• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.265-278
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• 2006

This paper presents a two-dimensional type inheritance hierarchy index(2D-THI) for XML databases. XML Schema is one of schema models for the XML documents supporting. The type inheritance. The conventional indexing techniques for XML databases can not support XML queries on type inheritance hierarchies. We construct a two-dimensional index structure using multidimensional file organizations for supporting type inheritance hierarchy in XML queries. This indexing technique deals with the problem of clustering index entries in the two-dimensional domain space that consists of a key element domain and a type identifier domain based on the user query pattern. This index enhances query performance by adjusting the degree of clustering between the two domains. For performance evaluation, we have compared our proposed 2D-THI with the conventional class hierarchy indexing techniques in object-oriented databases such as CH-index and CG-tree through the cost model. As the result of the performance evaluations, we have verified that our proposed two-dimensional type inheritance indexing technique can efficiently support the query Processing in XML databases according to the query types.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.1-14
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• 2004

In this study, we propose Linktable based on extended TB-Tree(LTB-Tree) which can improve the performance of existing TB (Trajectory-Bundle)-tree proposed for indexing the trajectory of moving objects in GIS Applications. In addition, in order to evaluate proposed indexing scheme, we take into account as follows. At first, we select existing R*-tree, TB-tree, and LTB-tree as the subject of performance evaluation. Secondly, we make use of random data set and real data set as experimental data. Thirdly, we evaluate the performance with respect to the variation of size of memory buffer by considering the restriction of available memory of a given system. Fourth, we test them by using the experimental data set with a variation of data distribution. Finally, we think over insertion and retrieval performance of trajectory query and range query as experimental measures. The experimental results show that the proposed indexing scheme, LTB-tree, gains better performance than traditional other schemes with respect to the insertion and retrieval of trajectory query.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.72-81
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• 2004

Theses days, a number of data over the Internet are represented using XML because of a virtue of XML. In proportion to the increase of XML data, query processing techniques are required that support quickly and efficiently the diverse queries to search the useful information on XML documents. But, up to now, the researches handling queries for XML data are methodologies focusing on how to process regular path expressions. Therefore, we have developed a new genealogy-based indexing technique to solve various queries such as not only regular path expression but also simple path expression, path expression referencing other elements, and so on. Also, we have applied this technique on object-relational model and evaluated the performance for many documents and various query types. The result shows improved performance in comparison with the other storage techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1886-1908
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• 2020

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively.

• Journal of the Korea Computer Industry Society
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• v.2 no.8
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• pp.1089-1098
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• 2001

Structure information contained in a structured document supports various access paths to document. In order to use structure information contained in a structured document, it is required to construct an index structural on document structures. Content indexing and structure indexing per document require high memory overhead. Therefore, processing of pure structure queries based on document structure like relationship between elements or element orders, low memory overhead for indexing are required. This paper suggests the GDIT(Global Document Instance Tree) data structure and indexing scheme about structure of document which supports low memory overhead for indexing and powerful types of user queries. The structure indexing scheme only index the lowest level element of document and does not effect number of document having retrieval element. Based on the index structure, we propose an query processing algorithm about pure structure, proof the indexing schemes keeps up indexing efficient in terms of space. The proposed index structure bases GDR concept and uses index technique based on GDIT.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.70-75
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• 2015

In this paper, we explore the efficient processing of continuous range queries over a huge number of moving objects, each of which retrieves the moving objects that are currently located within a geographic query region of interest. The moving objects should continually communicate with the server to report their current locations, so as to keep the results of the continuous range queries up-to-date. However, this increases the server workload and involves a enormous amount of communication as the number of continuous range queries and the moving objects becomes enormous. In this paper, we adopt an approach where we leverage available memory and computational resources of the moving objects in order to resolve these problems. To this end, we propose a query indexing structure, referred to as the Space Partitioning Query Index(SPQI), which enables the server to efficiently cooperate with the moving objects for processing continuous range queries. SPQI improves system performance in terms of server workload and communication cost. Through simulations, we show the superiority of SPQI.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.1
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• pp.11-16
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• 2006

To manipulate large video databases, effective video indexing and retrieval are required. A large number of video indexing and retrieval algorithms have been presented for frame-w]so user query or video content query whereas a relatively few video sequence matching algorithms have been proposed for video sequence query. In this paper, we propose an efficient algorithm to extract key frames using color histograms and to match the video sequences using edge features. To effectively match video sequences with low computational load, we make use of the key frames extracted by the cumulative measure and the distance between key frames, and compare two sets of key frames using the modified Hausdorff distance. Experimental results with several real sequences show that the proposed video retrieval algorithm using color and edge features yields the higher accuracy and performance than conventional methods such as histogram difference, Euclidean metric, Battachaya distance, and directed divergence methods.

• International Journal of Internet, Broadcasting and Communication
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• v.5 no.1
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• pp.14-17
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• 2013

RDF data is widely used for exchanging data nowadays to enable semantic web era. This leads to the need for storing and retrieving these data efficiently and effectively. Recently, the structure index in graph-based perspective is considered as a promising approach to deal with issues of complex query graphs. However, even though there are many researches based on structure indexing, there can be a better architectural approach instead of addressing the issue as a part. In this research, we propose architecture for storing, query processing and retrieving RDF data in efficient manner using structure indexing. Our research utilizes research results from iStore and 2 relation-based approaches and we focus on improving query processing to reduce the time of loading data and I/O cost.

• 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.