• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1075-1076
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• 2012

인터넷의 발달로 데이터가 이질적이고 방대해점에 따라 사용자의 의도와 목적에 맞는 정보를 빠르고 정확하게 찾아내는 것이 어려워지고, 대용량의 데이터를 빠르게 검색 할 수 있는 효율적인 top k 질의 처리가 중요해 지고 있다. top k 질의 처리는 릴레이션에서 가장 높은 (또는 가장 낮은) 값을 가지는 k개의 튜플을 반환하는 방법이며, 그 중 Layer 기반 방법은 객체가 가지는 d개의 속성 값들을 d-차원의 공간상의 점 객체로 랩핑하여, layer들의 list를 생성 한다. 본 논문에서는 Layer 기반 법 중 skyline을 사용하여 layer을 생성하고 인덱스를 구축하는 기존 연구에 대해서 조사한다. 그리고 대표적인 방법인 모든 객체를 순차적으로 비교하는 BNL과 이의 비교 횟수를 감소시킨 SFS, 그리고 R-tree를 사용한 NN과 이의 계산 비용을 감소시킨 BBS에 대해 설명한다.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.1-2
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• 2010

• Journal of the Korea Organic Resources Recycling Association
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• v.2 no.1
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• pp.41-49
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• 1994

This experiment was carried out in order to study the effect of organic fertilizer on the growth of Korean lawngrass (Zoysia matrella L. Merr.) and the change of soil chemical characteristics. The results obtained are summarized as follows : 1. Organic fertilizer was appropriate for base-dressing in the sod establishment of Korean lawngrass(Z. matrella L. Merr.) 2. Chlorophyll contents in mixed application plot of complex fertilizer(21-17-17) and organic fertilizer were more abundant than that in single application plot of organic fertilizer. 3. Application of organic fertilizer promotes the contents of available phosphorous in soil. 4. Dry weights of base-dressing and top-dressing plot had more weight about 40%, 12% than that of control plot, respectively. So base-dressing was more effective than top-dressing in the application of organic fertilizer.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.810-811
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• 2017

신약 개발은 고부가가치를 창출하는 차세대 전략 산업으로 주목받고 있지만, 동물 실험과 임상 시험에 막대한 비용이 필요한 고위험-초고소득(high risk-super high return) 산업이다. 따라서 신약 후보군의 선정이 매우 중요하며 약물 유사도를 랭킹함수를 사용하는 top-k 질의 처리를 통해 후보군을 효과적으로 선정할 수 있다. 본 논문에서는 ChEMBL 데이터베이스[4]에 존재하는 화합물들 중 사용자가 원하는 특성을 갖는 k개의 화합물들을 후보군으로 추천해주는 검색 엔진을 개발하였다.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.538-550
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• 2006

XML Keyword search enables us to get information easily without knowledge of structure of documents and returns specific and useful partial document results instead of whole documents. Element level query processing makes it possible, but computational complexity, as the number of documents grows, increases significantly overhead costs. In this paper, we present document-level ranking scheme over XML documents which predicts results of element-level processing to reduce processing cost. To do this, we propose the notion of 'keyword proximity' - the correlation of keywords in a document that affects the results of element-level query processing using path information of occurrence nodes and their resemblances - for document ranking process. In benefit of document-centric view, it is possible to reduce processing time using ranked document list or filtering of low scored documents. Our experimental evaluation shows that document-level processing technique using ranked document list is effective and improves performance by the early termination for top-k query.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.4
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• pp.412-429
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• 2008

As the amount of electronic documents increases rapidly with the growth of the Internet, a parallel search engine capable of handling a large number of documents are becoming ever important. To implement a parallel search engine, we need to partition the inverted index and search through the partitioned index in parallel. There are two methods of partitioning the inverted index: 1) document-identifier based partitioning and 2) keyword-identifier based partitioning. However, each method alone has the following drawbacks. The former is convenient in inserting documents and has high throughput, but has poor performance for top h query processing. The latter has good performance for top-k query processing, but is inconvenient in inserting documents and has low throughput. In this paper, we propose a hybrid partitioning method to compensate for the drawback of each method. We design and implement a parallel search engine that supports the hybrid partitioning method using the Odysseus DBMS tightly coupled with information retrieval capability. We first introduce the architecture of the parallel search engine-Odysseus/parallel-OOSQL. We then show the effectiveness of the proposed system through systematic experiments. The experimental results show that the query processing time of the document-identifier based partitioning method is approximately inversely proportional to the number of blocks in the partition of the inverted index. The results also show that the keyword-identifier based partitioning method has good performance in top-k query processing. The proposed parallel search engine can be optimized for performance by customizing the methods of partitioning the inverted index according to the application environment. The Odysseus/parallel OOSQL parallel search engine is capable of indexing, storing, and querying 100 million web documents per node or tens of billions of web documents for the entire system.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.702-714
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• 2001

As Internet technology evolves, there is growing need of Internet queries involving multiple information sources. Efficient processing of such queries necessitates the integrated summary data that compactly represents the data distribution of the entire database scattered over many information sources. This paper presents an efficient method of managing the integrated summary data based on the wavelet transform and addresses Internet query processing using the integrated summary data. The simplest method for creating the integrated summary data would be to summarize the integrated data sidtribution obtained by merging the data distributions in multiple information sources. However, this method suffers from the high cost of transmitting storing and merging a large amount of data distribution. To overcome the drawbacks, we propose a new wavelet transform based method that creates the integrated summary data by merging multiple summary data and effective method for optimizing Internet queries using it A wavelet transformed summary data is converted to satisfy conditions for merging. Moreover i the merging process is very simpe owing to the properties of the wavelet transform. we formally derive the upper bound of the error of the wavelet transformed intergrated summary data. Compared with the histogram-based integrated summary data the wavelet transformedintegrated summary data provesto be 1.6~5.5 time more accurate when used for selectivity estimation in experiments. In processing Internet top-N queries involving 56 information sources using the integrated summary data reduces the processing cost to 1/44 of the cost of not using it.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.33-43
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• 2022

Labeled graphs are used to represent entities, their relationships, and their structures in real data such as knowledge graphs and protein interactions. With the rapid development of IT and the explosive increase in data, there has been a need for a subgraph matching technology to provide information that the user is interested in. In this paper, we propose an approximate Top-k labeled subgraph matching scheme that considers the semantic similarity of labels and the difference in graph structure. The proposed scheme utilizes a learning model using FastText in order to consider the semantic similarity of a label. In addition, the label similarity graph(LSG) is used for approximate subgraph matching by calculating similarity values between labels in advance. Through the LSG, we can resolve the limitations of the existing schemes that subgraph expansion is possible only if the labels match exactly. It supports structural similarity for a query graph by performing searches up to 2-hop. Based on the similarity value, we provide k subgraph matching results. We conduct various performance evaluations in order to show the superiority of the proposed scheme.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.99-115
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• 2003

In this paper, we present a knowledge-based object-oriented spatial database system called KOBOS. A knowledge-based approach is introduced to the object-oriented spatial database system for data modeling and approximate query answering. For handling the structure of spatial objects and the approximate spatial operators, we propose three levels of object-oriented data model: (1) a spatial shape model; (2) a spatial object model; (3) an internal description model. We use spatial type abstraction hierarchies(STAHs) to provide the range of the approximate spatial operators. We then propose SOQL, a spatial object-oriented query language. SOQL provides an integrated mechanism for the graphical display of spatial objects and the retrieval of spatial and aspatial objects. To support an efficient hybrid query evaluation, we use the top-down spatial query processing method.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.371-374
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• 2019

PDA나 휴대폰 단말로 여러 속성의 데이터를 이용하여 사용자에게 필요한 정보를 제공하는 위치기반 서비스는 물류/운송 정보 서비스, 버스/지하철 노선 안내 서비스 등에 사용된다. 여기에서 제공하는 데이터들을 최적 경로를 구하는 외판원 순회문제 (Traveling Salesman Problem)에 사용한다면 더 정확한 경로 서비스 제공이 가능하다. 하지만 데이터의 수가 많아질수록 비교 횟수가 기하급수적으로 늘어나는 외판원 순회 알고리즘의 특성상 일반 단말기에서 활용하기에는 배터리의 제약이 따른다. 본 논문에서는 이와 같은 단점을 해결하기 위해서 최적 경로의 후보군을 줄일 수 있는 스카이라인 질의를 이용하여 n차원 속성에 대한 최적 경로 알고리즘을 제안한다. 실험에서 정확도와 오차율을 통해 제안한 방식의 유용성을 보였으며 기존방식과 연산시간 차이를 비교하여 다차원방식의 효율성을 나타내었다.