• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.345-348
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• 2006

경로 질의는 XML에서 가장 일반적으로 사용되는 질의이며, 기존에 경로 질의를 효율적으로 처리하기 위한 다양한 색인 기법들이 연구되었다. 최근에는 suffix 트리를 사용하는 구조 조인 기법들이 제안되어 경로 질의 성능을 향상시키고 있다. ViST는 가장 대표적인 구조 조인 기법으로 XML 문서에 대한 검색 시간을 줄이기 위해 suffix 트리와 $B^{+)$ 트리를 사용한다. 하지만, ViST는 suffix 트리에 최적화 되지 못한 번호 부여 기법을 사용함으로써, 질의 처리 시 불필요한 조인과 디스크 접근이 야기된다. 이와 같은 문제들은 경로 질의 성능을 급격히 감소시킨다. 따라서 본 논문에서는 ViST의 문제들을 해결하기 위한 새로운 색인 구조를 제안하다. 제안하는 색인 구조는 suffix 트리를 사용하는 구조 조인 기법의 성능을 향상시키기 위해서 최적화된 번호 부여 기법과 상향식 질외 처리 기법을 사용한다. 본 논문의 성능 평가에서는 제안하는 색인 구조를 ViST와 비교하여, 제안하는 색인 구조가 와일드-카드('$^{\ast}$와 '//')를 포함하는 다양한 단일 경로 질의와 분기 질의에 대해 향상된 성능을 나타냄을 보인다.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.328-342
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• 2007

We propose a new cache conscious indexing structure for processing frequently updated data efficiently. Our proposed index structure is based on a KDB-Tree, one of the representative index structures based on space partitioning techniques. In this paper, we propose a data compression technique and a pointer elimination technique to increase the utilization of a cache line. To show our proposed index structure's superiority, we compare our index structure with variants of the CR-tree(e.g. the FF CR-tree and the SE CR-tree) in a variety of environments. As a result, our experimental results show that the proposed index structure achieves about 85%, 97%, and 86% performance improvements over the existing index structures in terms of insertion, update and cache-utilization, respectively.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.320-330
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• 2004

Query languages for XML use paths in a data graph to represent queries. Actually, paths in a data graph are used as a basic constructor of an XML query. User can write more expressive Queries by using Patterns (e.g. regular expressions) for paths. There are many identical paths in a data graph because of the feature of semi-structured data. Current researches for indexing XML utilize identical paths in a data graph, but such an index can grow larger than source data graph and cannot guarantee efficient access path. In this paper we propose a partitioning technique that can partition all the paths in a data graph. We develop an index graph that can find appropriate partitions for a path query efficiently. The size of our index graph can be adjusted regardless of the source data. So, we can significantly improve the cost for index graph traversals. In the performance study, we show our index much faster than other graph based indexes.

• Journal of Korea Multimedia Society
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• v.12 no.1
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• pp.1-15
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• 2009

This paper presents an assignment method of the multidimensional type inheritance indexes (MD-TIXs) to support the processing of XML queries in XML databases. MD-TIX uses a multidimensional index structure for efficiently supporting nested predicates that involve both nested element and type inheritance hierarchies. In this paper, we have analyzed the strategy of the query processing by using the MD-TIXs, and presented an assignment method of the MD-TIXs in the framework of complex queries, containing conjunctions of nested predicates, each one involving an Xpath having target types or domain types substitution. We first consider MD-TIX operations caused by updating of XML data-bases, and the use of the MD-TIXs in the case of a query containing a single nested predicate. And then, we consider the assignments of the MD-TIXs in the framework of more general queries containing nested predicates over overlapping paths that have common subpaths.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1294-1297
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• 2013

본 논문은 기계학습을 통하여 특허문서를 국제 특허 분류(IPC) 기준에 따라 자동으로 분류하는 시스템에 관한 연구로 잠재 의미 색인 기법을 이용하여 분류의 성능을 높일 수 있는 방법을 제안하기 위한 연구이다. 종래 특허문서에 관한 IPC 자동 분류에 관한 연구가 단어 매칭 방식의 색인 기법에 의존해서 이루어진바가 있으나, 현대 기술용어의 발생 속도와 다양성 등을 고려할 때 특허문서들 간의 관련성을 분석하는데 있어서는 단어 자체의 빈도 보다는 용어의 개념에 의한 접근이 보다 효과적일 것이라 판단하여 잠재 의미 색인(LSI) 기법에 의한 분류에 관한 연구를 하게 된 것이다. 실험은 단어 매칭 방식의 색인 기법의 대표적인 자질선택 방법인 정보획득량(IG)과 카이제곱 통계량(CHI)을 이용했을 때의 성능과 잠재 의미 색인 방법을 이용했을 때의 성능을 SVM, kNN 및 Naive Bayes 분류기를 사용하여 분석하고, 그중 가장 성능이 우수하게 나오는 SVM을 사용하여 잠재 의미 색인에서 명사가 해당 용어의 개념적 의미 구조를 구축하는데 기여하는 정도가 어느 정도인지 평가함과 아울러, LSI 기법 이용시 최적의 성능을 나타내는 특이값의 범위를 실험을 통해 비교 분석 하였다. 분석결과 LSI 기법이 단어 매칭 기법(IG, CHI)에 비해 우수한 성능을 보였으며, SVM, Naive Bayes 분류기는 단어 매칭 기법에서는 비슷한 수준을 보였으나, LSI 기법에서는 SVM의 성능이 월등이 우수한 것으로 나왔다. 또한, SVM은 LSI 기법에서 약 3%의 성능 향상을 보였지만 Naive Bayes는 오히려 20%의 성능 저하를 보였다. LSI 기법에서 명사가 잠재적 의미 구조에 미치는 영향은 모든 단어들을 내용어로 한 경우 보다 약 10% 더 향상된 결과를 보여주었고, 특이값의 범위에 따른 성능 분석에 있어서는 30% 수준에 Rank 되는 범위에서 가장 높은 성능의 결과가 나왔다.

• Journal of the Korean Society for information Management
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• v.26 no.3
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• pp.451-462
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• 2009

The aim of this study is to apply latent semantic indexing(LSI) techniques for efficient automatic classification of opinionated documents. For the experiments, we collected 1,000 opinionated documents such as reviews and news, with 500 among them labelled as positive documents and the remaining 500 as negative. In this study, sets of content words and sentiment words were extracted using a POS tagger in order to identify the optimal feature set in opinion classification. Findings addressed that it was more effective to employ LSI techniques than using a term indexing method in sentiment classification. The best performance was achieved by a supervised LSI technique.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.213-242
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• 1993

During the past decades,several automatic indexing systems have been developed such as single term indexing.phrase indexing and thesaurus basedidndexing systems.Among these systems,single term indexing has been known as superior to others despte its simpicity of extracting meaningful terms.On the other hand,thesaurus based one has been conceived as producing low retrival rate ,mainly because thesauri do not usually have enough index terms.so that much of text data fail to be indexed if they do not match with any of index terms in thesauri.This paper develops a thesaurus based indexing system THINS that yields higher retrieval rate than other systems.by doing syntactic analysis of text data and matching them with index terms in thesauri partially.First,the system analyzes the input text syntactically by using the machine translation suystem MATES/EK and extracts noun phrases.After deleting stop words from noun phrases and stemming the remaining ones.it tries to index these with similar index terms in the thesaurus as much as possible. We conduct an experiment with CACM data set that measures the retrieval effectiveness with CACM data set that measures the retrieval effectuvenss of THINS with single term based one under HYKIS-a thesaurus based information retrieval system.It turns out that THINS yields about 10 percent higher precision than single term based one.while shows 8to9 percent lower recall.This retrieval rate shows that THINS improves much better than privious ones that only yields 25 or 30 percent lower precision than single term based one.We also argue that the relatively lower recall is cause by that CRCS-the thesaurus included in CACM datea set is very incomplete one,having only more than one thousand terms,thus THINS is expected to produce much higher rate if it is associated with currently available large thesaurus.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1025-1042
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• 2002

The goal of this study is to generate a structured document which improves the performance of an information retrieval system by using thesaurus, information on relations between words (terms), and to study on the technique for searching this structured document. In order to accomplish this goal, we propose a DODI (Depth -Oriented Decomposition Index) technique for the structured document and an algorithm to search for related information efficient]y through this index technique that uses a thesaurus. We establish a storage system by which the structured document generated by this index technique is saved in a database through OpenXML and XML documents are generated through ForXML methods.

• The KIPS Transactions:PartD
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• v.12D no.4 s.100
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• pp.543-552
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• 2005

As random access memory chip gets cheaper, it becomes affordable to realize main memory-based database systems. The disk-based spatial indexing techniques, however, cannot direct apply to main memory databases, because the main purpose of disk-based techniques is to reduce the number of disk accesses. In main memory-based indexing techniques, the node access time is much faster than that in disk-based indexing techniques, because all index nodes reside in a main memory. Unlike disk-based index techniques, main memory-based spatial indexing techniques must reduce key comparing time as well as node access time. In this paper, we propose an efficient spatial index structure for main memory-based databases, called Tmr-tree. Tmr-tree integrates the characteristics of R-tree and T-tree. Therefore, Nodes of Tmr-tree consist of several entries for data objects, main memory pointers to left and right child, and three additional fields. First is a MBR of a self node, which tightly encloses all data MBRs (Minimum Bounding Rectangles) in a current node, and second and third are MBRs of left and right sub-tree, respectively. Because Tmr-tree needs not to visit all leaf nodes, in terms of search time, proposed Tmr-tree outperforms R-tree in our experiments. As node size is increased, search time is drastically decreased followed by a gradual increase. However, in terms of insertion time, the performance of Tmr-tree was slightly lower than R-tree.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.416-428
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• 2005

In the paper we propose an index method for XML streaming services, where a server broadcasts XML data to a lot of clients in wireless information systems. In order to control the access and tuning time of mobile clients, the proposed method constructs the XML stream through replicating partial index intermixed with parts of data. For this purpose, we propose a two-level tree structure for separating index and XML data into two parts: replicated vs. non-replicated. We analyze the performance of the proposed method with respect to access time and tuning time. With the analysis result, we derive the optimal level value.