• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.69-76
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• 1999

Recently, it has been studied the methods to materialize and precompute the query results for complexed spatial aggregation queries with high response time and the popular use in spatial data warehouse. In this paper, we propose extended selective materialization algorithm and present the way to materialize selectively which is considered access frequency and computation time of spatial operation according to spatial measures of spatial views for improvement of existing selective materialization algorithms.

• KIISE Transactions on Computing Practices
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• v.23 no.10
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• pp.605-610
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• 2017

In distributed environments, dynamic partitioning is needed to resolve the load on a particular server or the load caused by communication among servers. In heterogeneous environments, existing dynamic partitioning schemes can distribute the same load to a server with a low physical performance, which results in a delayed query response time. In this paper, we propose a dynamic partitioning scheme for large RDF data in heterogeneous environments. The proposed scheme calculates the query loads with its frequency and the number of vertices used in the query for load balancing. In addition, we calculate the server loads by considering the physical performance of the servers to allocate less of a load to the servers with a smaller physical performance in a heterogeneous environment. We perform dynamic partitioning to minimize the number of edge-cuts to reduce the traffic among servers. To show the superiority of the proposed scheme, we compare it with an existing dynamic partitioning scheme through a performance evaluation.

• Journal of The Korean Astronomical Society
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• v.47 no.3
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• pp.115-122
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• 2014

The New Vacuum Solar Telescope (NVST) is a 1-meter vacuum solar telescope that aims to observe the fine structures of active regions on the Sun. The main tasks of the NVST are high resolution imaging and spectral observations, including the measurements of the solar magnetic field. The NVST has been collecting more than 20 million FITS files since it began routine observations in 2012 and produces maximum observational records of 120 thousand files in a day. Given the large amount of files, the effective archiving and retrieval of files becomes a critical and urgent problem. In this study, we implement a new data archiving system for the NVST based on the Fastbit Not Only Structured Query Language (NoSQL) database. Comparing to the relational database (i.e., MySQL; My Structured Query Language), the Fastbit database manifests distinctive advantages on indexing and querying performance. In a large scale database of 40 million records, the multi-field combined query response time of Fastbit database is about 15 times faster and fully meets the requirements of the NVST. Our slestudy brings a new idea for massive astronomical data archiving and would contribute to the design of data management systems for other astronomical telescopes.

• The KIPS Transactions:PartC
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• v.15C no.1
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• pp.19-30
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• 2008

Sensor nodes are used as a storage space in the data centric storage method for sensor networks. Sensor nodes save the data to the node which is computed by hash table and users also access to the node to get the data by using hash table. One of the problems which the data centric storage method has is that queries from many users who are interested in the popular data could be concentrated to one node. In this case, responses for queries could be delayed and the energy of heavy loaded node could be dissipated fast. This would lead to reduction of network life time. In this paper, ALT, Data Centric Storage based on Adaptive Local Trajectory, is proposed as scalable data centric storage method for sensor network. ALT constructs trajectory around the storage node. The scope of trajectory is increased or decreased based on the query frequency. ALT distributes the query processing loads to several nodes so that delay of response is reduced and energy dissipation is also distributed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1539-1545
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• 2020

With the advent of the ubiquitous era due to the development of science and technology in the modern society, interest in data generated in the IoT environment has increased socially. However, the existing IoT platform has difficulties in processing inquiry requests that require large amounts of throughput, such as statistical processing of large amounts of data. Accordingly, in this paper, we propose an IoT platform that can flexibly accommodate requirements for inquiry requests and improve inquiry performance. The platform proposed in this paper showed a performance improvement of about 1200 times in terms of average response time by introducing a separate read database. By separating the object model into a command side and a query side, the complexity of the object is reduced to meet the various demands on the platform. It was made to allow quick acceptance of the matter.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.325-338
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• 2004

A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored In the form of materialized views, which represent pre-computed portions of frequently asked queries. One of the most important tasks of designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views so that the total query response time over all queries can be minimized while a limited amount of time for maintaining the views is given(maintenance-cost view selection problem). In this paper, we propose an efficient solution to the maintenance-cost view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the maintenance-cost view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in terms of time complexity over existing search-based approaches that use heuristics. Our analysis shows that the algorithm consistently yields a solution that only has an additional 10% of query cost of over the optimal query cost while at the same time exhibits an impressive performance of only a linear increase in execution time. We have implemented a prototype version of our algorithm that is used to evaluate our approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7C
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• pp.719-725
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• 2003

The content-based image retrieval system extracts features of color, shape and texture from raw images, and builds the database with those features in the indexing process. The search in the whole retrieval system is defined as a process which finds images that have large similarity to query image using the feature database. This paper proposes a new two-stage search method in the content-based image retrieval system. The method is that the features are condensed and stored by the property of Cauchy-Schwartz inequality in order to reduce the similarity computation time which takes a mostly response time from entering a query to getting retrieval results. By the extensive computer simulations, we have observed that the proposed two-stage search method successfully reduces the similarity computation time while maintaining the same retrieval relevance as the conventional exhaustive search method. We also have observed that the method is more effective as the number of images and dimensions of the feature space increase.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.590-599
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• 2006

Disordered streams cause two issues in processing sliding windows: i) how to place input tuples into a buffer in an increasing order efficiently and ii) how to determine a time point to process the windows from input tuples in the buffer. To address these issues, we propose a structure and method of operators for processing sliding windows. We first present a structure of the operators using an index to handle input tuples efficiently. Then, we propose a method to determine the time point to process the windows, which is called a mean-based estimation. In the proposed method, users can describe parameters required for estimation in a query specification, which provides a way for users to control the properties of query results such as the accuracy or the response time according to application requirements. Our experimental results show that the mean-based estimation provides better adaptivity and stability than the one used in the existing method.

• Journal of Korea Spatial Information System Society
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• v.1 no.2 s.2
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• pp.137-149
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• 1999

Spatial database systems in client-server environment have network overload due to the large amount of spatial data transmission. Users use the window query that loads partial region of a whole map for quick response time in the environment. A series of window query such as screen movement, enlargement or shrinkage requires data in similar region and this increases network overload by re-transmitting the same data in intersected region with the earlier transmitted region. Removing the transmitted data from query results can solve this problem. In this paper, we design and implement a spatial object manager in order to remove the intersected region occurred by a series of window query. The spatial object manager manages the object identifiers of transmitted objects and removes transmitted objects from spatial objects of the query result by using the removal technique of the intersected region for the transmission and comparison. We utilize GEOMania Millennium server, an open client-server spatial database system, as spatial object manager in this paper. The result of the performance evaluation shows that the spatial object manager removes the transmission of the data redundancy, reduces network overload and improves the overall system performance.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1399-1408
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• 2004

Because there are so many spatio-temporal data in Moving Object Databases, a single disk system can not gain the fast response time and tota throughput. So it is needed to take a parallel processing system for the high effectiveness query process. In these existing parallel process-ing system. it does not consider characters of moving object data. Moving object data have to be thought about continuous report to the Moving Object Databases. So it is necessary think about the new Declustering System for the high performance system. In this paper, we propose the new Dechustering Policies of Moving objet data for high effectiveness query processing. At first, consider a spatial part of MBB(Minimum Bounding Box) then take a SD(SemiAllocation Disk) value. Second time, consider a SD value and time value which is node made at together as SDT-Proximity. And for more accuracy Declustering effect, consider a Load Balancing. Evaluation shows performance improvement of aver-age %15\%$ compare with Round-Robin method about $5\%\;and\;10\%$ query area. And performance improvement of average $6\%$ compare with Spatial Proximity method.