• Journal of Information Technology Services
• /
• v.7 no.1
• /
• pp.195-204
• /
• 2008

As the dependency to network system and demands of efficient storage systems rapidly grows in every networking filed, the current trends initiated by explosive networked data grow due to the wide-spread of internet multimedia data and internet requires a paradigm shift from computing-centric to data-centric in storage systems. Furthermore, the new environment of file systems such as SAN(Storage Area Network) is adopted to the existing storage paradigm for providing high availability and efficient data access. We describe the design issues and system components of $SANique^{TM}$, which is the cluster file system based on SAN environment. We, especially, present the comparative results of performance analysis for the intensive I/O test by using the DBMSs that are operated at the top of cluster file system $SANique^{TM}$, EXT3 and NFS respectively.

• Journal of information and communication convergence engineering
• /
• v.20 no.2
• /
• pp.96-102
• /
• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• The Bulletin of The Korean Astronomical Society
• /
• v.44 no.2
• /
• pp.39.2-39.2
• /
• 2019

Diffuse radio emissions at the outskirt of merging galaxy clusters called radio relics provide a unique channel to understand the merger history. We present a recent discovery of double radio relics in the cluster merger ZwCL1447+2619 from our recent Giant Metrewave Radio Telescope observations. Both Band 3 (300-500 MHz) and Band 4 (550-850 MHz) data reveal a large (~1Mpc) and thin (~40kpc) radio relic ~1Mpc from the cluster X-ray center and a small radio relic (~0.3 Mpc) on the opposite side. These remarkable radio data together with Subaru weak-lensing analysis and Chandra X-ray observations enable us to reconstruct the merger scenario. Our preliminary analysis suggests that the cluster ZwCL J1447+2619 is a post-merger near its returning phase. In addition, using Keck DEIMOS spectroscopy, we find many "green" and "blue" member galaxies are located between the radio relics, a possible indication of merger shock-driven star formation activities.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.2 no.4
• /
• pp.19-41
• /
• 2007

This research has a goal which is suggesting the way of constructing 'Cluster' which mean scheming the commencement of an enterprise in an early stage. Now it is reorganized into a IT industry structure 'Time-to market growth' is burst as a big issue. in that point, this research analyze the core success factor which is drawing from the existing IT industrial complex, and then it will be used to draw up to the 'Idealistic growth-support Cluster' on the basis of it, we pulled out various issues about the Corporate in the early stage of its growth. Therefore, this research is focused on presenting the ideal network(net) by considering the Network that organizations and business in Cluster or the network including the factors linked organizations and business in Cluster. therefore, this research carried out three big analysis. from the case investigation we pulled out the core growth factor, and then we approached the analysis of net structure for making application to Network Analysis. and then we analyzed that the characteristics of the Network after measuring by on the basis of analyzing core growth factor. and especailly, this research carried out the Core analysis for recognition of Core- support-frame by base Centrality Test on the net which is composed of growth support organizations at each Business. Judging from this, we can help to make full use of resources for the network analysis in Cluster and establish the Network Strategy by Structure comparison between the structure of industry-Cluster and ideal Business-support networks on the basis of the analysis from the Core-success-factor

• The Journal of Asian Finance, Economics and Business
• /
• v.6 no.4
• /
• pp.227-233
• /
• 2019

The study examines how business conditions, demand conditions and the role of government can influence the development of batik clusters in Pekalongan. This research is expected to be able to provide recommendations for both employers and local governments in order to help in optimizing the development of batik clusters. The research applied a quantitative research by engaging multiple regression analysis as an effort to understand the effect of the relationship between independent and dependent variables. In addition, this research was conducted in three largest batik clusters in Pekalongan, Indonesia namely batik cluster of Pasindon, Kauman, and Jenggot. These results indicate that business conditions positively affect the batik clusters development. It implies that the greater both business conditions in a cluster will lead the better the development. Indeed, the demand conditions also have an impact on the cluster development. This finding remarked that demand conditions are variable that need to be considered to development of batik cluster. Lastly, Government's role is confirmed that positively related to the Development of Batik Clusters. It implies that the more active the government's role in a cluster will have a good impact on the development of the cluster in certain area.

• Journal of Korean Clinical Nursing Research
• /
• v.20 no.1
• /
• pp.123-133
• /
• 2014

Purpose: The purpose of the study was to investigate symptom clusters experienced by patients on hemodialysis and to identify relations between symptom clusters and quality of life. Methods: Data were collected from 127 patients at 10 local hemodialysis clinics. Symptoms were measured using 10-item physical symptom checklist as well as the Hospital Anxiety depression Scale. Quality of life was measured with the Satisfaction with Life Scale. Data were analyzed using factor analysis, Pearson correlation, and stepwise multiple regression. Results: The most frequently reported symptoms included fatigue, itching, depression, numbness/tingling, and insomnia. Four distinct symptom clusters were identified: cluster 1 was comprised of dry mouth, headache, nausea (gastrointestinal); cluster 2 of decreased appetite, insomnia (basic need); cluster 3 of itching, numbness/tingling (sensory-comfort); and cluster 4 of fatigue, depression (mood-vitality). Among the clusters, the 'basic need' cluster and 'mood-vitality' cluster had a significant negative association with quality of life. The 'mood-vitality' cluster, explained 17.4% of the variance in quality of life. Conclusion: The results of the study indicate that comprehensive symptom assessment provides better symptom management for patients on hemodialysis. Further studies are needed to verify symptom clusters identified in this study.

• Journal of Korean Clinical Nursing Research
• /
• v.14 no.1
• /
• pp.61-70
• /
• 2008

Purpose: Women with gynecologic cancer often experience various physical and psychological symptoms relating to the cancer and its treatment. The purpose of this study was to identify symptom clusters. Method: A survey was conducted on 184 women with diagnoses of cervical, ovarian or endometrial cancer. Fifty symptoms were assessed for prevalence, severity and interference, and symptom clusters were identified. Cluster analysis was done using SPSS version 12.0. Results: Fatigue was identified as the most prevalent symptom (81.52%), lack of vaginal lubrication (2.26) as the most severe symptom, and lack of vaginal lubrication as the most interfering one (2.15). Identified six clusters were: Anorexia-pain cluster (loss of appetite, taste change, weight loss, appearance change, alopecia, weakness, pain), Fatigue cluster (lack of concentration, lack of memory, fatigue, dry mouth), Urinary-bowel distress cluster (urinary difficulty, constipation), Abdominal discomfort cluster (lower abdominal pain, abdominal pain, bloating), Emotional distress (sadness, anxiety-worry, nervousness, restlessness), and Menopausal cluster (sweating, hot flush, fever). Conclusion: The result of this study provides fundamental data to health care professionals in developing interventions for effective symptom management for women with gynecologic cancer by understanding identified 6 symptom clusters.

• Journal of Information Processing Systems
• /
• v.10 no.1
• /
• pp.36-54
• /
• 2014

Resource sharing is a major advantage of distributed computing. However, a distributed computing system may have some physical or virtual resource that may be accessible by a single process at a time. The mutual exclusion issue is to ensure that no more than one process at a time is allowed to access some shared resource. The article proposes a token-based mutual exclusion algorithm for the clustered mobile ad hoc networks (MANETs). The mechanism that is adapted to handle token passing at the inter-cluster level is different from that at the intra-cluster level. It makes our algorithm message efficient and thus suitable for MANETs. In the interest of efficiency, we implemented a centralized token passing scheme at the intra-cluster level. The centralized schemes are inherently failure prone. Thus, we have presented an intra-cluster token passing scheme that is able to tolerate a failure. In order to enhance reliability, we applied a distributed token circulation scheme at the inter-cluster level. More importantly, the message complexity of the proposed algorithm is independent of N, which is the total number of nodes in the system. Also, under a heavy load, it turns out to be inversely proportional to n, which is the (average) number of nodes per each cluster. We substantiated our claim with the correctness proof, complexity analysis, and simulation results. In the end, we present a simple approach to make our protocol fault tolerant.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.6
• /
• pp.29-35
• /
• 2021

In this paper, we propose a method that combines KNN(K-Nearest Neighbor), Local Map Classification and Bayes Filter as a way to increase the accuracy of location positioning. First, in this technique, Local Map Classification divides the actual map into several clusters, and then classifies the clusters by KNN. And posterior probability is calculated through the probability of each cluster acquired by Bayes Filter. With this posterior probability, the cluster where the robot is located is searched. For performance evaluation, the results of location positioning obtained by applying KNN, Local Map Classification, and Bayes Filter were analyzed. As a result of the analysis, it was confirmed that even if the RSSI signal changes, the location information is fixed to one cluster, and the accuracy of location positioning increases.

• Journal of Information Technology Applications and Management
• /
• v.26 no.1
• /
• pp.53-64
• /
• 2019

The purpose of this study is to find out the fast delivery route that several drones return a truck again after departing from it for delivery locations at each cluster while the truck goes through the cluster composed of several delivery locations. The main issue is to reduce the total delivery time composed of the delivery time by relatively slow trucks via clusters and the sum of maximum delivery times by relatively fast drones in each cluster. To solve this problem, we use a three-step heuristic approach. First, we cluster the nearby delivery locations with minimal number of clusters satisfying a constraint of drone flight distance to set delivery paths for drones in each cluster. Second, we set an optimal delivery route for a truck through centers of the clusters using the TSP model. Finally, we find out the moved centers of clusters while maintaining the delivery paths for the truck and drones and satisfying the constraint of drone flight. distance in the two-dimensional region to reduce the total delivery time. In order to analyze the effect of this study model according to the change of the number of delivery locations, we developed a R-based simulation prototype and compared the relative efficiency, and performed paired t-test between TSP model and the cluster-based models. This study showed its excellence through this experimentation.