• The KIPS Transactions:PartA
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• v.15A no.1
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• pp.1-8
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• 2008

An occlusion culling method, one of visibility culling methods, excludes invisible objects or triangles which are covered by other objects. As it reduces computation quantity, occlusion culling is an effective method to handle complex scenes in real-time. But an existing common occlusion culling method, such as hardware occlusion query method, sends objects' data twice to GPU and this causes processing overheads once for occlusion culling test and the other is for rendering. And another existing hardware occlusion culling method, VCBP, can test objects' visibility quickly, but it neither test bounding volume nor return test result to application stage. In this paper, we propose a single pass occlusion culling method which uses temporal and spatial coherency, with effective occlusion culling hardware architecture. In our approach, the hardware performs occlusion culling test rapidly with cache on the rasterization stage where triangles are transformed into fragments. At the same time, hardware sends each primitive's visibility information to application stage. As a result, the application stage reduces data transmission quantity by excluding covered objects using the visibility information on previous frame and hierarchical spatial tree. Our proposed method improved maximum 44%, minimum 14% compared with S&W method based on hardware occlusion query. And the performance is increased 25% and 17% respectively, compared to maximum and minimum performance of CHC method which is based on occlusion culling method.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.201-213
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• 2006

A time-series database is a set of time-series data sequences, each of which is a list of changing values of the object in a given period of time. Subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time-series database. This paper addresses a performance issue of time-series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We argue that index interpolation is fairly useful to resolve this problem. The index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their inherent sizes. For index interpolation, we first decide the sites of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes in the perspective of physical database design. For this, given a set of query sequences to be peformed in a target time-series database and a set of window sizes for building multiple indexes, we devise a formula that estimates the cost of all the subsequence matchings. Based on this formula, we propose an algorithm that determines the optimal window sizes for maximizing the performance of entire subsequence matchings. We formally Prove the optimality as well as the effectiveness of the algorithm. Finally, we perform a series of extensive experiments with a real-life stock data set and a large volume of a synthetic data set. The results reveal that the proposed approach improves the previous one by 1.5 to 7.8 times.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.30-38
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• 2004

In many scientific and commercial applications such as Earth Observation System (EOSDIS) and mobile Phone services tracking a large number of clients, it is a daunting task to archive and index ever increasing volume of complex data that are continuously added to databases. To efficiently manage multidimensional data in scientific and data warehousing environments, R-tree based index structures have been widely used. In this paper, we propose a scalable technique called seeded clustering that allows us to maintain R-tree indexes by bulk insertion while keeping pace with high data arrival rates. Our approach uses a seed tree, which is copied from the top k levels of a target R-tree, to classify input data objects into clusters. We then build an R-tree for each of the clusters and insert the input R-trees into the target R-tree in bulk one at a time. We present detailed algorithms for the seeded clustering and bulk insertion as well as the results from our extensive experimental study. The experimental results show that the bulk insertion by seeded clustering outperforms the previously known methods in terms of insertion cost and the quality of target R-trees measured by their query performance.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.2
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• pp.222-226
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• 2010

With the continuously increasing volume of e-commerce transactions, it is now popular to buy some products and to evaluate them on the World Wide Web. The product reviews are very useful to customers because they can make better decisions based on the indirect experiences obtainable through these reviews. However, since online shopping malls do not provide ranking results, it is not easy for users to read all the relevant review documents effectively. Product reviews include subjective and emotional opinions. Thus, the review search is different from the general web search in terms of ranking strategy. In this paper, we propose an effective method of ranking the reviews that can reflect user's intention by using opinion mining techniques. The proposed method analyzes product reviews with query words, and sentimental polarity of subjective opinions. Through diverse experiments, we show that our proposed method outperforms conventional ones.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.279-290
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• 2011

As the volume of internet and web contents have shown an explosive growth in recent years, lately contents recommendation system (CRS) has emerged as an important issue. Consequently, researches on contents recommendation method (CRM) for CRS have been conducted consistently. However, traditional CRMs have the limitations in that they are incapable of utilizing in web 2.0 environments where positions of content creators are important. In this paper, we suggest a novel way to recommend web contents of high quality using both degree of centrality and TF-IDF. For this purpose, we analyze TF-IDF and degree of centrality after collecting RSS and FOAF. Then we recommend contents using these two analyzed values. For the verification of the suggested method, we have developed the CRS and showed the results of contents recommendation. With the suggested idea we can analyze relations between users and contents on the entered query, and can consequently provide the appropriate contents to the user. Moreover, the implemented system we suggested in this paper can provide more reliable contents than traditional CRS because the importance of the role of content creators is reflected in the new system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.129-132
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• 2010

Users can obtain useful information from large of data because of development of internet. Since these data rise continuously like stream data, it is required to extract the appropriate information efficiently for the user's needs. In the traditional scheme, they are acquired by processing the user queries after the occurred data are stored at a database. However, it is inefficient to process the user queries over the large volume of continuous data by using the traditional scheme. In this paper, we propose the Event-Profile Model to define the data occurrence on the internet as the events and the user's requirements as the profiles. We also propose and implement the filtering scheme to process the events and the profiles efficiently. We evaluate the performance of the proposed scheme and our experiments show that the new scheme outperforms the other on various dataset.

• Journal of Korea Spatial Information System Society
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• v.4 no.1 s.7
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• pp.39-46
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• 2002

In distributed spatial database systems, users nay issue a query that joins two relations stored at different sites. The sheer volume and complexity of spatial data bring out expensive CPU and I/O costs during the spatial join processing. This paper shows a new spatial join method which joins two spatial relation in a parallel way. Firstly, the initial join operation is divided into two distinct ones by partitioning one of two participating relations based on the region. This two join operations are assigned to each sites and executed simultaneously. Finally, each intermediate result sets from the two join operations are merged to an ultimate result set. This method reduces the number of spatial objects participating in the spatial operations. It also reduces the scope and the number of scanning spatial indices. And it does not materialize the temporary results by implementing the join algebra operators using the iterator. The performance test shows that this join method can lead to efficient use in terms of buffer and disk by narrowing down the joining region and decreasing the number of spatial objects.

• Interdisciplinary Bio Central
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• v.3 no.2
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• pp.7.1-7.6
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• 2011

Background: Published manuscripts are the main source of biological knowledge. Since the manual examination is almost impossible due to the huge volume of literature data (approximately 19 million abstracts in PubMed), intelligent text mining systems are of great utility for knowledge discovery. However, most of current text mining tools have limited applicability because of i) providing abstract-based search rather than sentence-based search, ii) improper use or lack of ontology terms, iii) the design to be used for specific subjects, or iv) slow response time that hampers web services and real time applications. Results: We introduce an advanced text mining system called PubMine that supports intelligent knowledge discovery based on diverse bio-ontologies. PubMine improves query accuracy and flexibility with advanced search capabilities of fuzzy search, wildcard search, proximity search, range search, and the Boolean combinations. Furthermore, PubMine allows users to extract multi-dimensional relationships between genes, diseases, and chemical compounds by using OLAP (On-Line Analytical Processing) techniques. The HUGO gene symbols and the MeSH ontology for diseases, chemical compounds, and anatomy have been included in the current version of PubMine, which is freely available at http://pubmine.kobic.re.kr. Conclusions: PubMine is a unique bio-text mining system that provides flexible searches and analysis of biological entity relationships. We believe that PubMine would serve as a key bioinformatics utility due to its rapid response to enable web services for community and to the flexibility to accommodate general ontology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.8
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• pp.1750-1755
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• 2011

Recently, it is possible for users to acquire necessary data easily as the various schemes of the searching information are developed. Since these data rise continuously like stream data, it is required to extract the appropriate data for the user's needs from the mass data on the internet. In the traditional scheme, they are acquired by processing the user queries after the occurred data are stored at a database. However, it is inefficient to process the user queries over the large volume of continuous data by using the traditional scheme. In this paper, we propose the Event-Profile Model to define the data occurrence on the internet as the events and the user's requirements as the profiles. We also propose and implement the filtering scheme to process the events and the profiles efficiently. We evaluate the performance of the proposed scheme and our experiments show that the new scheme outperforms the other on various dataset.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1156-1166
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• 1999

의사결정 시스템은 전사적인 의사결정과 전략적 정보수집을 위해 거대한 량의 정보를 빠른 시간내에 제공할 것을 요구한다. 데이타 웨어하우스는 이러한 정보를 신속히 제공하기 위해 여러 지역 데이타베이스로부터 필요한 정보를 사전에 추출하고 가공 및 통합하여 별도의 저장공간에 저장한다. 일반적으로, 웨어하우스 내의 정보는 지역 데이타베이스에 저장된 정보에 대한 실체화된 뷰로서 간주하며 지역 데이타의 변경에 따라 일관성을 유지하도록 반영해야 한다. 본 논문에서는 일관성을 유지하기 위해 정보 공유가 가능한 데이타 웨어하우스 시스템의 구조와 비-보상 실체 뷰 관리 기법을 제안한다. 본 논문에서 제안한 데이타 웨어하우스 시스템의 구조는 지역 데이타베이스에서 추출된 정보를 관리하는 별도의 지역 정보 관리자를 두어 뷰 관리자들 간의 정보 공유가 가능하게 한다. 비-보상 실체 뷰 관리 기법은 지역 데이타 변경 사건에 따른 뷰 관리 시 다른 사건에 의해 영향을 받지 않도록 하기 때문에 기본의 사전 보상이나 나중 보상 기법과는 달리 추가적인 질의 처리를 요구하지 않는 기법이다.Abstract A decision support system(DSS) commonly requires fast access to tremendous volume of information. A data warehouse is a database storing the information that is extracted, filtered and integrated from several relevant local databases to reply upon aggregated queries. The information stored in the data warehouse can be regarded as materialized views. The materialized view has to be modified according to the change of the corresponding local databases to preserve the data consistency. In this paper, we propose a data warehousing system architecture allowing information sharing (DAWINS), and a non-compensating materialized view maintenance algorithm(NCA). DAWINS architecture allows relevant information to be shared by individual view managers with local data manager for each local database. Unlikely to the pre- or post-compensating algorithms, which are required to remove the effects of some events to other view in the process of view maintenance, NCA does not require any additional query processing, since a local data manager in DAWINS already maintains the effects of update events occurring in local systems.