Earticle

연역 데이타베이스가 병렬 컴퓨터 구조에 분할 적재될 때, 내포 술어에 대한 갱신이 결정적일 필요가 있으며, 이 결과를 이용한 내포 술어의 병렬 평가 알고리즘이 요구된 다. 본 논문에서는 병렬 연역 데이타베이스의 내포 술어에 대한 삽입과 삭제가 결정적 인 방법을 제안하고 병렬 영역 데이타베이스를 휘한 병렬 컴퓨터 구도에서 갱신 방 법이 고려된 확장된 병렬 평가 알고리즘을 제안한다. 연역 데이타베이스는 외연적 데이타베이스 즉 사실들의 집합과, 내포적 데이타베이스 즉 규칙들의 집합으로 구성된다. 이 집합들을 여러개의 처리기에 분산 적재 하였을 때, 각각의 처리기에서 갱신 방법과 그 결과를 이용한 병렬 평가방법을 연구한다. 각각의 처리기는 자신의 지역 기억장치를 가지며 연결망을 통하여 서로 메세지를 교환함으로써 통신한다.

The deterministic update method of intensional predicates in a parallel deductive database that deductive database is distributed in a parallel computer architecture in needed. Using updated data from the deterministic update method, a strategy for parallel evaluation of intensional predicates is required. The paper is concerned with an approach to updating parallel deductive database in which very insertion or deletion can be performed in a deterministic way, and an extended parallel semi-naive evaluation algorithm in a parallel computer architecture. After presenting an approach to updating intensional predicates and strategy for parallel evaluation, its implementation is discussed. A parallel deductive database consists of the set of facts being the extensional database and the set of rules being the intensional database. We assume that these sets are distributed in each processor, research how to update intensional predicates and evaluate using the update method. The parallel architecture for the deductive database consists of a set of processors and a message passing network to interconnect these processors.

Querying a database is a common task for most database systems. To query a database is to find some answers from stored data. Traditional database systems return exactly what is being asked. This is a method of direct query answering and users are required to construct a query intelligently and properly. To remove the burden of intelligence from the database users, the concept of intelligent or cooperative query answering has emerged. The process of intelligent query answering consists of analyzing the intent of query, rewriting the query based on the intention and other kinds of knowledge, and providing answers in an intelligent way. Intelligent answers could be generalized, neighborhood or associated information relevant to the query. This concept is based on the assumption that some users might not have a clear idea of the database content and schema. Therefore, it is difficult to pose queries correctly to get some useful answers. Producing answers effectively depends largely on users' knowledge about the query language and the database schema. Knowledge, either intentional or extensional, is the key ingredient of intelligence. In order to improve effectiveness and convenience of querying databases, we design a systematic way to analyze user's request and revise the query with data mining models and materialized views. The models obtained from the automatic knowledge extraction process is a set of association rules discovered from the database contents. Materialized views are pre-computed and normally aggregated data from base tables to speed up the processing of frequently asked queries. This paper presents the knowledge acquisition method focusing on association pattern mining, its implementation, and a systematic method of rewriting query with association patterns and materialized views. We perform preliminary efficiency tests of the proposed system. The experimental results demonstrate the effectiveness of our system in answering queries sharing the same pattern as the available knowledge and the pre-computed views.