규칙 기반 시스템은 비교적 간단한 규칙을 사용하여 데이터로부터 유효한 정보를 추출하는 인공 지능 응용에 사용된다. 규칙 기반 시스템은 일반적으로 학습이 없으므로 시스템 설치 후 즉시 작동하는 장점이 있으나, 규칙 매칭에 복잡한 처리가 필요하다. 기존의 규칙 매칭 방법들은 규칙들이 서로 중첩되는 조건을 가질 때 빠르지만, 조건들이 중첩되지 않는 경우 검색 속도가 저하된다. 본 논문에서는 규칙의 조건이 중첩되지 않을 때 규칙 검색 시간을 단축하기 위한 다단계 인덱싱 방법을 제안한다. 제안된 방법은 2단계 검색 트리를 사용하며, 트리의 각 레벨에서 서로 다른 값을 사용하여 규칙을 분류한다. 첫 번째 단계에서는 조건에 포함된 센싱 값의 수를 사용하며, 두 번째 단계에서는 각 센싱 값의 범위를 사용하였다. 분석적 방법을 통해 최적의 서브 그룹 수를 정의하였으며, 실험 결과를 통해 제안된 방법의 빠른 검색 시간을 확인하였다. 검색 시간은 기존의 PHREAK 방식보다 약 3 배 정도 빠르다.
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