Jong-Hwa Na

Customer Preference Analysis Based on SNS Data

Due to rapid improvement of information technology, the emergence of various information channels such as mobile devices and social media has been producing tremendous amount of data. The evolution of smartphones and social network services (SNS) leads to the big data era. The research for unstructured, large and varied data, has been going on for more systematic and appropriate ways of collection and analysis. In this paper, Twitter data has been collected, stored and analyzed in a multi-dimensional fashion on top of Hadoop platform in order to find out what kind of factors can affect the customer preference for the smartphones. About 600,000 Twitter data has been collected for one month and the analysis result shows the most popular smartphone, the most interesting attributes in the smartphones, and the maker the customers most interested in.

A Private Cloud System for Web-based High-Performance Multiple Sequence Alignment Services

We proposed a LanLinux-based cloud system for ClustalW-MPI, a parallel implementation of Clustal-W based on MPI, where researchers can submit their sequence data online for multiple sequence alignment. ClustalW is one of the most widely used programs for multiple sequence alignment (MSA) in bioinformatics. However, current in-silico environmental conditions for MSAs are facing computing power problems. The proposed system uses the MPICH2 (a standard message-passing interface for distributed-memory applications used in parallel computing) for handling all the tasks associated with the multiple sequence alignment on the Web. It provides sufficient computing power for aligning large number of sequences at a time, with real-time monitoring capabilities to ensure correctness, efficiency and effectiveness.

Performance Evaluation of Apache Spark According to the Number of Nodes using Principal Component Analysis

With the development of big data collection and storage technology, an analysis for its utilization has recently been expanded in public sector and various industries. Especialy in manufacturing and financial sectors, there has been a very high demand for real-time analysis of big data. Existing studies on the big data analysis mainly dealt with its batch scheme as a premise. In recent years, studies related to real-time analytics using SPARK, STORM and IMDG have been underway. In this regard, this paper seeks to evaluate the processing performance of the principal component analysis using an open sourse SPARK which is in-memeory based distributed processing method. It is necessary for real-time analysis and fast operation of large amount of data. This paper shows how fast spark is by comprison with open source R and also investigate the distributed processing capability of Spark according to the Node configuration.