• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.184-197
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• 2001

In this paper, we provide the formal specification of a fuzzy object inference language and propose ICOT(Integrated C-Object Tool) as its implementation for knowledge-based programming with the disjunctive fuzzy information. The novelty of our model is that it seamlessly combines object inference and fuzzy reasoning into a unified framework without compromising a compatibility with extant databases, especially object-relational ones. In this model most of the object-oriented paradigm is successfully expressed in terms of relational constructs, tailoring fuzzy reasoning style to be well suited to the framework of the databases. It turns out to be useful in preserving its conceptual simplicity as well, since simple-to-use is one of important criteria in designing the databases. Additionally this model considerably enhanced the semantic expressiveness of data allowing disjunctive fuzzy information.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.103-116
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• 2017

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.774-779
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• 2015

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner using Apache Spark, an in-memory high speed cluster computing environment, which is effective for sequencing and iterating component reasoning jobs. The proposed reasoner can not only check the integrity of a large-scale spatial knowledge base representing topological and directional relationships between spatial objects, but also expand the given knowledge base by deriving new facts in highly efficient ways. In general, qualitative reasoning on topological and directional relationships between spatial objects includes a number of composition operations on every possible pair of disjunctive relations. The proposed reasoner enhances computational efficiency by determining the minimal set of disjunctive relations for spatial reasoning and then reducing the size of the composition table to include only that set. Additionally, in order to improve performance, the proposed reasoner is designed to minimize disk I/Os during distributed reasoning jobs, which are performed on a Hadoop cluster system. In experiments with both artificial and real spatial knowledge bases, the proposed Spark-based spatial reasoner showed higher performance than the existing MapReduce-based one.

• Journal of the Korean Institute of Telematics and Electronics
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• v.15 no.6
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• pp.43-47
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• 1978

This paper presents a technique for symbolic reliability analysis of a general communication network. All simple paths are determined, then they are easily modified to be mutually disjoint by the specific Boolean expressions. A computer program for Binding all of the simple paths between two terminals is also developed. The examples are given to illustrate the technique.

• Journal of Sericultural and Entomological Science
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• v.1
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• pp.9-14
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• 1960

접목법은 우리나라에 있어서의 유일한 상묘생산의 수단인데도 불구하고 해년 그 성적이 좋지 못하여 그 생산에 지대한 지장을 주고 있는 실정에 비추어 필자는 이의 성묘비율을 향상시킬 수 있는 몇 가지 방도에 대하여 이미 실험을 행한 결과 접목후의 가식일수, 이식심도, 묘포의 피복물, 종목채취시기, 시비량등이 접목의 활착이나 묘목의 생육에 영향이 있음을 보고한바 있거니와 필자는 그후 이접하여 국내 주요 상품종에 대하여 품종상호간에 있어서 활착비율에 어떠한 정도의 차이가 있는가 또는 품종의 차이에 따라서 접목적기에 차이가 있는지의 여부와 아울러서 중부지방에 있어서의 접목의 적기를 조사하고 또 한편 우리나라에서 현재 행하고 있는 주요한 각종접목법에 대하여 그 상호간에 있어서의 활착비율의 차이와 묘목의 생육상태 및 접목법의 차이에 따르는 접목적기등을 알기 위하여 4286년부터 5개년간에 걸쳐서 시험을 계속한 결과 그 성적을 얻었으므로 여기에 그 개요를 보고하려고 하는 것이다. (중략)

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.231-242
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• 2016

In order to meet rapid changes of Web information, it is necessary to extend the current Web technologies to represent both the valid time and location of each fact and knowledge, and reason their relationships. Until recently, many researches on qualitative temporal reasoning have been conducted in laboratory-scale, dealing with small knowledge bases. However, in this paper, we propose the design and implementation of a parallel qualitative temporal reasoner, MRQUTER, which can make reasoning over Web-scale large knowledge bases. This parallel temporal reasoner was built on a Hadoop cluster system using the MapReduce parallel programming framework. It decomposes the entire qualitative temporal reasoning process into several MapReduce jobs such as the encoding and decoding job, the inverse and equal reasoning job, the transitive reasoning job, the refining job, and applies some optimization techniques into each component reasoning job implemented with a pair of Map and Reduce functions. Through experiments using large benchmarking temporal knowledge bases, MRQUTER shows high reasoning performance and scalability.