Big Data Preprocessing for Predicting Box Office Success
Hee-Gook Jun, Geun-Soo Hyun, Kyung-Bin Lim, Woo-Hyun Lee, Hyoung-Joo Kim · 정보과학회 컴퓨팅의 실제 논문지 · 2014
The Korean film market has rapidly achieved an international scale, and this has led to a need for decision-making based on analytical methods that are more precise and appropriate. In this modern era, a highly advanced information environment can provide an overwhelming amount of data that is generated in real time, and this data must be properly handled and analyzed in order to extract useful information. In particular, the preprocessing of large data, which is the most time-consuming step, should be done in a reasonable amount of time. In this paper, we investigated a big data preprocessing method for predicting movie box office success. We analyzed the movie data characteristics for specialized preprocessing methods, and used the Hadoop MapReduce framework. The experimental results showed that the preprocessing methods using big data techniques are more effective than existing methods.