중요 장소 추출 연구, 사용자 행위 추론 연구, 목적지 예측 연구 를 수행하기 위해 구축한 라이프 로그 수집 플랫폼은 본 연구를 통 해 라이프 로그 연구를 수행하기 위해 필요한 과정임을 확인하였다. 구축한 플랫폼의 범용성 및 활용성을 개선하기 위해서는 시간이나 공간에 의한 1차원적인 조건을 기반으로 작업을 수행하는 것에서 더 나아가 복합적인 조건에 대해서 작업을 수행할 수 있도록 확장 하고, 점점 더 중요성이 높아져가는 개인정보 보호 및 보안을 위한 시스템 고도화의 수행이 필요하고, 이러한 일련의 개선 과정을 통해 좀 더 다양한 연구에 대해 유연하게 실험을 수행할 수 있은 플랫폼 을 제공해 줄 수 있을 것으로 기대된다.
본 연구에서 수행한 중요 장소 추출 연구는 이상점이 다수 존재 하는 현실적인 상황에서 장소를 추출하는 방법론을 제시하였다. 제 시한 방법론은 행위 추론 연구를 위한 중요 장소 추출에 활용되어 그 효과성을 확인하였지만 그 이외의 응용에 대해서는 적용사례가 부족하기 때문에 이에 대한 추후 연구가 필요할 것으로 판단된다. 또한 제시한 방법론은 개인별 중요 장소 추출에 집중한 모형이었지 만 다수의 사용자들에 대한 정보가 확보되면 이를 바탕으로 다중 사용자를 위한 중요 장소 추출 모형으로 개선하는 것도 추후 연구 과제라고 할 수 있다. 마지막으로 장소에 대한 의미 정보가 점차 누 적되고 공개되고 있는 상황에서 이들 정보와 융합하여 좀 더 정교 하게 의미 장소를 추출하거나, 더 나아가 숨겨져 있던 새로운 장소 구분을 도출하는 방향으로 연구가 가능할 것으로 기대된다.
본 연구에서 제안한 행위 추론 모형은 고수준 행위를 현재와 과
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거의 데이터를 기반으로 예측하는 방법론을 제시하였으며, 기존 대 비 개선된 성능을 보여줌을 확인하였다. 하지만 많은 고수준 행위의 경우 실제적 응용에 사용하기에는 부족한 분류 성능을 제공하기 때 문에 이를 개선할 수 있는 방법론에 대한 지속적 연구가 필요하다 고 할 수 있다. 본 연구에서 사용한 고수준 행위 분류는 관리의 관 점에서 편의를 위해 구분한 분류체계로 기계가 학습을 수행하기에 적합한 분류체계와는 차이가 존재할 수 있다. 따라서 학습에 적합한 형태의 새로운 계층형 고수준 분류 체계를 확립하고 이를 바탕으로 행위 추론을 하는 연구가 필요할 것으로 판단된다.
사용자에 대한 과거와 현재에 대한 이동 기록이 주어졌을 때 방 문할 목적지를 예측하는 방법론을 제안하였으며, 장소들에 대한 사 전 정보가 주어지지 않는 응용에 대해서는 기존 방법론 대비 개선 된 성능을 제공함을 확인하였다. 목적지를 예측하기 위해 사용되는 정보가 적다는 것은 다양한 응용에 폭넓게 사용할 수 있다는 장점 이 있는 반면, 장소에 대한 의미 정보나 사용자의 기호 정보와 같은 예측의 성능을 개선할 수 있는 정보가 주어진 경우에는 이를 활용 하지 못한다는 한계점이 있다. 따라서 사용자의 이동 기록 이외에 이러한 추가 정보가 주어졌을 시 이를 함께 반영하여 예측의 성능 을 높이는 연구를 병행할 필요성이 있다. 또한 사람을 대상으로 하 여 일주일 기반의 주기성을 가정하고 예측을 수행하였지만, 더 나아 가 주기성을 자동으로 도출하고 이를 활용하는 추가 연구가 필요할 것으로 판단된다.
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