• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.689-698
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• 2005

Various systems are used to provide the location based services. But, the existing systems have some problems which have difficulties in dealing with faster services for above million people. In order to solve it, a multi-level DBMS which supports both fast data processing and large data management support should be used. The multi-level DBMS with snapshots has all the data existing in disk database and the data which are required to be processed for fast processing are managed in main memory database as snapshots. To optimize performance of this system for location based services, the query classification component which classifies the queries for efficient snapshot usage is needed. In this paper, the query classification component in multi-level DBMS for location based services is designed and implemented. The proposed component classifies queries into three types: (1) memory query, (2) disk query, (3) hybrid query, and increases the rate of snapshot usage. In addition, it applies division mechanisms which divide aspatial and spatial filter condition for partial snapshot usage. Hence, the proposed component enhances system performance by maximizing the usage of snapshot as a result of the efficient query classification.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.1
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• pp.8-18
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• 2008

In this paper, we propose an efficient algorithm using wavelet transform for still image database retrieval. Especially, it uses only the lowest frequency sub-band in multi-level wavelet transform so that a retrieval system uses a smaller quantity of memory and takes a faster processing time. We extract different textured features, statistical information such as mean, variance and histogram, from low frequency sub-band. Then we measure the distances between the query image and the images in a database in terms of these features. To obtain good retrieval performance, we use the first feature (mean and variance of wavelet coefficients) to filter out most of the unlikely images. The rest of the images are considered to be candidate images. Then we apply the second feature (histogram of wavelet coefficient) to rank all the candidate images. To evaluate the algorithm, we create various distorted image databases using MIT VisTex texture images and PICS natural images. Through simulations, we demonstrate that our method can achieve performance satisfactorily in terms of the retrieval accuracy as well as the both memory requirement and computational complexity. Therefore it is expected to provide good retrieval solution for JPEG-2000 using wavelet transform.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.841-850
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• 1999

Many commercial database systems maintain histograms to summarize the contents of relations, permit efficient estimation of query result sizes, and access plan costs. In spatial database systems, most query predicates consist of topological relationship between spatial objects, and ti is ver important to estimate the selectivity of those predicates for spatial query optimizer. In this paper, we propose a selectivity estimation scheme for spatial topological predicates based on the multi-dimensional histogram and the transformation scheme. Proposed scheme applies two partition strategies on transformed object space to generate spatial histogram, and estimates the selectivity of topological predicates based on the topological characteristic of transformed space. Proposed scheme provides a way for estimating the selectivity without too much memory space usage and additional I/Os in spatial query optimizer.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.732-736
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• 2010

Processing a large amount of data becomes more important than ever. Particularly, the information queries which require multi-dimensional record scan can be efficiently implemented with SIMD instruction sets. In this article, we present a SIMD record scan technique which employs row-based scanning. Our technique is different from existing SIMD techniques for predicate processes and aggregate operations. Those techniques apply SIMD instructions to the attributes in the same column of the database, exploiting the column-based record organization of the in-memory database systems. Whereas, our SIMD technique is useful for multi-dimensional record scanning. As the sizes of registers and the memory become larger, our row-based SIMD scan can have bigger impact on the performance. Moreover, since our technique is orthogonal to the parallelization techniques for multi-core processors, it can be applied to both uni-processors and multi-core processors without too many changes in the software architectures.

• Journal of KIISE:Databases
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• v.33 no.4
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• pp.363-371
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• 2006

In spite of computer's development, the present state of a lot of electronic documents overflowed it's going to be more difficult to get appropriate information. Therefore it's more important to get meaningful information than to focus on the speed of processing. Semantic web enables and intelligent processing by adding semantic meta data on your web documents. Also as the semantic web grows, the knowledge resource is more important. In this paper, we propose a RDF storage system using relational database model aimed at intelligent processing by adding semantic meta data on your web documents, also a query processor aimed at query processing through the storage system. By using relational model, we could overcome a weakness of object or memory model.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.1-13
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• 1994

The Temporal Database Management Testbed supports valid and transaction time. In this paper, we discuss the design and implementation of a testbed of a temporal database management system in main memory. The testbed consists of a syntactic analyzer, a semantic analyzer, a code generator, and an interpreter. The syntactic analyzer builds a parse tree from a temporal query. The semantic analyzer then checks it for correctness against the system catalog. The code generator builds an execution tree termed ann update network. We employ an incremental view materialization for the execution tree. After building the execution tree, the interpreter activates each node of the execution tree. Also, the indexing structure and the concurrency control are discussed in the testbed.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.167-176
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• 2012

In this paper we design and implement a new Hybrid Equipment Data Acquisition System (HEDAS) for data collection of semiconductor and optoelectronic manufacturing equipments in the equipment engineering system(EES) framework. The amount of the data collected from equipments have increased rapidly in equipment engineering system. The proposed HEDAS efficiently handles a large amount of real-time equipment data generated from EES framework. It also can support the real-time ESS applications as well as non real-time ESS applications. For the real-time EES applications, it performs high-speed real-time processing that uses continuous query and filtering techniques based on memory buffers. The HEDAS can optionally store non real-time equipment data using a HEDAS-based database or a traditional DBMS-based database. In particular, The proposed HEDAS offers the compression indexing based on the timestamp of data and query processing technique saving the cost of disks storage against extremely increasing equipment data. The HEDAS is efficient system to collect huge real-time and non real-time equipment data and transmit the collected equipment data to several EES applications in EES framework.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.39-47
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• 2004

Spatio-temporal databases have been mostly studied in the area of access methods. However, without considering an extraordinary update maintenance overhead after building up a spatio-temporal index, most indexing techniques have focused on fast query processing only. In this paper, we propose an efficient update management method that reduces the number of disk accesses required in order to apply the updates of moving objects to a spatio-temporal index. We consider realistic update patterns that can represent the movements of objects properly. We present a memory based structure that can efficiently maintain a small number of very frequently updating objects. For an experimental environment with realistic update patterns, the number of disk accesses of our method is about 40% lower than that of a general update method of existing spatio-temporal indexes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1862-1878
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• 2011

With the advances in wireless communication technology and the advent of smartphones, research on location-based services (LBSs) is being actively carried out. In particular, several spatial index methods have been proposed to provide efficient LBSs. However, finding an optimal indexing method that balances query performance and index size remains a challenge in the case of wireless environments that have limited channel bandwidths and device resources (computational power, memory, and battery power). Thus, mechanisms that make existing spatial indexing techniques more efficient and highly applicable in resource-limited environments should be studied. Bitmap-based Spatial Indexing (BSI) has been designed to support LBSs, especially in wireless broadcast environments. However, the access latency in BSI is extremely large because of the large size of the bitmap, and this may lead to increases in the search time. In this paper, we introduce a Selective Bitmap-based Spatial Indexing (SBSI) technique. Then, we propose an Adaptive Bitmap-based Spatial Indexing (ABSI) to improve the tuning time in the proposed SBSI scheme. The ABSI is applied to the distribution of geographical objects in a grid by using the Hilbert curve (HC). With the information in the ABSI, grid cells that have no objects placed, (i.e., 0-bit information in the spatial bitmap index) are not tuned during a search. This leads to an improvement in the tuning time on the client side. We have carried out a performance evaluation and demonstrated that our SBSI and ABSI techniques outperform the existing bitmap-based DSI (B DSI) technique.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.147-158
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• 2009

Recently, the data model in stream data environment has massive, continuous, and infinity properties. However the stream data processing like query process or data analysis is conducted using a limited capacity of disk or memory. In these environment, the traditional frequent pattern discovery on transaction database can be performed because it is difficult to manage the information continuously whether a continuous stream data is the frequent item or not. In this paper, we propose the method which we are able to predict the frequent items using the regression model on continuous stream data environment. We can use as a prediction model on indefinite items by constructing the regression model on stream data. We will show that the proposed method is able to be efficiently used on stream data environment through a variety of experiments.