• 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 Elementary Mathematics Education in Korea
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• v.14 no.2
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• pp.401-420
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• 2010

The purpose of this research is to find out effectiveness of geometry learning through spatial reasoning activities on mathematical problem solving ability and mathematical attitude. In order to proof this research problem, the controlled experiment was done on two groups of 6th graders in N elementary school; one group went through the geometry learning style through spatial reasoning activities, and the other group went through the general geometry learning style. As a result, the experimental group and the comparing group on mathematical problem solving ability have statistically meaningful difference. However, the experimental group and the comparing group have not statistically meaningful difference on mathematical attitude. But the mathematical attitude in the experimental group has improved clearly after all the process of experiment. With these results we came up with this conclusion. First, the geometry learning through spatial reasoning activities enhances the ability of analyzing, spatial sensibility and logical ability, which is effective in increasing the mathematical problem solving ability. Second, the geometry learning through spatial reasoning activities enhances confidence in problem solving and an interest in mathematics, which has a positive influence on the mathematical attitude.

• The Mathematical Education
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• v.59 no.2
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• pp.113-130
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• 2020

The purpose of this study is to analyze how well middle school students understand space geometrical concept related to a cylinder. To this end, we developed the test tool based on prior research and examined 433 middle school students in November and December, 2018. And in March 2019, we interviewed 4 students who showed some type of errors. The difference in the correct answer rate of the questions by the grade and gender was tested, and the error type was analyzed based on the student's responses to the questions to evaluate the spatial reasoning ability. The results of this study are as follows. First, the difference by graders was not statistically significant in the questions evaluating spatial visual ability. On the other hand, in the case of the two questions for evaluating spatial measurement ability and spatial reasoning ability, the difference in the correct answer rate between the 7th graders and 8th is not significant, but the difference between lower graders and 9th was significant. Second, there was no significant difference in the spatial geometric ability of all girls and boys participating in this study. Third, analyzing the student's error type for an item which assessed spatial reasoning ability, we found that there are various error types in relation to visual, manipulative, and reasoning errors.

• Korean Journal of Cognitive Science
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• v.15 no.4
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• pp.15-20
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• 2004

To develop a cognitive system with the flexibility and breadth of human, it's very important to construct a large scale knowledge base which include commonsense knowledge as well as expert knowledge. Efficient knowledge representation and reasoning techniques will play a key role for this. This paper introduce a cognitive system which is based on Cyc knowledge base and augmented with our work on qualitative and spatial representation and reasoning. Our system has been implemented and tested on various examples.

• Journal of The Korean Association For Science Education
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• v.17 no.1
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• pp.75-83
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• 1997

The purpose of this study was to determine the strategies that middle school students used in solving problems concerning density and solubility. These were compared in the aspects of problem contexts for 42 students of varying logical reasoning ability, spatial ability, and learning approach. A coding scheme used consists of five categories: reading & organization, production, errors, evaluation, and strategy. Students' protocols were analyzed after intercoder agreement had been established to be .95. The results were as follows: 1. Students had more difficulties in reading and organizing the problems in everyday contexts than in scientific contexts. Students at the concrete-operational stage and / or surface approach were more likely to have difficulties in reading and organizing the problems than those at the formal-operational stage and / or deep approach. 2. Students tended to split up the solubility problems into sub-problems and to solve the density problem in everyday contexts in random manner. These were significantly correlated with the test scores concerning logical reasoning ability, spatial ability, and learning approach at the .1 level of significance. 3. Major errors in solving the density problems were to disregard the given information or generated and to use inappropriate information. Many errors in solving the solubility problems were found to be executive errors. The strategy to use the information given appropriately was positively related to students' logical reasoning ability, spatial ability, and learning approach. 4. More evaluation strategies were found in everyday contexts. Their strategies to grasp the meaning of answers and to check the math were significantly related to students' logical reasoning ability. 5. Students used the random trial-and-error strategy more than the systematic strategy and the systematic trial-and-error strategy, especially in everyday contexts. The strategies used by the students were significantly related to students' logical reasoning ability, spatial ability, and learning approach.

• Architectural research
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• v.17 no.2
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• pp.57-64
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• 2015

In architectural planning, previous cases of similar spatial program provide important data for architectural design. Case-based reasoning (CBR) paradigm in the field of architectural design is closely related to the designing behavior of a planner who makes use of similar architectural designs and spatial programs in the past. In CBR, spatial graph can be constituted with most fundamental data, which can provide a method of searching spatial program by using visual graphs. This study developed a system for CBR that can analyze the similarity through graph comparison and search for buildings. This is an integrated system that is able to compare space similarity of different buildings and analyze their types, in addition to the analysis on a space within a single structure.

• Korean Journal of Cognitive Science
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• v.22 no.1
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• pp.19-38
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• 2011

When quantitative representation and reasoning about space is difficult or impossible in a real world, we can use qualitative representation and reasoning. RCC-8 is a well-known qualitative method for 2D space. RCC-8, in which a basic ontological primitive entity for space is a region, shows the connection-based logics and the conceptual neighbors and transitions of topological status between two regions. The transitions happen by changing position or size of regions. However, more aspects have to be considered for representing and reasoning of the world. We propose a modified and extended method QSR-14 for qualitative spatial representation and reasoning of a dynamic physical 2D world in the gravitation field. We mention the need of a global reference frame and describe QSR-14 in detail for representing and reasoning of physical and chemical changes of a real world using the global reference frame. We believe QSR-14 is appropriate for the qualitative spatial representation and reasoning of a dynamic physical world. The usefulness of QSR-14 is shown with several examples.

• Journal of Korean Library and Information Science Society
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• v.35 no.1
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• pp.303-317
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• 2004

The purpose of this study was to develop a framework for predicting searching performance through an understanding of how cognitive ability relates to searching process and outcome. Specifically, this study examined the relationship between spatial visualization, logical reasoning, integrative reasoning, and information searching process and outcome. Information searching process was assessed by seven search process indicators: (1) search command selection: (2) combination of search commands; (3) application of Boolean logic: (4) application of truncation; (5) use of limit search function; (6) number of search statements; and (7) number of search errors made. Searching outcome was assessed by the number of correct answers to search questions. Subjects first took three standardized cognitive tests that measured cognitive abilities, and performed online catalog searching in response to seven information search questions. The searches were logged using Lotus ScreenCam, and reviewed for the analysis. Factor analysis was used to find underlying structures of the seven search process variables. Multiple regression analysis was applied to examine the predictive power of three cognitive variables on three extracted factors, and search outcome. Results of the data analysis showed that individual differences in logical reasoning could predict information searching process and outcome.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1324-1331
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• 1999

물체의 공간에서의 운동을 다루는 공간 추론의 연구에서 충돌을 이해하는 것은 중요하다. 본 논문에서는 정성적 충돌 이론과 추론 기법을 소개하고자 한다. 충돌 해석에 있어 관련된 물체의 공간적 속성과 운동방향의 상호작용을 분석하여 충돌로 인한 힘과 운동 방향의 전달을 계산하였다. 추론 과정에 있어서는 충돌이 갖는 특성인 불연속적인 변화에 대한 분석과 회전 운동으로 인한 변화의 분석이 소개되었다. 제안된 충돌 이론과 추론 기법은 구현되어 자동차 충돌 사고의 충돌에 적용되어 유효성을 입증할 수 있었다. Abstract Understanding collision is important in spatial reasoning problems that study the motions of objects. This paper introduces qualitative collision theory and reasoning techniques. Force and motion transfers are computed by analyzing the interactions of the spatial properties and motions of the objects. This paper also presents inference techniques for handling discontinuous changes and angular changes by rotation. These theories and inference techniques are implemented and applied to real car-to-car collision accidents. The test results verify the reliabilities of our techniques.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.397-406
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• 2014

In order to answer the questions successfully on behalf of the human in DeepQA environments such as Jeopardy! of the American quiz show, the computer is required to have the capability of fast temporal and spatial reasoning on a large-scale commonsense knowledge base. In this paper, we present a scalable spatial reasoning algorithm for deriving efficiently new directional and topological relations using the MapReduce framework, one of well-known parallel distributed computing environments. The proposed reasoning algorithm assumes as input a large-scale spatial knowledge base including CSD-9 directional relations and RCC-8 topological relations. To infer new directional and topological relations from the given spatial knowledge base, it performs the cross-consistency checks as well as the path-consistency checks on the knowledge base. To maximize the parallelism of reasoning computations according to the principle of the MapReduce framework, we design the algorithm to partition effectively the large knowledge base into smaller ones and distribute them over multiple computing nodes at the map phase. And then, at the reduce phase, the algorithm infers the new knowledge from distributed spatial knowledge bases. Through experiments performed on the sample knowledge base with the MapReduce-based implementation of our algorithm, we proved the high performance of our large-scale spatial reasoner.