• Journal of Computing Science and Engineering
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• 제1권2호
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• 국제초고층학회논문집
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• 제6권2호
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• pp.165-175
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• 2017

The importance of designing urban building complexes so that they obtain 'urban' power, rather than become isolated from the surrounding urban context, has been well recognized by both researchers and practitioners. Nevertheless, most current discussions are made from architects' personal experiences and intuition, and lack a quantitative understanding, to which obstacles include an in-depth exploration of the 'urban' power between building complexes and the urban environment. This paper attempts to measure this feature of 'urban', i.e., 'urbanity,' through a new analytical approach derived from the opendata environment. Three measurements that can be easily collected though the Google Maps API and Open Street Map are applied herein to evaluate high or low values of urbanity. Specifically, these are 'metric depth', i.e., the scale of extended public space, 'development density', i.e., density and distribution of point of interests (POIs), and 'type diversity', i.e., diversity of different commercial types. Six cases located in Japan, China and Hong Kong respectively are ranked based on this analytical approach and compared with each other. It shows that Japanese cases, i.e., Osaka Station City and Namba Parks, Osaka, obtained clearly higher values than cases in Shanghai and Hong Kong. On one hand, the insight generated from measuring and explaining 'urban' power would help to assist better implementation of this feature in the design of urban building complexes. On the other hand, this analytical approach can be easily extended to achieve a large-scale measurement and comparison among different urban building complexes, which is also helpful for design practitioners.

• 정보처리학회논문지A
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• 제12A권5호
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• pp.451-460
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• 2005

본 논문은 ffGA상에서 에너지 효율이 높은 데이터 경로 설계 방법론을 제안한다. 에너지, 처리시간, 그리고 면적간의 트레이드오프를 이해하기 위하여, 도메인 특성 모델링, coarse-grained 성능평가, 설계공간 조사, 그리고 로우-레벨 시뮬레이션 과정들을 통합한다. 도메인 특성 모델링 기술은 도메인의 특성에 따른 시스템 전체의 에너지 모에 영향을 미치는 여러 가지 구성요소와 파라미터들을 식별함으로써 하이-레벨 모델을 명시한다. 도메인이란 주어진 어플리케이션 커널의 알고리즘에 대응하는 아키텍쳐 패밀리이다. 하이-레벨 모델 또한 에너지, 처리시간 그리고 면적을 예측하는 함수들로 구성되어 트레이드오프 분석을 용이하게 한다. 설계 공간 조사(DSE)는 도메인에 명시된 설계 공간을 분석하여 설계 셋을 선택하도록 한다. 로우-레벨 시뮬레이션은 설계 공간 조사(DSE)에 의해 선택된 설계와 최종 선택된 설계의 정확한 성능평가를 위하여 사용된다. 본 논문에서 제안한 설계 방법은 매트릭스 곱셈에 대응하는 알고리즘과 아키텍쳐 패밀리를 사용한다. 제안된 방법에 의해 검증된 설계는 에너지, 처리시간과 면적간의 트레이드오프를 보인다. 제안된 설계 방법의 효율성을 보이기 위하여 Xilinx에서 제공되는 매트릭스 곱셈 커널과 비교하였다. 성능 비교 메트릭으로 평균 전력 밀도(E/AT)와 에너지 대 (면적 x 처리시간)비를 사용하였다. 다양한 문제의 크기에 대하여 Xilinx설계들과 비교하였을 때 제안한 설계 방법이 전력밀도(E/AT)에서 평균 $25\%$우수하였다. 또한 본 논문에 제안한 설계의 방법을 MILAN 프레임워크를 이용하여 구현하였다.

• 지능정보연구
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• 제14권1호
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• pp.101-115
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• 2008

일반적으로 혼합 추천 시스템(hybrid recommender system)이란 협업적 여과 방법(collaborative filtering)을 다른 기술들과 결합하여 사용하여 사용자가 원하는 정보를 손쉽게 찾을 수 있도록 도와주는 시스템이다. 협업적 여과 방법과 결합된 혼합 시스템은 대체로 내용이 유사한 아이템들이 추천 되어 전반적인 아이템 추천 성능 및 새로이 추가된 아이템에 대한 추천의 질이 떨어지는 문제가 있다. 이러한 문제를 해결하기 위해, 본 논문에서는 다양성(diversity)을 고려한 새로운 혼합 추천 시스템을 제안한다. 제안된 시스템에서는 첫 번째 단계로 협업적 여과 방법으로부터 추천된 아이템들 간의 비유사도를 측정한다. 두 번째 단계로는 첫 번째 단계에선 추천된 비유사도가 높은 아이템들을 내용 기반의 여과 방법(content-based filtering)에 적용하여 새로운 아이템에 대한 추천 성능을 향상 시킨다. 제안된 방법의 성능 평가를 위해 movielens 데이터를 이용하여 기존의 내용기반 추천 시스템 및 단순 혼합 시스템과 비교 평가하였다. 실험 결과 제안된 방법이 내용기반 추천 시스템 및 단순 혼합시스템보다 높은 추천 성능을 보였다.

• 터널과지하공간
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• 제33권6호
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• pp.508-518
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• 2023

이 논문은 LiDAR 스캔 또는 사진측량 기술에 의해 재구성된 3D 디지털 모델을 기반으로 터널 벽면의 불연속면을 자동으로 매핑하는 새로운 접근 방식을 제안한다. 본 제안에서는 U-Net이라 불리는 딥러닝 시맨틱 영역분할 모델을 사용하며, 터널 막장면의 3D 지형 모델에서 불연속면 영역을 식별해 낸다. 제안된 딥러닝 모델은 투영된 RGB 이미지, 면의 깊이 이미지 및 국부적인 면의 표면 속성 이미지(즉, 법선 벡터 및 곡률 이미지)를 포함한 다양한 정보를 종합 학습하여 기본 3차원 이미지에서 불연속면 영역을 효과적으로 분할한다. 이후 영역분할 결과는 면의 깊이 맵과 투영 행렬을 사용하여 3D 모델로 다시 투영시키고, 3D 공간 내에서 불연속면의 위치 및 범위를 정확하게 표현한다. 영역분할 모델의 성능은 영역 분할된 결과를 해당 지면 실측 값과 비교함으로써 평가하였으며, IoU(intersection-over-union) 값이 약 0.8 정도로 나타나 영역분할 결과의 높은 정확성을 확인하였다. 여전히 학습데이터가 제한적 이었음에도 불구하고, 제안 기법은 3D 모델의 점군 데이터를 불연속면의 유사군으로 그룹화하기 위해 전 막장면의 법선 벡터와 클러스터링과 같은 비지도 학습기반 알고리즘에만 의존하던 기존 접근 방식의 한계의 극복 가능성을 보여주었다.

• 대한토목학회논문집
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• 제31권6B호
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• pp.565-575
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• 2011

본 연구는 다변수 접근방식인 서식지평가 정성지수(Qualitative Habitat Evaluation Index, QHEI)와 생물보전지수(Index of Biological Integrity, IBI)의 적용방법 도입과 어류 서식 및 피신처 확보용 인공구조물인 방틀둠벙의 정량적인 평가를 목적으로 하였다. 또한, 안동시 남후면 하아리에 소재한 한국건설기술연구원 수자원 환경실험센터의 실외 실험장을 활용하여 실규모로 생물 평가와 검증을 실시하였다. 본 평가기법의 적용을 위해 구성한 실험수로 및 방틀둠벙 시설은 국내 하천의 조건을 최대한 반영하여 조성하였으며, 흐름상태와 정체상태로 구분하여 유속 $0.0{\sim}1.5m\;s^{-1}$, 폭 1.0~3.0 m, 수심 0.05~0.70 m, 다양한 하상재료를 조성하여 2010년 5월~12월의 8개월간 모니터링 하였다. QHEI는 실험수로의 E~F지점이 평균 83.1로 가장 높게 나타났고, B~C지점에서 78.1로 가장 낮게 나타났다. 하지만 방틀둠벙은 시 공간적 구분 없이 98.9 이상의 값을 나타내어 전체 지점 중 가장 높은 양호상태(Good)의 서식처 조건을 유지하였다. IBI는 전반적으로 QHEI와 비슷한 패턴을 나타내었으나 겨울 갈수기에 양호상태(Good)인 44.2를 보여 32.8을 나타낸 실험수로와 비교되었다. 또한, 흐름과 정체상태 조건에서 방틀둠벙의 IBI 값은 실험수로보다 각각 5.7~11.4%, 18.7~34.8% 높은 평가 값을 나타내어, 유량이 적거나 흐름이 없는 갈수기에 어류 서식처 확보 가능성을 정량적으로 시사하였다. 종합적인 수생태계 건강성 평가에서 실험하천은 보통과 양호상태(Fair~Good) 범위를 나타낸 반면, 방틀둠벙은 대부분 양호상태(Good)를 보여 수생태계 건강성 확보에 적용 가능함을 도출하였다.

• Asia pacific journal of information systems
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• 제20권1호
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• pp.81-99
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• 2010

Lately, in consumers' markets the number of new items is rapidly increasing at an overwhelming rate while consumers have limited access to information about those new products in making a sensible, well-informed purchase. Therefore, item providers and customers need a system which recommends right items to right customers. Also, whenever new items are released, for instance, the recommender system specializing in new items can help item providers locate and identify potential customers. Currently, new items are being added to an existing system without being specially noted to consumers, making it difficult for consumers to identify and evaluate new products introduced in the markets. Most of previous approaches for recommender systems have to rely on the usage history of customers. For new items, this content-based (CB) approach is simply not available for the system to recommend those new items to potential consumers. Although collaborative filtering (CF) approach is not directly applicable to solve the new item problem, it would be a good idea to use the basic principle of CF which identifies similar customers, i,e. neighbors, and recommend items to those customers who have liked the similar items in the past. This research aims to suggest a hybrid recommendation procedure based on the preference boundary of target customer. We suggest the hybrid recommendation procedure using the preference boundary in the feature space for recommending new items only. The basic principle is that if a new item belongs within the preference boundary of a target customer, then it is evaluated to be preferred by the customer. Customers' preferences and characteristics of items including new items are represented in a feature space, and the scope or boundary of the target customer's preference is extended to those of neighbors'. The new item recommendation procedure consists of three steps. The first step is analyzing the profile of items, which are represented as k-dimensional feature values. The second step is to determine the representative point of the target customer's preference boundary, the centroid, based on a personal information set. To determine the centroid of preference boundary of a target customer, three algorithms are developed in this research: one is using the centroid of a target customer only (TC), the other is using centroid of a (dummy) big target customer that is composed of a target customer and his/her neighbors (BC), and another is using centroids of a target customer and his/her neighbors (NC). The third step is to determine the range of the preference boundary, the radius. The suggested algorithm Is using the average distance (AD) between the centroid and all purchased items. We test whether the CF-based approach to determine the centroid of the preference boundary improves the recommendation quality or not. For this purpose, we develop two hybrid algorithms, BC and NC, which use neighbors when deciding centroid of the preference boundary. To test the validity of hybrid algorithms, BC and NC, we developed CB-algorithm, TC, which uses target customers only. We measured effectiveness scores of suggested algorithms and compared them through a series of experiments with a set of real mobile image transaction data. We spilt the period between 1st June 2004 and 31st July and the period between 1st August and 31st August 2004 as a training set and a test set, respectively. The training set Is used to make the preference boundary, and the test set is used to evaluate the performance of the suggested hybrid recommendation procedure. The main aim of this research Is to compare the hybrid recommendation algorithm with the CB algorithm. To evaluate the performance of each algorithm, we compare the purchased new item list in test period with the recommended item list which is recommended by suggested algorithms. So we employ the evaluation metric to hit the ratio for evaluating our algorithms. The hit ratio is defined as the ratio of the hit set size to the recommended set size. The hit set size means the number of success of recommendations in our experiment, and the test set size means the number of purchased items during the test period. Experimental test result shows the hit ratio of BC and NC is bigger than that of TC. This means using neighbors Is more effective to recommend new items. That is hybrid algorithm using CF is more effective when recommending to consumers new items than the algorithm using only CB. The reason of the smaller hit ratio of BC than that of NC is that BC is defined as a dummy or virtual customer who purchased all items of target customers' and neighbors'. That is centroid of BC often shifts from that of TC, so it tends to reflect skewed characters of target customer. So the recommendation algorithm using NC shows the best hit ratio, because NC has sufficient information about target customers and their neighbors without damaging the information about the target customers.

• 마케팅과학연구
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• 제19권1호
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• pp.23-35
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• 2009

A hypermarket as the one of the most globally standardized retailing format is also the type of store among various types of stores that the most active in expanding into other foreign markets. Recently, as several Korean retailing companies start to penetrate into Chinese market they differentiate themselves with modern facilities and customers service oriented high-end concept. China and Korea as Far East Asian countries share many common values, however precise and careful analysis should be carried out since there may also be critical differences in socio-economic aspects as well as in consumption patterns due to the level of development stages of retail industry among two countries. Even though precise and careful study is crucial on Chinese retailing market and consumers, none of researches and studies on 'how the quality of service dimensional structure is different between Korea and China', and 'what will be the most important and influential service dimensional factors for Chinese consuers compared to the hypermarkets customers in Korea' in order to improve the level of Chinese consumers satisfaction' have been fulfilled At this point of view, this study uses KD-SQS (Rho Eun Jung & Sir Yong Gu, 2008) which is a measure of Korean hypermarkets service quality to set up a hypothesis on Korean and Chinese consumers, and an empirical analysis is conducted. We try to get the answers about how the comparative importance of Service quality dimensions which decides the level of customer satisfaction is different depending on the cultural dimensions and socio-economic factors among two countries, Korea and China. Based upon the results, we try to give a valuable suggestion of what service dimensional factors should be reinforced to improve the level of CS in Chinese retailing market. Hypotheses for this study are as follows : H1. Each dimension of Service Quality significantly affects the level of CS H2. The effect of 'Basic Benefit' in service quality dimensions on the level of CS is greater in China than in Korea H3. The effect of 'Promotion' in service quality dimensions on the level of CS is greater in China than in Korea H4. The effect of 'Physical Aspects'in service quality dimensions on the level of CS is greater in Korea than in China. H5. The effect of 'Personal Interaction' in service quality dimensions on the level of CS is greater in China than in Korea H6. The effect of 'Policy' in service quality dimensions on the level of CS will be greater in Korean than in China H7. The effect of additional convenience in service quality dimensions on the level of CS will be greater in Korean than in China. More than 1,100 data were collected directly from the surveys of Chinese and Korean consumers in order to verify the hypotheses above. In Korea, stores which have floor space of over $9,000m^2$and opened later than year 2000 were selected for the samples, and thus Gayang, Wolgye, Sangbong, Eunpyeong, Suh-Suwon, Gojan stores and their customers were surveyed. In China, notable differences in the income levels and consumer behaviors between cities and regions were considered, and thus the research area was limited to the stores only in Shanghai. 6 stores which have the size of over $6,000m^2$ and opened later than 2000, such as Ruihong, Intu, Mudanjang, Sanrin, Raosimon, and Ranchao stores were selected for the survey. SPSS 12.0 and AMOS 7.0 were used as statistical tools, and exploratory factor analysis, confirmatory factor analysis, and multi-group analysis were conducted. In order to carry out a multi group analysis that decides whether the structure variables which shows the different effects of 6 service dimensions in Korean and Chinese groups is statistically valid, configural invariance, metric invariance, and structural invariance are tested in order. At the results of the tests, 3 out of 7 hypotheses were supported and other 4 hypotheses were denied. According to the study, 4 dimensions (Basic Benefit, Physical Environment, Policy, and additional convenience) were positively correlated with CS in Korea, and 3 dimensions (i.e. basic benefit, policy, additional convenience) were significant in China. However, the significance of the service-dimensions was turned out to be partially different in Korea and China. The Basic Benefit is more influential in deciding the level of CS in china than Korea, however Physical Aspect is more important factor in Korea. 'Policy dimension' did not make significant difference between two countries. In the 'additional convenience dimension', the differences in 'socio-economic factors' than in'cultural background' were considered as more important in Chinese consumers than Korean. Overall, the improvement of Service quality will be crucial factors to increase the level of CS in Chinese market same as Korean market. In addition, more emphases need to be placed on the service qualities of 'Basic Benefit' and 'additional convenience' dimensions in China. In particular, 'low price' and 'product diversity' that constitute 'Basic Benefit' are proved to be comparatively disadvantageous and weak points of Korean companies compared to global players, and thus the prompt strengthening those dimensions would be urgent for Korean retailers. Moreover, additional conveniences such as various tenants and complex service and entertaining area will be more important in China than in Korea. Besides, Applying advanced Korean Hypermaret`s customer policy to Chinese consumers will help to get higher reliability and to differentiate themselves to other competitors. However, as personal interaction, physical aspect, promotions were proved as not significant for the level of CS in China, Korean companies need to reconsider the priority order of resource allocations when they tap into Chinese market.