The column-oriented database storage is a very advanced model for large-volume data analysis systems because of its superior I/O performance. Traditional data storages exploit row-oriented storage where the attributes of a record are placed contiguously in hard disk for fast write operations. However, for search-mostly datawarehouse systems, column-oriented storage has become a more proper model because of its superior read performance. Recently, solid state drive using MLC flash memory is largely recognized as the preferred storage media for high-speed data analysis systems. The features of non-volatility, low power consumption, and fast access time for read operations are sufficient grounds to support flash memory as major storage components of modern database servers. However, we need to improve traditional transaction management scheme due to the relatively slow characteristics of column compression and flash operation as compared to RAM memory. In this research, we propose a new scheme called Column-aware Multi-Version Locking (CaMVL) scheme for efficient transaction processing. CaMVL improves transaction performance by using compression lock and multi version reads for efficiently handling slow flash write/erase operation in lock management process. We also propose a simulation model to show the performance of CaMVL. Based on the results of the performance evaluation, we conclude that CaMVL scheme outperforms the traditional scheme.
The Journal of the Institute of Internet, Broadcasting and Communication
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v.20
no.5
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pp.107-112
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2020
Recently, unstructured data is rapidly being produced based on web-based services. NoSQL systems and key value stores that process unstructured data as key and value pairs are widely used in various applications. In this paper, a study was conducted on a skip list used for in-memory data management in an LSM-tree based key value store. The skip list used in the key value store is an insertion-based skip list that does not allow overwriting and processes all changes only by inserting. This behavior can support Multi-Version Concurrency Control (MVCC), which can simultaneously process multiple read/write requests through snapshot isolation. However, since duplicate keys exist in the skip list, the performance significantly degrades due to unnecessary node visits during a list traverse. In particular, serious overhead occurs when a range query or scan operation that collectively searches a specific range of data occurs. This paper proposes a newly designed Stride SkipList to reduce this overhead. The stride skip list additionally maintains an indexing pointer for the last node of the same key to avoid unnecessary node visits. The proposed scheme is implemented using RocksDB's in-memory component, and the performance evaluation shows that the performance of SCAN operation improves by up to 350 times compared to the existing skip list for various workloads.
Journal of the Korea Academia-Industrial cooperation Society
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v.22
no.4
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pp.580-586
/
2021
The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.
Directly converting the focal depth and image size of computer-generated-hologram (CGH), which is obtained by calculating the interference pattern of light from the 3D image, is known to be quite difficult because of the less similarity between the CGH and the original image. This paper proposes a method for separately converting the each of focal length of the given CGH, which is composed of multi-depth images. Firstly, the proposed technique converts the 3D image reproduced from the CGH into a Light-Field (LF) image composed of a set of 2D images observed from various angles, and the positions of the moving objects for each observed views are checked using an object detection algorithm YOLOv5 (You-Only-Look-Once-version-5). After that, by adjusting the positions of objects, the depth-transformed LF image and CGH are generated. Numerical simulations and experimental results show that the proposed technique can change the focal length within a range of about 3 cm without significant loss of the image quality when applied to the image which have original depth of 10 cm, with a spatial light modulator which has a pixel size of 3.6 ㎛ and a resolution of 3840⨯2160.
This study conducted an e-mail survey of office workers working in large companies in Seoul and Gyeonggi-do to verify the structural relationship between meaning in work, work-life balance, life satisfaction, and depression. The study participants were 329 male and 176 female workers, and a total of 505 ($\bar{M}=38.0$ yrs, $\bar{SD}=8.1$ yrs), and the measurement tools used the Korean version of work meaning scale, work-life balance scale, Korean version of life satisfaction scale, and shortened CES-D. After establishing a research model on the structural relationship between major variables, the measurement model and structural model were verified using the collected data, and the applicability of the model was reviewed through comparison according to group classification by gender and age group. As a result of the analysis, it was found that the work-life balance had a partial mediating effect on the effect of the meaning in work on the life satisfaction and depression of office workers. As a result of analyzing the multi-group structural model by age group, the mediating effect of work-life balance did not differ by age in the effect of meaning in work on life satisfaction, but the mediating effect of work-life balance on meaning in work on depression was different by age. These results suggest that the emphasis needs to vary depending on the target group in the practice of individual and group coaching and counseling.
The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.
Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.
In parallel cluster computing systems, the efficiency of communication between computing nodes is one of important factors that decide overall system performance. Accordingly, many researchers have studied on high-performance inter-node communication. The recently launched multi-core processor, however. increases the importance of intra-node communication as well because the more the number of cores in a node, the more the number of parallel processes running in the same node. Though there have been studies on intra-node communications, these have limited considerations on the state-of-the-art systems. In this paper, we propose a Linux kernel module that minimizes the number of data copy by exploiting the memory mapping mechanism for high-performance intra-node communication. The proposed kernel module supports the Linux kernel version 2.6. The performance measurements over a multi-core system present that the proposed kernel module can achieve lower latency up to 62% and higher throughput up to 144% than an existing kernel module approach. In addition, the measurements reveal that the performance of intra-node communication can vary significantly based on whether the cores that run the communication processes are belong to the same processor package (i.e., sharing the L2 cache).
Magaji, Bello Arkilla;Moy, Foong Ming;Roslani, April Camilla;Law, Chee Wei;Raduan, Farhana;Sagap, Ismail
Asian Pacific Journal of Cancer Prevention
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v.16
no.18
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pp.8101-8105
/
2016
Background: This study examined the psychometric properties of the Bahasa Malaysia (BM) version of the European Organization for Research and Treatment of Cancer (EORTC) Colorectal Cancer-specific Quality Of Life Questionnaire (QLQ-CR29). Materials and Methods: We studied 93 patients recruited from University Malaya and Universiti Kebangsaan Medical Centers, Kuala Lumpur, Malaysia using a self-administered method. Tools included QLQ-C30, QLQ-CR29 and Karnofsky Performance Scales (KPS). Statistical analyses included Cronbach's alpha, test-retest correlations, multi-traits scaling and known-groups comparisons. A p vaue ${\leq}0.05$ was considered significant. Results: The internal consistency coefficients for body image, urinary frequency, blood and mucus and stool frequency scales were acceptable (Cronbach's alpha ${\alpha}{\geq}0.65$). However, the coefficients were low for the blood and mucus and stool frequency scales in patients with a stoma bag (${\alpha}=0.46$). Test-retest correlation coefficients were moderate to high (range: r = 0.51 to 1.00) for most of the scales except anxiety, urinary frequency, buttock pain, hair loss, stoma care related problems, and dyspareunia (r ${\leq}0.49$). Convergent and discriminant validities were achieved in all scales. Patients with a stoma reported significantly higher symptoms of blood and mucus in the stool, flatulence, faecal incontinence, sore skin, and embarrassment due to the frequent need to change the stoma bag (p < 0.05) compared to patients without stoma. None of the scales distinguished between patients based on the KPS scores. There were no overlaps between scales in the QLQ-C30 and QLQ-CR29 (r < 0.40). Conclusions: the BM version of the QLQ-CR29 indicated acceptable psychometric properties in most of the scales similar to original validation study. This questionnaire could be used to complement the QLQ-C30 in assessing HRQOL among BM speaking population with colorectal cancer.
Background: Breast cancer is the leading cause of cancer-related death among women in Malaysia. A diagnosis is very stressful for women, affecting all aspects of their being and quality of life. As such, there is little information on quality of life of women with breast cancer across the different ethnic groups in Malaysia. The purpose of this study was to examine the quality of life in Malay and Chinese women newly diagnosed with breast cancer in Kelantan. Materials and Methods: A descriptive study involved 58 Malays and 15 Chinese women newly diagnosed with breast cancer prior to treatment. Quality of life was measured using the Malay version of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and its breast-specific module (QLQ-BR23). Socio-demographic and clinical data were also collected. All the data were analyzed using SPSS version 20.0. Results: Most of the women were married with at least a secondary education and were in late stages of breast cancer. The Malay women had lower incomes (p=0.046) and more children (p=0.001) when compared to the Chinese women. Generally, both the Malay and Chinese women had good functioning quality-of-life scores [mean score range: 60.3-84.8 (Malays); 65.0-91.1 (Chinese)] and global quality of life [mean score 60.3, SD 22.2 (Malays); mean score 65.0, SD 26.6 (Chinese)]. The Malay women experienced more symptoms such as nausea and vomiting (p=0.002), dyspnoea (p=0.004), constipation (p<0.001) and breast-specific symptoms (p=0.041) when compared to the Chinese. Conclusions: Quality of life was satisfactory in both Malays and Chinese women newly diagnosed with breast cancer in Kelantan. However, Malay women had a lower quality of life due to high general as well as breast-specific symptoms. This study finding underlined the importance of measuring quality of life in the newly diagnosed breast cancer patient, as it will provide a broader picture on how a cancer diagnosis impacts multi-ethnic patients. Once health care professionals understand this, they might then be able to determine how to best support and improve the quality of life of these women during the difficult times of their disease and on-going cancer treatments.
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