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생활 패턴 인지를 위한 이벤트 연산 기반 예측 모델 학습 기법
배석현(Seok-Hyun Bae),방성혁(Sung-hyuk Bang),박현규(Hyun-Kyu Park),전명중(Myung-Joong Jeon),김제민(Je-Min Kim),박영택(Young-Tack Park) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.5
Several studies have been conducted on data analysis and predicting results with the advance of machine learning algorithms. Still, there are many problems of cleaning the noise of the real-life dataset, which is disturbing a clear recognition on complex patterns of human intention. To overcome this limitation, this paper proposes an event calculus methodology with 3 additional steps for the recognition of human intention: intention reasoning, conflict resolution, and noise reduction. Intention reasoning identifies the intention of the living pattern time-series data. In conflict resolution, existing ongoing intentions and inferred intention are checked by a conflict graph, so that the intentions that can occur in parallel are inferred. Finally, for noise reduction, the inferred intention from the noise of living pattern data is filtered by the history of fluent. For the evaluation of the event calculus module, this paper also proposes data generation methodology based on a gaussian mixture model and heuristic rules. The performance estimation was conducted with 300 sequential instances with 5 intentions that were observed for 13 hours. An accuracy of 89.3% was achieved between the probabilistic model and event calculus module.
분산 처리 환경에서 SWRL 규칙을 이용한 대용량 점증적 추론 방법
이완곤(Wan-Gon Lee),방성혁(Sung-Hyuk Bang),박영택(Young-Tack Park) Korean Institute of Information Scientists and Eng 2017 정보과학회논문지 Vol.44 No.4
As we enter a new era of Big Data, the amount of semantic data has rapidly increased. In order to derive meaningful information from this large semantic data, studies that utilize the SWRL(Semantic Web Rule Language) are being actively conducted. SWRL rules are based on data extracted from a user’s empirical knowledge. However, conventional reasoning systems developed on single machines cannot process large scale data. Similarly, multi-node based reasoning systems have performance degradation problems due to network shuffling. Therefore, this paper overcomes the limitations of existing systems and proposes more efficient distributed inference methods. It also introduces data partitioning strategies to minimize network shuffling. In addition, it describes a method for optimizing the incremental reasoning process through data selection and determining the rule order. In order to evaluate the proposed methods, the experiments were conducted using WiseKB consisting of 200 million triples with 83 user defined rules and the overall reasoning task was completed in 32.7 minutes. Also, the experiment results using LUBM bench datasets showed that our approach could perform reasoning twice as fast as MapReduce based reasoning systems.