• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.193-202
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• 2024

As companies specialize, they use more modern applications, but they still rely on legacy systems and data access is limited by data silos. In this paper, we propose the Fabricator system, a design system for metadata based on Data Fabric that plays a key role in the data orchestration layer consisting of three layers: Sources Engine, Workload Builder, and Data Fabric Ingestion, thereby achieving meaningful integration of data and information. Provides useful insights to users through conversion. This allows businesses to efficiently access and utilize data, overcoming the limitations of legacy systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.574-577
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• 2007

While disk can be overwritten on updating data, because flash memory can not be overwritten on updating data, new data are updated in new area. If data are frequently updated, garbage collection, which is achieved by erasing blocks, should be performed to reclaim new area. Hence, because the number of erase operations is limited due to characteristics of flash memory, every block should be evenly written and erased. However, if data with access locality are processed by cost benefit algorithm with separation of hot block and cold block, though the performance of processing is high, wear-leveling is not even. In this paper, we propose CB-MB (Cost Benefit between Multi Bank) algorithm in which hot data are allocated in one bank and cold data in another bank, and in which role of hot bank and cold bank is exchanged every period. CB-MB showed that its performance was similar to that of others for uniform workload, however, the method provides much better performance than that of others for workload of access locality.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1067-1076
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• 2003

Database sharing system (DSS) refers to a system for high performance transaction processing. In DSS, the processing nodes are locally coupled via a high speed network and share a common database at the disk level. Each node has a local memory and a separate copy of operating system. To reduce the number of disk accesses, the node caches database pages in its local memory buffer. In this paper, we propose a dynamic transaction routing algorithm to balance the load of each node in the DSS. The proposed algorithm is novel in the sense that it can support node-specific locality of reference by utilizing the primary copy authority assigned to each node; hence, it can achieve better cache hit ratios and thus fewer disk I/Os. Furthermore, the proposed algorithm avoids a specific node being overloaded by considering the current workload of each node. To evaluate the performance of the proposed algorithm, we develop a simulation model of the DSS, and then analyze the simulation results. The results show that the proposed algorithm outperforms the existing algorithms in the transaction processing rate. Especially the proposed algorithm shows better performance when the number of concurrently executed transactions is high and the data page access patterns of the transactions are not equally distributed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.41-52
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• 2016

Prescription, pickup, and acting information from the ward are stored in a hospital database, and can be accessed and retrieved by the relevant departments. Frequent inquiries from many departments can cause a great load on the hospital information system. When the nursing and care services are integrated, the real-time pickup and acting tasks are also increased, which may lead to an increase in database inquiries, thereby increasing the amount of information being transferred. To effectively solve this inefficiency problem, we aimed to develop a nursing and care integrated information system that excludes database inquiries and incorporate a method that transmits pickup and acting information in real-time. Because the new system increases the workload and responsibility, we developed a ward acting dashboard so that every ward employee can determine all the acting situations of patients in real-time to improve the quality of services. We designed a database by concentrating on the pickup and acting business procedures, and applied real-time web techniques to enable pickup and acting information to be delivered instantly. Through our implementation, we were able to reduce the inquiry time and transmission amount significantly compared with the existing method.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.935-948
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• 2003

An online scaling method adds new nodes to the shared-nothing database cluster and makes tables be reorganized while the system is running. The objective is to share the workload with many nodes and increase the capacity of cluster systems. The existing online scaling method, however, has two problems. One is the degradation of response time and transactions throughput due to the additional overheads of data transfer and replica's condidtency. The other is and inefficient recovery mechanism in which the overall scaling transaction is aborted by a fault. These problems deteriorate the availability of shared-nothing database cluster. To avoid the additional overheads throughout the scaling period, our scalingmethod consists of twophases : a parallel data transfer phase and a combination phase. The parallel data transferred datausing reduces the size of data transfer by dividing the data into the number of replicas. The combination phase combines the transferred datausing resources of spare nodes. Also, our method reduces the possibility of failure throughout the scaling period and improves the availability of the database cluster.

• Journal of Computing Science and Engineering
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• v.5 no.2
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• pp.111-120
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• 2011

Data mining algorithms should exploit new hardware technologies to accelerate computations. Such goal is difficult to achieve in database management system (DBMS) due to its complex internal subsystems and because data mining numeric computations of large data sets are difficult to optimize. This paper explores taking advantage of existing multithreaded capabilities of multicore CPUs as well as caching in RAM memory to efficiently compute summaries of a large data set, a fundamental data mining problem. We introduce parallel algorithms working on multiple threads, which overcome the row aggregation processing bottleneck of accessing secondary storage, while maintaining linear time complexity with respect to data set size. Our proposal is based on a combination of table scans and parallel multithreaded processing among multiple cores in the CPU. We introduce several database-style and hardware-level optimizations: caching row blocks of the input table, managing available RAM memory, interleaving I/O and CPU processing, as well as tuning the number of working threads. We experimentally benchmark our algorithms with large data sets on a DBMS running on a computer with a multicore CPU. We show that our algorithms outperform existing DBMS mechanisms in computing aggregations of multidimensional data summaries, especially as dimensionality grows. Furthermore, we show that local memory allocation (RAM block size) does not have a significant impact when the thread management algorithm distributes the workload among a fixed number of threads. Our proposal is unique in the sense that we do not modify or require access to the DBMS source code, but instead, we extend the DBMS with analytic functionality by developing User-Defined Functions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.293-300
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• 2016

Traditional databases exploit a record-based model, where the attributes of a record are placed contiguously in a slow hard disk to achieve high performance. On the other hand, for read-intensive data analysis systems, the column-based compressed database has become a proper model because of its superior read performance. Currently, flash memory SSD is largely recognized as the preferred storage media for high-speed analysis systems. This paper introduces a compressed column-storage model and proposes a new index and its data management scheme for a high-capacity data warehouse system. The proposed index management scheme is based on the asymmetric index duplication and achieves superior search performance using the master index and compact index, particularly for large read-mostly databases. In addition, the data management scheme contributes to the read performance and high reliability by compressing the related columns and replicating them in two mirrored SSD. Based on the results of the performance evaluation under the high workload conditions, the data management scheme outperforms the traditional scheme in terms of the search throughput and response time.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.439-449
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• 2005

Distributed main memory database management systems (DMM-DBMSs) store the database in main memories of the participating sites. They provide high performance through fast access to the local databases and high speed communication among the sites. Recently, a lot of research results on DMM- DBMSs has been reported. However, to the best of our knowledge, there is no known research result on the performance of the catalog management schemes for DMM-DBMSs. In this work, we evaluated the performance of the partitioned catalog management schemes through experimental analysis. First, we classified the partitioned catalog management schemes into three categories : Partitioned Catalogs Without Caching (PCWC), Partitioned Catalogs With Incremental Caching (PCWIC), and Partitioned Catalogs With Full Caching (PCWFC). Experiments were conducted by varying the number of sites, the number of terminals per site, buffer size, write query ratio, and local query ratio. Experiments show that PCWFC outperforms the other two schemes in all cases. It also means that the performance of PCWIC gradually increases as time goes by. It should be noted that PCWFC does not guarantee high performance for disk-based distributed DBMSs in cases when the workload of individual site is high, catalog write ratio is high, or remote data objects are accessed very frequently. Main reason that PCWFC outperforms for DMM-DBMSs is that query compilation and remote catalog access can be done in a very high speed, even when the catalogs of the remote data objects are frequently updated.

• Journal of Navigation and Port Research
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• v.27 no.1
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• pp.19-24
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• 2003

This paper describes the computerization of medication management that is done manually now. That medication management effective and gives the enhanced way to provide information about seamen's medical condition check and the medicine habit. Transmission of detail information about medicine inventory through the wireless communication network is able to reduce the time required and workload. In this research, the server and client modules information between ships and shores.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.593-600
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• 2005

Recently, RFID system is a main technology to realize ubiquitous computing environments. Because RFID system that is an automatic identification technology using radio frequency is a system to read and write the data of the entity. Therefore, many companies are interested in RFID system to reduce supply chain management and inventory control cost. However, for providing secure service, RFID authentication technology secure against tracking by an adversary is researched first. In this paper, we proposed security level based RFID authentication protocol providing reduce computational and communicational workload in the back-end database. The proposed protocol is secure against reply attack, spoofing attack, traffic analysis, and location privacy, since the proposed protocol based on the security of the hash function.