• Journal of KIISE:Databases
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• v.34 no.6
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• pp.564-571
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• 2007

One of the methods for integrating XML data in distributed environments is using XML view. User can query toward distributed local XML views by using global XQuery queries in XQuery which is a standard query language for searching XML data. The global XQuery queries naturally contain join operations because of integrating and searching distributed heterogeneous data. Since join operations are generally expensive for processing a query, its processing technique is very important for efficient processing of global XQuery queries. Therefore there are some studies on the efficient processing of join operations and one of these studies is that selects minimum join cost by estimating a join selectivity. In case of SQL, there are already some researches for estimating a join selectivity and join cost of global SQL queries. However we can not apply their methods for estimating the selectivity of join operations in SQL queries into XQuery queries because of the structural difference between relational data and XML data. Therefore this paper proposes a method for estimating a selectivity of join operations in XQuery queries using the information of XML views. Our contribution is three threefold. First, we define the difference point for estimating join selectivity between SQL and XQuery. Second, we estimate join selectivity in XQuery queries by referring XML views. Third, we evaluate our estimating method.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.743-750
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• 2003

In this paper, we propose a query expansion and weighting method for document filtering to increase precision of the result of Web search engines. Query expansion for document filtering uses ConceptNet, encyclopedia and documents of 10% high similarity. Term weighting method is used for calculation of query-documents similarity. In the first step, we expand an initial query into the first expanded query using ConceptNet and encyclopedia. And then we weight the first expanded query and calculate the first expanded query-documents similarity. Next, we create the second expanded query using documents of top 10% high similarity and calculate the second expanded query- documents similarity. We combine two similarities from the first and the second step. And then we re-rank the documents according to the combined similarities and filter off non-relevant documents with the lower similarity than the threshold. Our experiments showed that our document filtering method results in a notable improvement in the retrieval effectiveness when measured using both precision-recall and F-Measure.

• Journal of KIISE
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• v.44 no.7
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• pp.703-709
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• 2017

Outsourcing databases is to offload storage and computationally intensive tasks to the third party server. Therefore, data owners can manage big data, and handle queries from clients, without building a costly infrastructure. However, because of the insecurity of network systems, the third-party server may be untrusted, thus the query results from the server may be tampered with. This problem has motivated significant research efforts on authenticating various queries such as range query, kNN query, function query, etc. Although aggregation queries play a key role in analyzing big data, authenticating aggregation queries has not been extensively studied, and the previous works are not efficient for data with high dimension or a large number of distinct values. In this paper, we propose the AMR-tree that is a data structure, applied to authenticate aggregation queries. We also propose an efficient proof construction method and a verification method with the AMR-tree. Furthermore, we validate the performance of the proposed algorithm by conducting various experiments through changing parameters such as the number of distinct values, the number of records, and the dimension of data.

• Journal of Korea Multimedia Society
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• v.14 no.3
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• pp.339-347
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• 2011

As the volume visualization has been applied to render medical datasets, there has been a requirement to produce high quality images. Even though nice images can be generated by using previous linear filter, high order filter is required for better images. However, it takes much time for high order resampling, so that, overall rendering time is increased. In this paper, we perform high quality volume visualization using the cardinal interpolation. By enabling the empty space leaping which reduces the number of resampling, we achieve the efficient visualization. In detail, we divide the volume data into small blocks and leap empty blocks by referring the upper and lower bound value for each block. We propose a new method to estimate upper and lower bound value of for each block. As the result, we noticeably accelerated high quality volume visualization.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3
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• pp.209-215
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• 2010

Recently portable terminal is received attention by wireless networks and mass capacity ROM. In this result, TTS(Text to Speech) system is inserted to portable terminal. Nevertheless high quality synthesis is difficult in portable terminal, users need high quality synthesis. In this paper, we proposed Distributed TTS (DTTS) that was composed of server and terminal. The DTTS on corpus based speech synthesis can be high quality synthesis. Synthesis system in server that generate optimized speech concatenation information after database search and transmit terminal. Synthesis system in terminal make high quality speech synthesis as low computation using transmitted speech concatenation information from server. The proposed method that can be reducing complexity, smaller power consumption and efficient maintenance.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.1-13
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• 1994

The Temporal Database Management Testbed supports valid and transaction time. In this paper, we discuss the design and implementation of a testbed of a temporal database management system in main memory. The testbed consists of a syntactic analyzer, a semantic analyzer, a code generator, and an interpreter. The syntactic analyzer builds a parse tree from a temporal query. The semantic analyzer then checks it for correctness against the system catalog. The code generator builds an execution tree termed ann update network. We employ an incremental view materialization for the execution tree. After building the execution tree, the interpreter activates each node of the execution tree. Also, the indexing structure and the concurrency control are discussed in the testbed.

• Resources Recycling
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• v.25 no.3
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• pp.82-87
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• 2016

The discharged products of Li-$O_2$ and Na-$O_2$ batteries using ether-based electrolyte as next-generation battery system were analyzed. The morphology of the discharged products showed millet-like shape in the both battery systems by FESEM. However, the discharged product, $Li_2O_2$ showed amorphous-like form in the Li-$O_2$ cell while crystalline $NaO_2$ is formed in the Na-$O_2$ cell when confirmed by X-ray diffraction. In this work, we comprehended a principle operating mechanism of Li-$O_2$ and Na-$O_2$ battery.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.49-51
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• 2005

시간의 흐름에 따라 그 위치가 빈번히 변화하는 이동 객체의 특성으로 인해 실시간으로 증가하는 이동객체의 연산 정보를 효과적으로 관리할 수 있는 효율적인 연산 기법이 요구된다. 따라서 본 논문에서는 이동 객체의 시공간 연산을 위해 기존에 제안되었던 HR 트리의 성능을 개선시킬 수 있는 확장된 HR-트리(Extended HR-Tree : EHR-Tree)를 제안한다. 기존의 HR 트리는 삽입, 삭제, 갱신과 같은 연산이 빈번한 경우에 단말 노드 및 비단말 노드를 새로 생성해야 함으로 인하여 성능이 떨어지고, 공간의 낭비가 있게 된다. 이 문제를 해결하기 위해 본 논문에서 제안하는 EHR-트리는 연산이 발생할 때마다 새로운 HR-트리를 생성하는 것이 아니라 시간 구간을 두어서 새로 발생한 연산이 같은 시간 구간에 있을 경우 그 단위시간에 생성된 HR-트리에 그대로 삽입, 삭제, 갱신과 같은 연산을 수행하게 된다. 따라서 기존 HR-트리에서 단말 노드 및 비단말 노드를 새로 생성해야함으로 발생되던 많은 저장 공간 요구를 감소시킴으로, 즉 전체 연산 크기를 작게 하여 디스크 I/O수를 감소시킴으로써, 시공간 질의의 처리 속도를 향상시켜 효율적인 질의가 가능하도록 한다.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.461-466
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• 2021

본 연구에서는 DECO(Dictionnaire Electronique du COreen) 한국어 전자사전과 LGG(Local-Grammar Graph)에 기반한 반자동 언어데이터 증강(Semi-automatic Symbolic Propagation: SSP) 방식에 입각하여, 핀테크 분야의 CS(Customer Service) 챗봇 NLU(Natural Language Understanding)을 위한 주석 학습 데이터를 효과적으로 생성하고, 이를 기반으로 RASA 오픈 소스에서 제공하는 DIET(Dual Intent and Entity Transformer) 아키텍처를 활용하여 핀테크 CS 챗봇 NLU 시스템을 구현하였다. 실 데이터을 통해 확인된 핀테크 분야의 32가지의 토픽 유형 및 38가지의 핵심 이벤트와 10가지 담화소 구성에 따라, DECO-LGG 데이터 생성 모듈은 질의 및 불만 화행에 대한 양질의 주석 학습 데이터를 효과적으로 생성하며, 이를 의도 분류 및 Slot-filling을 위한 개체명 인식을 종합적으로 처리하는 End to End 방식의 멀티태스크 트랜스포머 모델 DIET로 학습함으로써 DIET-only F1-score 0.931(Intent)/0.865(Slot/Entity), DIET+KoBERT F1-score 0.951(Intent)/0.901(Slot/Entity)의 성능을 확인하였으며, DECO-LGG 기반의 SSP 생성 데이터의 학습 데이터로서의 효과성과 함께 KoBERT에 기반한 DIET 모델 성능의 우수성을 입증하였다.

• Journal of the Korean Society for Library and Information Science
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• v.39 no.4
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• pp.105-118
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• 2005

As the number of users who ask and give answers in the query-answer documents retrieval system is growing exponentially, the query-answer document become a crucial information resource, as a new type of information retrieval service. A query-answer document Consists of three structural parts : a query, explanation on query, and answers Chosen by users who asked the query. To identify the role of each structural part in representing the topics of documents, the three structural parts were clustered automatically and the results of several clustering tests were compared in this study.