• Journal of KIISE:Databases
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• 제35권5호
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• pp.447-458
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• 2008

In this paper, we address an efficient processing scheme for k-nearest neighbor queries to retrieve k static objects in road network databases. Existing methods cannot expect a query processing speed-up by index structures in road network databases, since it is impossible to build an index by the network distance, which cannot meet the triangular inequality requirement, essential for index creation, but only possible in a totally ordered set. Thus, these previous methods suffer from a serious performance degradation in query processing. Another method using pre-computed network distances also suffers from a serious storage overhead to maintain a huge amount of pre-computed network distances. To solve these performance and storage problems at the same time, this paper proposes a novel approach that creates an index for moving objects by approximating their network distances and efficiently processes k-nearest neighbor queries by means of the approximate index. For this approach, we proposed a systematic way of mapping each moving object on a road network into the corresponding absolute position in the m-dimensional space. To meet the triangular inequality this paper proposes a new notion of average network distance, and uses FastMap to map moving objects to their corresponding points in the m-dimensional space. After then, we present an approximate indexing algorithm to build an R*-tree, a multidimensional index, on the m-dimensional points of moving objects. The proposed scheme presents a query processing algorithm capable of efficiently evaluating k-nearest neighbor queries by finding k-nearest points (i.e., k-nearest moving objects) from the m-dimensional index. Finally, a variety of extensive experiments verifies the performance enhancement of the proposed approach by performing especially for the real-life road network databases.

• Journal of KIISE:Databases
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• 제30권1호
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• pp.27-40
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• 2003

The inverted index widely used in the existing information retrieval field should be extended for XML documents to support containment queries by XML information retrieval systems. In this paper, we consider that there are two methods in storing the inverted index and processing containment queries for XML documents as the previous work suggested: using a RDBMS or using an inverted lift engine. It has two drawbacks to extend the inverted index in the previous work. One is that using a RDBMS is moth worse in the performance than using an inverted list engine. The other is that when containment queries are processed in a RDBMS, there is an increase in the number of a join operation as the path length of a query increases and a join operation always happens between large fables. In this paper. we extend the inverted index in a different way to solve these problems and show the effectiveness of using a RDBMS.

• The Transactions of the Korea Information Processing Society
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• 제6권1호
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• pp.32-41
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• 1999

In spatial database system, the nearest neighbor query occurs frequently and requires the processing cost higher than other spatial queries do. The number of nodes to be searched in the index can be minimized for optimizing the cost of processing the nearest neighbor query. The optimal search distance is pr9posed for the measurement of a search distance to accurately select the nodes which will be searched in the nearest neighbor query. In this paper, we prove properties of the optimal search distance in N-dimensional. We show through experiments that the performance of query processing of our method is superior to other method using maximum search distance.

• Journal of Korea Spatial Information System Society
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• 제11권4호
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• pp.85-88
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• 2009

In multimedia database systems, the nearest neighbor Query occurs frequently and requires the processing cost higher than other spatial Queries do. It needs the measurement of search distance that the number of searched nodes and the computation time in an index can be minimized for optimizing the cost of processing the nearest neighbor query. The circular location property of objects is considered to accurately select the nodes which will be searched in the nearest neighbor query. In this paper, we propose the processing method of nearest neighbor queries be considered a circular location property of object where the search space consists of a circular domain and show its characteristics. The proposed method uses the circular minimum distance and the circular optimal distance, the search measurement for optimizing the processing cost of nearest neighbor queries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.993-1014
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• 2021

This paper proposes a new image retrieval method called the 3D integrated multi-index to fuse SIFT (Scale Invariant Feature Transform) visual words with other features at the indexing level. The advantage of the 3D integrated multi-index is that it can produce finer subdivisions in the search space. Compared with the inverted indices of medium-sized codebook, the proposed method increases time slightly in preprocessing and querying. Particularly, the SIFT, contour and colour features are fused into the integrated multi-index, and the joint cooperation of complementary features significantly reduces the impact of false positive matches, so that effective image retrieval can be achieved. Extensive experiments on five benchmark datasets show that the 3D integrated multi-index significantly improves the retrieval accuracy. While compared with other methods, it requires an acceptable memory usage and query time. Importantly, we show that the 3D integrated multi-index is well complementary to many prior techniques, which make our method compared favorably with the state-of-the-arts.

• The KIPS Transactions:PartD
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• 제13D권1호
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• pp.23-28
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• 2006

In this paper, a power-aware query processing using optimized distributed R-tree in a sensor network is proposed. The proposed technique is a new approach for processing range queries that uses spatial indexing. Range queries are most often encountered under sensor networks for computing aggregation values. The previous work just addressed the importance but didn't provide any efficient technique for processing range queries. A query processing scheme is thus designed for efficiently processing them. Each node in the sensor network has the MBR of the region where its children nodes and the node itself are located. The range query is evaluated over the region which intersects the geographic location of sensors. It ensures the maximum power savings by avoiding the communication of nodes not participating over the evaluation of the query.

• Journal of KIISE
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• 제42권4호
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• pp.522-529
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• 2015

Ever since the R-tree was proposed to index multi-dimensional data, many efforts have been made to improve its query performances. One common trend to improve query performance is to parallelize query processing with the use of multi-core architectures. To this end, a GPU-base R-tree has been recently proposed. However, even though a GPU-based R-tree can exhibit an improvement in query performance, it is limited in its ability to handle large volumes of data because GPUs have limited physical memory. To address this problem, we propose MGR-tree (Multi-GPU R-tree), which can manage large volumes of data by dividing nodes into multiple GPUs. Our experiments show that MGR-tree is up to 9.1 times faster than a sequential search on a GPU and up to 1.6 times faster than a conventional GPU-based R-tree.

• Journal of the Korean Society for Library and Information Science
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• 제57권3호
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• pp.249-277
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• 2023

The purpose of this study is to explore the potential contribution of citation metrics to improving the search performance of citation index databases. To this end, the study generated ten queries in the field of library and information science and conducted experiments based on the relevance assessment using 3,467 documents retrieved from the Web of Science and 60,734 documents published in 85 SSCI journals in the field of library and information science from 2000 to 2021. The experiments included re-ranking of the top 100 search results using citation metrics and search methods, query expansion experiments using vector space model retrieval systems, and the construction of a citation-based re-ranking system. The results are as follows: 1) Re-ranking using citation metrics differed from Web of Science's performance, acting as independent metrics. 2) Combining query term frequencies and citation counts positively affected performance. 3) Query expansion generally improved performance compared to the vector space model baseline. 4) User-based query expansion outperformed system-based. 5) Combining citation counts with suitability documents affected ranking within top suitability documents.

• The Journal of Society for e-Business Studies
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• 제18권2호
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• pp.153-173
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• 2013

The ontology has been gaining increasing interests by recent arise of the semantic web and related technologies. The focus is mostly on inference query processing that requires high-level techniques for storage and searching ontologies efficiently, and it has been actively studied in the area of semantic-based searching. W3C's recommendation is to use RDFS and OWL for representing ontologies. However memory-based editors, inference engines, and triple storages all store ontology as a simple set of triplets. Naturally the performance is limited, especially when a large-scale ontology needs to be processed. A variety of researches on proposing algorithms for efficient inference query processing has been conducted, and many of them are based on using proven relational database technology. However, none of them had been successful in obtaining the complete set of inference results which reflects the five characteristics of the ontology properties. In this paper, we propose a new index structure called hyper cube index to efficiently process inference queries. Our approach is based on an intuition that an index can speed up the query processing when extensive inferencing is required.

• KIISE Transactions on Computing Practices
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• 제23권5호
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• pp.275-283
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• 2017

A large amount of RDF data has been generated along with the increase of semantic web services. Various distributed storage and query processing schemes have been studied to efficiently use the massive amounts of RDF data. In this paper, we propose a distributed SPARQL query processing scheme that considers the data locality and query execution path of large RDF data. The proposed scheme considers the data locality and query execution path in order to reduce join and communication costs. In a distributed environment, when processing a SPARQL query, it is divided into several sub-queries according to the conditions of the WHERE clause by considering the data locality. The proposed scheme reduces data communication costs by grouping and processing the sub-queries through the index based on associated nodes. In addition, in order to reduce unnecessary joins and latency when processing the query, it creates an efficient query execution path considering data parsing cost, the amount of each node's data communication, and latency. It is shown through various performance evaluations that the proposed scheme outperforms the existing scheme.