• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.55-65
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• 2005

This paper presents a bulk insertion technique for efficiently inserting data items. Traditional moving object database focused on efficient query processing that happens mainly after index building. Traditional index structures rarely considered disk I/O overhead for index rebuilding by inserting data items. This paper, to solve this problem, describes a new bulk insertion technique which efficiently induces the current positions of moving objects and reduces update cost greatly. This technique uses buffering technique for bulk insertion in spatial index structures such as R-tree. To analyze split or merge node, we add a secondary index for information management on leaf node of primary index. And operations are classified to reduce unnecessary insertion and deletion. This technique decides processing order of moving objects, which minimize split and merge cost as a result of update operations. Experimental results show that this technique reduces insertion cost as compared with existing insertion techniques.

• Journal of Korea Spatial Information System Society
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• v.7 no.2 s.14
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• pp.67-79
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• 2005

For tracing tag locations, the trajectories should be modeled and indexed in a radio frequency identification (RFID) system. The trajectory of a tag is represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry. Because the information that a tag stays in a reader is missing from the trajectory represented only as a point, it is impossible to find the tag that remains in a reader. To solve this problem we propose the data model in which trajectories are defined as intervals and new index scheme called the Interval R-tree. We also propose new insert and split algorithms to enable efficient query processing. We evaluate the performance of the proposed index scheme and compare it with the R-tree and the R*-tree. Our experiments show that the new index scheme outperforms the other two in processing queries of tags on various datasets.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.23-37
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• 1994

In this paper, we design and implement the prototype system, and propose the Extended Faceted Classification. Scheme and the Hybrid Retrieval Method that support classifying the software components, storing in library, and efficient retrieval according to user's request. In order to designs the classification scheme, we identify several necessary items by analyzing basic classes of software components that are to be classified. Then, we classify the items by their characteristics, decide the facets, and compose the component descriptors. According to their basic characteristics, we store software components in the library by clustering their application domains and are assign weights to the facets and its items to describe the component characteristics. In order to retrieve the software components, we use the retrieval-by-query model, and the weights and similarity for easy retrieval of similar software components. As the result of applying proposed classification scheme and retrieval model, we can easily identify similar components and the process of classification become simple. Also, the construction of queries becomes simple, the control of the size and order of the components to be retrieved possible, and the retrieval effectiveness is improved.

• The Transactions of the Korea Information Processing Society
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• v.3 no.6
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• pp.1443-1452
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• 1996

In this paper, we propose a semantic similarity measure for reusable software components, which aims to provide the automatic classification process of reusable to be stored in the structure of a software library, and to provide an efficient retrieval method of the software components satisfying the user's requirements. We have identified the facets to represent component characteristics by extracting information from the component descriptions written in a natural language, composed the software component identifiers from the automatically extracted terms corresponding to each facets, and stored them which the components in the nearest locations according to the semantic similarity of the classified components. In order to retrieve components satisfying user's requirements, we measured a semantic similarity between the queries and the stored components in the software library. As a result of using the semantic similarity to retrieve reusable components, we could not only retrieve the set of components satisfying user's queries. but also reduce the retrieval time of components of user's request. And we further improve the overall retrieval efficiency by assigning relevance ranking to the retrieved components according to the degree of query satisfaction.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.10
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• pp.539-549
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• 2002

PC cluster-based Information Retrieval (IR) systems improve their performances by parallel processing of query terms using cluster nodes. However TCP/IP based communication used to exchange data between cluster nodes prevents the performance from being improved further. The user-level communication mechanisms solve the problem by eliminating the time-consuming kernel access in exchanging data between cluster nodes. The Virtual Interface Architecture (VIA) is one of the representative user-level communication mechanisms which provide low latency and high bandwidth. In this paper, we propose a VIA-based parallel IR system on a PC cluster. The IR system is implemented using the following three communication methods: Sealable Coherent Interface (SCI) based VIA, MPI on SCI based VIA, MPI on Fast Ethernet based VIA. Through experiments, the performances of the three methods are analyzed in various aspects.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.325-336
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• 2006

For supporting LBS service, recent studies on spatial network databases (SNDB) have been done actively. In order to gain good performance on query processing in SNDB, we, in this paper. design efficient storage and index structures for spatial network data, point of interests (POIs), and moving objects on spatial networks. First, we design a spatial network file organization for maintaining the spatial network data itself consisting of both node and edges. Secondly, we design a POI storage and index structure which is used for gaining fast accesses to POIs, like restaurant, hotel, and gas station. Thirdly, we design a signature-based storage and index structure for efficiently maintaining past, current, and expected future trajectory information of moving objects. Finally, we show that the storage and index structures designed in this paper outperform the existing storage structures for spatial networks as well as the conventional trajectory index structures for moving objects.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.63-68
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• 2022

Currently, there are a total of 68 emergency rooms based on Seoul, South Korea, and there is a portal site that allows you to inquire the location of the emergency room, but it is difficult to use in an actual emergency situation because it consists of selecting a gu and a self-governing dong. In addition, it may be more efficient to go to the emergency room directly because you may miss the golden time necessary for survival in a situation where you call 119 and wait for the rescue team. Therefore, in this paper, we propose a service that can quickly search the location of the emergency room based on a specific location through various functions supported by MongoDB. After downloading emergency room location data based on Seoul Metropolitan City, storing it in MongoDB, processing the data through various processing techniques, and applying a spatial index, you can query the emergency room based on distance from a specific location in real time.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.103-116
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• 2017

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.441-451
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• 2005

HTML is mostly stored in native file systems instead of specialized repositories such as a database. Like HTML, XML, the standard for the exchange and the representation of data in the Internet, is mostly resident on native file systems. However. since XML data is irregular and verbose, the disk space and the network bandwidth are wasted compared to those of regularly structured data. To overcome this inefficiency of XML data, the research on the compression of XML data has been conducted. Among recently proposed XML compression techniques, some techniques do not support querying compressed data, while other techniques which support querying compressed data blindly encode data values using predefined encoding methods without considering the types of data values which necessitates partial decompression for processing range queries. As a result, the query performance on compressed XML data is degraded. Thus, this research proposes an XML compression technique which supports direct and efficient evaluations of queries on compressed XML data. This XML compression technique adopts an encoding method, called dictionary encoding, to encode each tag of XML data and applies proper encoding methods for encoding data values according to the inferred types of data values. Also, through the implementation and the performance evaluation of the XML compression technique proposed in this research, it is shown that the implemented XML compressor efficiently compresses real-life XML data lets and achieves significant improvements on query performance for compressed XML data.

• The KIPS Transactions:PartA
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• v.13A no.6 s.103
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• pp.545-554
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• 2006

Although the current Web search engines provide tremendous information, it is hard to find right information among the huge information. Users need to spend extra time to filter out unnecessary information. In order to overcome the limit of current Web search engines, Semantic Web was developed to provide efficient search, integration, and reuse of information by structuring semantic information from Web resources. In this paper, an elementary education contents retrieval system using Semantic Web is proposed. The proposed system emphasizes history contents that have high relevancy among data. For construction of the proposed system, ontology is proposed first for elementary study contents and ontology for historical contents is proposed for easy access to those contents using semantic relation among them. Based on the ontology, the proposed system is designed and implemented. The proposed system has the following characteristics. First, the system provides various query formats in detail so that search results can be refined efficiently. Second, the system presents only semantically information connected with key words or including key words using study contents ontology. Finally, the proposed system can increase study effects by presenting various contents that are related with query by users.