• Journal of Industrial Technology
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• v.20 no.B
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• pp.213-218
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• 2000

The nearest neighbor query is an important operation widely used in multimedia databases for finding the object that is most similar to a given query object. Most of techniques for processing nearest neighbor queries employ multidimensional indexes for effective indexing of objects. However, the performance of previous multidimensional indexes, which use N-dimensional rectangles or spheres for representing the capsule of the object cluster, deteriorates seriously as the number of dimensions gets higher. This paper proposes a new index structure based singular value decomposition resolving this problem and the query processing method using it. We also verify the superiority of our approach through performance evaluation by performing extensive experiments.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.521-531
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• 2003

Multi-level geographic data can be mainpulated by a window query such as a zoom operation. In order to handle multi-level geographic data efficiently, a spatial indexing method supporting a window query is needed. However, the conventional spatial indexing methods are not efficient to access multi-level geographic data quickly. To solve it, other a few spatial indexing methods for multi-level geographic data are known. However these methods do not support all types of multi-level geographic data. This paper presents a new efficient spatial indexing method, the MLR-tree for window query of multi-level geographic data. The MLR-tree offers both high search performance and no data redundancy. Experiments show them. Moreover, the MLR-tree supports all types of multi-level geographic data.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.533-535
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• 2003

The Euclidean distance is sensitive to the absolute offsets of time sequences, so it is not a suitable similarity measure in terms of shape. In this paper. we propose an indexing scheme for efficient matching and retrieval of time sequences based on the minimum distance. The minimum distance can give a better estimation of similarity in shape between two time sequences. Our indexing scheme can match time sequences of similar shapes irrespective of their vortical positions and guarantees no false dismissals

• Journal of Platform Technology
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• v.11 no.6
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• pp.89-105
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• 2023

With the development of information technology, most of the information has been converted into digital information, leading to the Big Data era. The demand for search platform has increased to enhance accessibility and usability of information in the databases. Big data search software platforms consist of two main components: (1) an indexing component to generate and store data indices for a fast and efficient data search and (2) a searching component to look up the given data fast. As an amount of data has explosively increased, data indexing performance has become a key performance bottleneck of big data search platforms. Though many companies adopted big data search platforms, relatively little research has been made to improve indexing performance. This research study employs Elasticsearch platform, one of the most famous enterprise big data search platforms, and builds physical clusters of 3 nodes to investigate optimal indexing performance configurations. Our comprehensive experiments and studies demonstrate that the proposed optimal Elasticsearch configuration achieves high indexing performance by an average of 3.13 times.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.610-617
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• 2003

Similarity matching in video database is of growing importance in many new applications such as video clustering and digital video libraries. In order to provide efficient access to relevant data in large databases, there have been many research efforts in video indexing with diverse spatial and temporal features. however, most of the previous works relied on sequential matching methods or memory-based inverted file techniques, thus making them unsuitable for a large volume of video databases. In order to resolve this problem, this paper proposes an effective and scalable indexing technique using a trie, originally proposed for string matching, as an index structure. For building an index, we convert each frame into a symbol sequence using a window order heuristic and build a disk-resident trie from a set of symbol sequences. For query processing, we perform a depth-first search on the trie and execute a temporal segmentation. To verify the superiority of our approach, we perform several experiments with real and synthetic data sets. The results reveal that our approach consistently outperforms the sequential scan method, and the performance gain is maintained even with a large volume of video databases.

• Journal of KIISE:Databases
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• v.31 no.6
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• pp.650-663
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• 2004

In molecular biology, DNA sequence searching is one of the most crucial operations. Since DNA databases contain a huge volume of sequences, a fast indexing mechanism is essential for efficient processing of DNA sequence searches. In this paper, we first identify the problems of the suffix tree in aspects of the storage overhead, search performance, and integration with DBMSs. Then, we propose a new index structure that solves those problems. The proposed index consists of two parts: the primary part represents the trie as bit strings without any pointers, and the secondary part helps fast accesses of the leaf nodes of the trio that need to be accessed for post processing. We also suggest an efficient algorithm based on that index for DNA sequence searching. To verify the superiority of the proposed approach, we conducted a performance evaluation via a series of experiments. The results revealed that the proposed approach, which requires smaller storage space, achieves 13 to 29 times performance improvement over the suffix tree.

• Journal of KIISE:Databases
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• v.35 no.5
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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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• v.32 no.3
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• pp.254-262
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• 2005

This paper presents a new sequential indexing method called segment-page indexing (SP-indexing) for multidimensional range queries. The design objectives of SP-indexing are twofold:(1) improving the range query performance of multidimensional indexing methods (MIMs) and (2) providing a compromise between optimal index clustering and the full index reorganization overhead. Although more than ten years of database research has resulted in a great variety of MIMs, most efforts have focused on data-level clustering and there has been less attempt to cluster indexes. As a result, most relevant index nodes are widely scattered on a disk and many random disk accesses are required during the search. SP-indexing avoids such scattering by storing the relevant nodes contiguously in a segment that contains a sequence of contiguous disk pages and improves performance by offering sequential access within a segment. Experimental results demonstrate that SP-indexing improves query performance up to several times compared with traditional MIMs using small disk pages with respect to total elapsed time and it reduces waste of disk bandwidth due to the use of simple large pages.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.112-114
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• 2011

On multimedia databases, in order to realize the fast access method, indexing methods for the multidimension data space are used. However, since it is a premise to use the Euclid distance as the distance measure, this method lacks in flexibility. On the other hand, there are metric indexing methods which require only to satisfy distance axiom. Since metric indexing methods can also apply for distance measures other than the Euclid distance, these methods have high flexibility. This paper proposes an improved method of VP-tree which is one of the metric indexing methods. VP-tree follows the node which suits the search range from a route node at searching. And distances between a query and all objects linked from the leaf node which finally arrived are computed, and it investigates whether each object is contained in the search range. However, search speed will become slow if the number of distance calculations in a leaf node increases. Therefore, we paid attention to the candidates selection method using the triangular inequality in a leaf node. As the improved methods, we propose a method to use the nearest neighbor object point for the query as the datum point of the triangular inequality. It becomes possible to make the search range smaller and to cut down the number of times of distance calculation by these improved methods. From evaluation experiments using 10,000 image data, it was found that our proposed method could cut 5%~12% of search time of the traditional method.

• Journal of KIISE:Databases
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• v.37 no.1
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• pp.44-49
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• 2010

In this paper, we first address the problems associated with indexing and searching for reduced-dimensional vectors, which are reduced by using a combination of angle approximation and dimension grouping. Then, we propose a novel method to solve the problems. We also show the superiority of the proposed method by performing extensive experiments with synthetic and real-life data sets.