Application of Big Data and Data Mining Techniques to Designing Building
Hyoung-Seon Choe1, Jinhwa Kim2
1Hyoung-Seon Choe, Department of School of Business, Sogang University, Shinsoo-Dong, Mapo-Gu, Seoul (Korea), East Asian.
2Jinhwa Kim, Department of School of Business, Sogang University, Shinsoo-Dong, Mapo-Gu, Seoul (Korea), East Asian.
Manuscript received on 20 June 2019 | Revised Manuscript received on 27 June 2019 | Manuscript Published on 22 June 2019 | PP: 214-210 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H10380688S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study suggests an approach in designing new building, specifically a library with peer to peer social big data and survey data. Methods/Statistical analysis: Big data techniques such as social data mining, text mining, and association rule analysis are used in this study. This study uses sentiment analysis and opining mining in analyzing social data. Association rule analysis is used to understand the behavioral patter in survey data on daily movement of users in libraries. Findings: This study confirms that big data techniques such as social data mining, text mining, and association rule analysis can be efficiently applied to designing a building such as a library. Nouns related to library extracted from social media such as Twitter &blogs describe major services and facilities many people want in libraries. Adjectives from social data show that users’ feeling on the libraries. An analysis of data set from actual movement behaviors in the library shows efficient routing for library users. The study finds that data mash-up and big data techniques can help design new building, which is more efficient and convenient for users. Improvements/Applications: Designing a building using more advanced technique such as an artificial intelligence technique is possible with more diverse applications in design areas.
Keywords: Designing Building, Data Mash-Up, Text Mining, Social Mining, Sentiment Analysis, Opinion Mining.
Scope of the Article: Data Mining and Warehousing