• Journal of Computing Science and Engineering
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• v.3 no.1
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• pp.27-58
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• 2009

Aggregation join queries are an important class of queries over data streams. These queries involve both join and aggregation operations, with window-based joins followed by an aggregation on the join output. All existing research address join query optimization and aggregation query optimization as separate problems. We observe that, by putting them within the same scope of query optimization, more efficient query execution plans are possible through more versatile query transformations. The enabling idea is to perform aggregation before join so that the join execution time may be reduced. There has been some research done on such query transformations in relational databases, but none has been done in data streams. Doing it in data streams brings new challenges due to the incremental and continuous arrival of tuples. These challenges are addressed in this paper. Specifically, we first present a query processing model geared to facilitate query transformations and propose a query transformation rule specialized to work with streams. The rule is simple and yet covers all possible cases of transformation. Then we present a generic query processing algorithm that works with all alternative query execution plans possible with the transformation, and develop the cost formulas of the query execution plans. Based on the processing algorithm, we validate the rule theoretically by proving the equivalence of query execution plans. Finally, through extensive experiments, we validate the cost formulas and study the performances of alternative query execution plans.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.36-43
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• 2016

The transformation time among queries in the database management system (DBMS) is responsible for the execution time of users' queries, because a conventional DBMS does not consider the transformation cost when queries are transformed for execution. To reduce the transformation time (cost reduction) during execution, we propose an optimal query transformation method by exploring queries from a cost-based point of view. This cost-based point of view means considering the cost whenever queries are transformed for execution. Toward that end, we explore and compare set off heuristic, linear, and exhaustive cost-based transformations. Further, we describe practical methods of cost-based transformation integration and some query transformation problems. Our results show that, some cost-based transformations significantly improve query execution time. For instance, linear and heuristic transformed queries work 43% and 74% better than exhaustive queries.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.13-38
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• 2006

Range query is one of the most important operations for spatial objects, it retrieves all spatial objects that overlap a given query region in multi-dimensional space. The DOT(DOuble Transformation) is known as an efficient indexing methods, it transforms the MBR of a spatial object into a single numeric value using a space filling curve, and stores the value in a $B^+$-tree. The DOT index is possible to be employed as a primary index for spatial objects. However, the range query processing based on the DOT index requires much overhead for spatial transformations to get the query region in the final space. Also, the detailed range query processing method for 2-dimensional spatial objects has not been studied yet in this paper, we propose an efficient multi-dimensional range query processing technique based on the DOT index. The proposed technique exploits the regularities in the moving patterns of space filling curves to divide a query region into a set of maximal sub-legions within which space filling curves traverse without interruption. Such division reduces the number of spatial transformations required to perform the range query and thus improves the performance of range query processing. A visual simulator is developed to show the evaluation method and the performance of our technique.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.1-7
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• 2002

In this paper, when users choose a query image, we implemented a content-based image retrieval system that users can simply choose and extract a object region of query wanted with not only a whole image but various objects in it. Histogram is obtained by improved HSV transformations from query image and then candidate images are retrieved rapidly by a 1st similarity measure with histogram intersection using representative colors of query image. And finally retrieved images are extracted since 2nd similarity measure with banded autocorrelogram is performed so that recall and precision are improved by combining two retrieval methods that can make up for respective weak points. Moreover images in the database are indexed automatically within feature library that makes possible to retrieve images rapidly.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.39-42
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• 2000

Extensible Markup Language(XML) is fast emerging as the dominant standard for representing data in the World Wide Web. Sophisticated query engines that allow users to effectively tap the data stored in XML documents will be crucial to exploiting the full power of XML. While there has been a great deal of activity recently proposing new semi-structured data models and query languages for this purpose, this paper explores the more conservative approach of using traditional relational database engines for processing XML documents conforming to Document Type Descriptors(DTDs). In this paper, we describe how to generate relational schemas from XML DTDs. The main issues that must be addressed inc]ode (a) dealing with the complexity of DTD element specifications (b) resolving the conflict between the two-level nature of relational schemas (table and attribute) vs. the arbitrary nesting of XML DTD schemas and (c) dealing with set-valued attributes and recursion. We now propose a set of transformations that can be used to "simplify" any arbitrary DTD without undermining the effectiveness of queries over documents conforming to that DTD.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.577-590
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• 2003

The shape-based retrieval is defined as the operation that searches for the (sub) sequences whose shapes are similar to that of a query sequence regardless of their actual element values. In this paper, we propose a similarity model suitable for shape-based retrieval and present an indexing method for supporting the similarity model. The proposed similarity model enables to retrieve similar shapes accurately by providing the combination of various shape-preserving transformations such as normalization, moving average, and time warping. Our indexing method stores every distinct subsequence concisely into the disk-based suffix tree for efficient and adaptive query processing. We allow the user to dynamically choose a similarity model suitable for a given application. More specifically, we allow the user to determine the parameter p of the distance function $L_p$ when submitting a query. The result of extensive experiments revealed that our approach not only successfully finds the subsequences whose shapes are similar to a query shape but also significantly outperforms the sequence search.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.381-392
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• 2002

This paper deals with the problem of shape-based retrieval in time-series databases. The shape-based retrieval is defined as the operation that searches for the (sub)sequences whose shapes are similar to that of a given query sequence regardless of their actual element values. In this paper, we propose an effective and efficient approach for shape-based retrieval of subsequences. We first introduce a new similarity model for shape-based retrieval that supports various combinations of transformations such as shifting, scaling, moving average, and time warping. For efficient processing of the shape-based retrieval based on the similarity model, we also propose the indexing and query processing methods. To verify the superiority of our approach, we perform extensive experiments with the real-world S&P 500 stock data. The results reveal that our approach successfully finds all the subsequences that have the shapes similar to that of the query sequence, and also achieves significant speedup up to around 66 times compared with the sequential scan method.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.425-430
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• 2022

As robots are considered one of the mainstream digital transformations, robots with machine vision becomes a main area of study providing the ability to check what robots watch and make decisions based on it. However, it is difficult to find a small object in the image mainly due to the flaw of the most of visual recognition networks. Because visual recognition networks are mostly convolution neural network which usually consider local features. So, we make a model considering not only local feature, but also global feature. In this paper, we propose a detection method of a small marker on the object using deep learning and an algorithm that considers global features by combining Transformer's self-attention technique with a convolutional neural network. We suggest a self-attention model with new definition of Query, Key and Value for model to learn global feature and simplified equation by getting rid of position vector and classification token which cause the model to be heavy and slow. Finally, we show that our model achieves higher mAP than state of the art model YOLOr.

• Journal of KIISE
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• v.42 no.2
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• pp.242-254
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• 2015

In this paper, we address the problem of improving the performance of multi-step k-NN search using multi-dimensional indexes. Due to information loss by lower-dimensional transformations, existing multi-step k-NN search solutions produce a large tolerance (i.e., a large search range), and thus, incur a large number of candidates, which are retrieved by a range query. Those many candidates lead to overwhelming I/O and CPU overheads in the postprocessing step. To overcome this problem, we propose two efficient solutions that improve the search performance by reducing the tolerance of a range query, and accordingly, reducing the number of candidates. First, we propose a tolerance reduction-based (approximate) solution that forcibly decreases the tolerance, which is determined by a k-NN query on the index, by the average ratio of high- and low-dimensional distances. Second, we propose a coefficient control-based (exact) solution that uses c k instead of k in a k-NN query to obtain a tigher tolerance and performs a range query using this tigher tolerance. Experimental results show that the proposed solutions significantly reduce the number of candidates, and accordingly, improve the search performance in comparison with the existing multi-step k-NN solution.

• Journal of KIISE:Databases
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• v.34 no.1
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• pp.18-34
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• 2007

In order to provide various business analysis methods, OLAP(On-Line Analytical Processing) systems represent their data with multidimensional structures. These multidimensional data are often delivered to users in the horizontal format of tables whose columns are corresponding to values of dimension attributes. Since the horizontal tables nay have a large number of columns, they cannot be stored directly in relational database systems. Furthermore, the tables are likely to have many null values (i.e., sparse tables). In order to manage the horizontal tables efficiently, we can store them as the vertical format of tables which has dimension attribute names as their columns thus transforms the columns of horizontal tables into rows. In this way, every queries for horizontal tables have to be transformed into those for vertical tables. This paper proposed a technique for transforming horizontal table queries into vertical table ones by utilizing not only traditional relational algebraic operators but also the PIVOT operator which recent DBMS versions are providing. For achieving this goal, we designed a relational algebraic expression equivalent to the PIVOT operator and we formally proved their equivalence. Then, we developed a transformation technique for horizontal table queries using the PIVOT operator. We also performed experiments to analyze the performance of the proposed method. From the experimental results, we revealed that the proposed method has better performance than existing methods.