the system as the data mining tools to analyze the data after preprocessing. In a case study, the proposed system was applied to find hidden dependencies or regularities in the processed data sets for the influences of the states of a two story building 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.

[1] 林宗禧，「Cloud BIM: 應用雲端運算與WebGL技術之網路式BIM系統」，碩士論文，國立臺灣科技大學，臺北 (2012)。
[2] 張凱筌，「雲端化之巨量BIM資料儲存與分析系統架構」，碩士論文，國立臺灣科技大學，臺北 (2014)。
[3] Popov, V., Juocevicius, V., Migilinskas, D., Ustinovichius, L. and Mikalauskas, S., “The Use of A Virtual Building Design and Construction Model for Developing An Effective Project Concept in 5D Environment”, Automation in Construction, Vol. 19, Issue 3, pp. 357-367 (2010).
[4] Leite, F., Akcamete, A., Akinci, B., Atasoy, G. and Kiziltas, S., “Analysis of modeling effort and impact of different levels of detail in building information models”, Automation in Construction, Vol. 20, Issue 5, pp. 601-609 (2011).
[5] Lin, S.-H., Kensek, K. and Haymond, L., “Analytical Building Information Modeling: What is the Gap Between BIM and Energy Simulation Tools’ Performance Feedback Loops?”, Proceedings of Ecobuild 2010Ecobuild 2010, Washiton, DC, USA. (2010).
[6] Sanguinetti, P., Abdelmohsen, S., Lee, J., Lee, J., Sheward, H. and Eastman, C., “General system architecture for BIM: An integrated approach for design and analysis”, Advanced Engineering Informatics, Vol. 26, Issue 5, pp. 317-333 (2012).
[7] Jensen, K., “CRISP-DM Process Diagram”,
http://commons.wikimedia.org/wiki/File:CRISP-M_Process_Diagram.png
(2012).
[8] Shearer, C., “The CRISP-DM Model: The New Blueprint for Data Mining”, Journal of Data Warehousing, Vol. 5, No. 4, pp. 13-22 (2000).
[9] Yakushev, A. and Mityagin, S., “Social Networks Mining for Analysis and Modeling Drugs Usage”, Procedia Computer Science, Vol. 29, pp. 2462-2471 (2014).
[10] Wang, Z., Tu, L., Guo, Z., Yang, L. T. and Huang, B., “Analysis of user behaviors by mining large network data sets”, Future Generation Computer Systems, Vol. 37, pp. 429-437 (2014).
[11]巨量資料來襲雲端運算新企機研討會, Information Security資安人科技網, http://www.informationsecurity.com.tw/article/
article_detail.aspx?aid=7472 (2013).
[12] Big Data - What is Big Data - 3 Vs of Big Data - Volume, Velocity and Variety - Day 2 of 21 | Journey to SQL Authority with Pinal Dave,http://blog.sqlauthority.
com/2013/10/02/big-data-what-is-big-data-3-vs-of-big-data-volume-velocity-and-variety-day-2-of-21 (2013).
[13] Singh, K., Guntuku, S. C., Thakur, A. and Hota, C., “Big Data Analytics framework for Peer-to-Peer Botnet detection using Random Forests”, Information Sciences, Vol. 278, pp. 488-497 (2014).
[14] Apache™ Hadoop, http://hadoop.apache.org/ (2015).
[15] Apache™ Hive, http://hive.apache.org/ (2015).
[16] Apache™ Mahout, https://mahout.apache.org/ (2015).
[17] Ackermann, K. and Angus, S. D., “A Resource Efficient Big Data Analysis Method for the Social Sciences: the case of global IP activity”, Procedia Computer Science, Vol. 29, pp. 2360-2369 (2014).
[18] Steed, C. A., Ricciuto, D. M., Shipman, G., Smith, B., Thornton, P.E., Wang, D., Shi, X. and Williams, D. N., “Big data visual analytics for exploratory earth system simulation analysis”, Computer & Geosciences, Vol. 61, pp. 71-82 (2013).
[19] Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M. and Welton, C., “MAD Skills: New Analysis Practices for Big Data”, Proceedings of the VLDB Endowment, Vol. 2, No. 2, pp. 1481-1492 (2009).
[20] Apache™ HBase, http://hbase.apache.org/ (2015).
[21] Apache™ MapReduce,
http://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html (2015).
[22] EnergyPlus, http://apps1.eere.energy.gov/buildings/energyplus/ (2015).
[23] The R Project for Statistical Computing, http://www.r-project.org/ (2015).
[24] Google Inc., http://www.google.com/ (2015).
[25] 陳景祥，R軟體應用統計方法，東華書局，臺北，第3頁 (2015)。
[26] Java, http://www.oracle.com/technetwork/java/index.html (2015).
[27] HTML語法教學, http://www.powmo.com/ (2014).
[28] PHP, https://www.php.net/ (2015).
[29] RapidMiner Studio - RapidMiner, http://rapidminer.com/products/rapidminer-studio/ (2014).
[30] GPUMiner - Parallel Data Mining on Graphics Processors - Google Project Hosting, https://code.google.com/p/gpuminer (2009).