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The Study on the Analysis System of Effective Attack Patterns in Kendo Match using Hadoop-based Hive

Affiliations

  • Department of Computer Science and Engineering, Kongju National University, Chungnam Cheonam Subuk Cheonam-Dearo 1223-24 (Budeadong275), 32588, South Korea

Abstract


Objectives: To analyze the effective attack pattern of Kendo for the use of effective training through Hive. Methods/ Statistical Analysis: The qualified three referees of Kendo competition analyze a video image of a real contest to extract the attack pattern, and store the extracted data in a database by pattern and in HDFS through the classification process. After purifying the stored data through the Mapping and Reducing, the results are extracted by using HiveQL, one of the sub-project ecosystems of Hadoop. Findings: In this paper, by using Hadoop and analysis tool Hive, a big data analysis system for performance enhancement of Kendo athlete is proposed. Experiment subjects were implemented for one game, but after collecting and purifying the recorded historical data over several years, various types of information will be collected. For example, many types of information can be collected such as changes in the age-specific, player-specific pattern, the contextual pattern, and so on by using such information effectively. Then, it may provide a pattern of the Kendo training. Improvements/Applications: In the future research, the methods of collecting data by image analysis techniques and the coaching environment for research and Kendo training should be studied continuously.

Keywords

Data Collection, HDFS, Hadoop, Hive, Kendo.

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