• Journal of the Ergonomics Society of Korea
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• v.33 no.6
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• pp.465-475
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• 2014

Objective: The aim of this study is to investigate the effect of workload on musculoskeletal degeneration. Background: It is important that workers maintain a health body for a long time as a measure of aging workers. In order do so, it is necessary to find out if workload can effect on musculoskeletal degeneration. Method: Epidemiological studies on workload or occupation and disc degeneration, osteoarthritis of knee were identified through database and bibliography searches. Results: Epidemiological studies showed that workload certainly effect on musculoskeletal degeneration. But we couldn't know which of individual factors and occupational factors further contribute to the musculoskeletal degeneration. And we could find that studies on workload and genetic factors were very few. In addition, there was also very few studies that it was possible interaction between individual factors as obesity and occupational factors as heavy manual lifting. Conclusion: Our reviews suggest that it need to conduct study between workload and genetic factors for musculoskeletal degeneration. This further study can identify attributable risk of workload for musculoskeletal degeneration, and contribute to the measure of aging workers. Application: This paper can help to establish research plan for the measure of aging workers.

• The Journal of Information Technology and Database
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• v.7 no.2
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• pp.47-59
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• 2000

In this paper, we analyze the applicability of a general optimization algorithm to a database optimization problem. The index selection problem Is the problem to choose a set of indexes for a database in a way that the cost to process queries in the given workload is minimized subject to a given storage space restriction for storing indexes. The problem is well known in database research fields, and many optimization and/or heuristic algorithms have been proposed. Our work differs from previous research in that we formalize the problem in the form of non-linear Integer Programming model, and investigate the feasibility and applicability of a general purpose optimization algorithm, called OPBDP, through experiments. We implemented algorithms to generate workload data sets and problem instances for the experiment. The OPBDP algorithm, which is a non-linear 0-1 Integer Programming problem solver based on Davis-Putnam method, worked generally well for our problem formulation. The experiment result showed various performance characteristics depending on the types of decision variables, variable navigation methods and ocher algorithm parameters, and indicates the need of further study on the exploitation of the general purpose optimization techniques for the optimization problems in database area.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1107-1117
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• 2006

In this paper, a load balancing method using ring network structure in the Grid database is proposed. In the Grid database, generally, data is replicated for performance and availability. And, user's request is transferred to node and processed in that node which has target data. But, in such environment, a decline of performance can be occurred because unbalanced workload. A traditional research is proposed to solve unbalanced load problem. However, the Grid database has a number of systems and user's request always changes dynamically. Therefore, a traditional research can not be applied. The proposed method connects each node which has a same replicated data through ing network structure. If workload is overflowed in some node, user's request is transferred to a linked node which has a target data. And, this node stops another request processing until workload is decreased. Then, it changes the link structure through sending a message to a previous node, to stop request forwarding from a previous node. This paper shows a proposed method increases performance than existing research through performance evaluation and is more suitable for a complex and dynamic environment.

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.183-190
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• 2006

Database workloads may show different resource usages for database applications. Database administrators can enhance the DBMS performances through resource management that reflects workload characteristics. We provide a method that can identify tunable resources from analyzing the relationship between performance indicators and resources. First, we select which performance indicators increase or decrease by expanding resources using a correlation coefficient and a significance level test. Next, we identify resources that can affect the DBMS Performances by using increasing or decreasing performance indicators. We evaluated our method in the TPC-C and TPC-W environments.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.747-754
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• 2004

Database administrators should be aware of performance characteristics of database systems in order to manage database system effectively. The usages of system resources in database systems could be quite different under database workloads. The objective of this paper is to identify and analyze performance characteristics of database systems in different workloads, which could help database tuners tune database systems Under the TPC-C and TPC-W workloads, which represent typical workloads of online transaction processing and electronic commerce respectively, we investigated usage types of resource that are determined by fourteen performance indicator, and are behaved in response to changes of four tuning parameters (data buffer, private memory, I/O process, shared memory). Eight out of the fourteen performance indicators cleary show the performance differences under the workloads. Changes of data buffer parameter give a influences to database system. The tuning parameter that affects the system performance significantly is the database buffer size in the both workloads.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.43-50
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• 2020

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.198-208
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• 2021

ArangoDB is a NoSQL database system that has been popularly utilized in many applications for storing large amounts of data. In order to apply a new NoSQL database system such as ArangoDB, to real work environments we need a benchmarking system that can evaluate its performance. In this paper, we design and implement a ArangoDB based benchmarking system that measures a kernel level performance well as an application level performance. We partially modify YCSB to measure the performance of a NoSQL database system in the cluster environment. We also define three real-world workload types by analyzing the existing materials. We prove the feasibility of the proposed system through the benchmarking of three workload types. We derive available workloads in ArangoDB and show that performance at the kernel layer as well as the application layer can be visualized through benchmarking of three workload types. It is expected that applicability and risk reviews will be possible through benchmarking of this system in environments that need to transfer data from the existing database engine to ArangoDB.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.111-116
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• 2023

The WiscKey database, which optimizes overhead by compaction of the LSM tree-based Key-Value database, stores the value in a separate file, and stores only the key and value addresses in the database. Each time an fsync system call function is used to ensure data integrity in the process of storing values. In previously conducted studies, workload performance was reduced by up to 5.8 times as a result of performing the workload without calling the fsync system call function. However, it is difficult to ensure the data integrity of the database without using the fsync system call function. In this paper, to reduce the overhead of the fsync system call function while performing workloads on the WiscKey database, we use NVDIMM caching techniques to ensure data integrity while improving the performance of the WiscKey database.

• IE interfaces
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• v.10 no.1
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• pp.145-154
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• 1997

In developing the HMI(Human-Machine Interface) evaluation system for the IVNS(In-Vehicle Navigation System), design guidelines and evaluation methods are the most crucial problems for its use and efficiency. As the part of this system, focused on the final product of the database, subjective mental workload assessment is seriously considered to evaluate the driver's own driving task using the IVNS. This paper suggests the methodology for the ergonomic assessment of the IVNS that corresponds to the subjective measurement of the driver's mental workload by rating his or her own driving task. For this approach, Revision of NASA-Task Load Index(RNASA-TLX) was developed which translated and revised the version of NASA-TLX that is generally accepted an efficient and powerful method for evaluating the in-vehicle information systems. To verify the RNASA-TLX, an experiment was conducted in a real road situation, because the result of the laboratory approach is uncertain and has the differences from the real road test.

• Journal of Korea Multimedia Society
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• v.11 no.7
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• pp.914-927
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• 2008

Grid database management systems are used for large data processing, high availability and data integration in grid computing. Furthermore the grid database management systems are in the use of manipulating the queries that are sent to distributed nodes for efficient query processing. However, when the query processing is concentrated in a random node, it will be occurred with imbalance workload and decreased query processing. In this paper we propose a load balancing method for query processing based on cache Management in grid databases. This proposed method focuses on managing a cache in nodes by cache manager. The cache manager connects a node to area group and then the cache manager maintains a cached meta information in node. A node is used for caching the efficient meta information which is propagated to other node using cache manager. The workload of node is distributed by using caching meta information of node. This paper shows that there is an obvious improvement compared with existing methods, through adopting the proposed algorithm.