• Journal of applied mathematics & informatics
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• v.39 no.1_2
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• pp.105-116
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• 2021

Information systems and decision rules with imprecision and uncertainty in data analysis are studied in complete residuated lattices. In this paper, we introduce the notions of distance spaces, Alexandrov pretopology (precotopology) and join-meet (meet-join) operators in complete co-residuated lattices. We investigate their relations and properties. Moreover, we give their examples.

• Journal of KIISE:Databases
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• v.29 no.1
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• pp.72-78
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• 2002

The distance join is a spatial join which finds data pairs in the order of distance between two spatial data sets using R-trees. The distance join stores node pairs in a priority queue, which are retrieved while traversing R-trees in a top-town manner, in the order of distance. This paper first shows that a priority strategy for the tied pairs in the priority queue during distance join processing has much effect on its performance, and then proposes an optimized secondary priority method. The experiments show that the proposed method is always better than the other methods in the performance perspectives.

• Journal of Korea Spatial Information System Society
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• v.9 no.3
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• pp.67-80
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• 2007

Recently, many studies on query processing algorithms has been done for spatial networks, such as roads and railways, instead of Euclidean spaces, in order to efficiently support LBS(location-based service) and Telematics applications. However, both a closest pairs query and an e-distance join query require a very high cost in query processing because they can be answered by processing a set of POIs, instead of a single POI. Nevertheless, the query processing cost for closest pairs and e-distance join queries is rapidly increased as the number of k (or the length of radius) is increased. Therefore, we propose both a closest pairs query processing algorithm and an e-distance join query processing algorithm using a POI-based materialization technique so that we can process closest pairs and e-distance join queries in an efficient way. In addition, we show the retrieval efficiency of the proposed algorithms by making a performance comparison of the conventional algorithms.

• The Pure and Applied Mathematics
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• v.28 no.4
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• pp.267-280
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• 2021

In this paper, we introduce the notions of fuzzy join (resp. meet) complete lattices and distance spaces in complete co-residuated lattices. Moreover, we investigate the relations between Alexandrov pretopologies (resp. precotopologies) and fuzzy join (resp. meet) complete lattices, respectively. We give their examples.

• Journal of KIISE:Databases
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• v.27 no.2
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• pp.238-246
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• 2000

The distance join has been introduced previously, which finds nearest pairs in the order of distance incrementally among two spatial data sets built with multidimensional indexes like R-trees. We propose efficient K-distance joins when the number(K) of pairs to find is preset. Especially, we develop a distance join algorithm with bi-directional expansion and optimized plane sweeping using selection method of sweep axis and direction. The experiments on real spatial data sets show that the proposed algorithm is much better than the former algorithms.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.373-383
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• 2001

The distance join is a spatial join which finds data pairs in the order of distance when associating two spatial data sets. This paper proposes several methods to optimize the adaptive multi-stage distance join, presented in [1]. First, we optimize the sweeping index formula which is used for selecting sweeping axis during plane sweeping. Second, to improve the performance of a priority queue used for maintaining node pairs, we propose to use the maximum distance of a node pair as the second priority of the queue. Moreover, we compare trade-offs in estimating the cut-off distance between under uniformity assumption of data distribution and non-uniformity assumption. The experiments show that the proposed methods greatly improve the performance of the algorithm in CPU cost as well as in I/O cost.

• The Pure and Applied Mathematics
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• v.29 no.1
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• pp.19-29
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• 2022

Information systems and decision rules with imprecision and uncertainty in data analysis are studied in complete residuated lattices. In this paper, we introduce the notions of Alexandrov pretopology (precotopology) and join-meet (meet-join) operators in complete co-residuated lattices. Moreover, their properties and examples are investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3578-3593
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• 2017

Given two collections of objects that carry both spatial and textual information in the form of tags, a $\text\underline{S}patio$-$\text\underline{T}extual$-based object $\text\underline{S}imilarity$ $\text\underline{JOIN}$ (ST-SJOIN) retrieves the pairs of objects that are textually similar and spatially close. In this paper, we have proposed a block index-based approach called BIST-JOIN to facilitate the efficient ST-SJOIN processing. In this approach, a dual-feature distance plane (DFDP) is first partitioned into some blocks based on four segmentation schemes, and the ST-SJOIN is then transformed into searching the object pairs falling in some affected blocks in the DFDP. Extensive experiments on real and synthetic datasets demonstrate that our proposed join method outperforms the state-of-the-art solutions.

• Journal of IKEEE
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• v.18 no.2
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• pp.248-254
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• 2014

The hierarchical conference causes the data delay because the exchanged data between terminals in the conference go through the hierarchical structure. In this paper, we propose an algorithm which minimizes the average path distance between terminals in a non-incremental hierarchical conference, and which considers the computing resource. For terminals which want to join the ongoing hierarchical conference, our algorithm selects terminals that the new connection is possible among terminals in the conference with the computing resource consideration. Then, after all distance values between the selected terminals and terminals which want to join the ongoing hierarchical conference are computed, and the terminal-pair which have minimum distance value is selected, the ongoing hierarchical conference is extended with a connection of the terminal-pair which consists of a terminal among selected terminals and other terminal among terminals which want to join the ongoing hierarchical conference. This continues until all terminals are included in the conference. As the experimental results with the proposed non-incremental scheme, the hierarchical conference can be organized with 24% better performance than the earlier incremental scheme on the basis of average path distance between terminals.

• Journal of KIISE
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• v.42 no.4
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• pp.504-511
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• 2015

Recently, the amount of data is rapidly increasing with the popularity of the SNS and the development of mobile technology. So, it has been actively studied for the effective data analysis schemes of the large amounts of data. One of the typical schemes is a Voronoi diagram based on kNN join algorithm (VkNN-join) using MapReduce. For two datasets R and S, VkNN-join can reduce the time of the join query processing involving big data because it selects the corresponding subset Sj for each Ri and processes the query with them. However, VkNN-join requires a high computational cost for constructing the Voronoi diagram. Moreover, the computational overhead of the VkNN-join is high because the number of the candidate cells increases as the value of the k increases. In order to solve these problems, we propose a MapReduce-based kNN-join query processing algorithm for analyzing the large amounts of data. Using the seed-based dynamic partitioning, our algorithm can reduce the overhead for constructing the index structure. Also, it can reduce the computational overhead to find the candidate partitions by selecting corresponding partitions with the average distance between two seeds. We show that our algorithm has better performance than the existing scheme in terms of the query processing time.