• Journal of KIISE:Computer Systems and Theory
• /
• v.27 no.6
• /
• pp.549-557
• /
• 2000

Distributed Memory Machine(DMM) is necessary for the effective computation of the data which is complicated and very large. Task scheduling is a method that reduces the communication time among tasks to reduce the total execution time of application program and is very important for the improvement of DMM. Task Duplicated based Scheduling(TDS) method improves execution time by reducing communication time of tasks. It uses clustering method which schedules tasks of the large communication time on the same processor. But there is a problem that cannot optimize communication time between task sending data and task receiving data. Hence, this paper proposes a new method which solves the above problem in TDS. Modified Task Duplicated based Scheduling(MTDS) method which can approximately optimize the communication time between task sending data and task receiving data by checking the optimal condition, resulted in the minimization of task execution time by reducing the communication time among tasks. Also system modeling shows that task execution time of MTDS is about 70% faster than that of TDS in the best case and the same as the result of TDS in the worst case. It proves that MTDS method is better than TDS method.

• English Language & Literature Teaching
• /
• v.11 no.4
• /
• pp.365-389
• /
• 2005

The purpose of this study is to present the task model that is the most effective in English language methodology based on the investigation of task-based performance in Korean EFL classroom practice. The subjects were 538 high school students and 126 high school teachers, each of whom had common experiences using the materials of task-based activities for more than one year. To analyze the data, the program SPSS WIN 11.0 including frequency distribution and chi-square analysis was used. The results of the questionnaire analysis showed that both teachers and students had a comparatively high level of satisfaction in task rationale, but that they had some mixed responses in the fields of input data, settings, and activity types. To conclude, a few suggestions are made to provide some meaningful considerations for the EFL teachers and material developers: a) task goals and rationale that encourage the learner's positive motivation; b) authenticity of input data based on the real-world context; c) collaborative learning environment that enhances communicative interaction; d) proportional representation of the creative problem-solving activities related to discussions and decision-making processes; e) systematic introduction of integrated language skills. It also suggests that the multi-lateral task model, which has some positive assets compared to previous task models, be newly introduced and applied to the second language learning classrooms.

• English Language & Literature Teaching
• /
• v.12 no.4
• /
• pp.253-276
• /
• 2006

The purpose of this study is to compare and analyze the aspects of task-based materials in high school English textbooks for first year students in Korea. Based on the theoretical backgrounds for designing communicative tasks and the basic contents of the 7th national curriculum for English, a total of six different qualitative evaluation categories of task-based materials are constructed. The six categories include input data, settings, activity types, language skills, activity themes, and communicative functions. The results of the data analysis showed that the regulations of the 7th national English curriculum, which were aimed at improving the students' communicative abilities, were properly reflected in the materials of task-based activities of all textbooks. On the other hand, a few problems were found in some textbooks: too many individual tasks; being out of proportion in presenting task types and themes; non-systematic introduction of language skills, etc. To conclude, a few suggestions are made to provide some meaningful considerations for the text material developers in order to produce better textbooks in the future: task goals and rationale that encourage the learner's positive motivation; authenticity of input data based on the real-world context; a collaborative learning environment that enhances communicative interaction; a proportional representation of the various activity types including creative problem-solving procedures; systematic introduction of integrated language skills, etc.

• Journal of Information Technology Applications and Management
• /
• v.21 no.2
• /
• pp.99-126
• /
• 2014

Tourism industry increasingly rely on information technology (IT) to improve the task performance. Many studies suggested that the evidence of IT showed not only task performance improvement but also organizational performance. Drawing from the resource-based view, technology and task fitness, and marketing orientation theories, this study proposes that IT use influences directly the task performance and proved the effectiveness of IT in the organizations of tourism industry. Further, the innovativeness, resources, and marketing orientation are identified as main determinants of IT use. The use of IT can serve as a catalyst in improving task performance for organizations in tourism industry. Based on data collected from surveying people who work in the tourism industry, the present study shed light on these issues. The findings provide a new perspective of IT effectiveness in the tourism industry. Then, we discussed the theoretical and practical implications.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.128-130
• /
• 2021

Smart cities collect data from thousands of IoT-based sensor devices for intelligent application-based services. Centralized cloud servers support application tasks with higher computation resources but introduce network latency. Fog layer-based data centers bring data processing at the edge, but fewer available computation resources and poor task allocation strategy prevent real-time data analysis. In this paper, tasks generated from devices are distributed as high resource and low resource intensity tasks. The novelty of this research lies in deploying a virtual node assigned to each cluster of IoT sensor machines serving a joint application. The node allocates tasks based on the task intensity to either cloud-computing or fog computing resources. The proposed Task Allocation Strategy provides seamless allocation of jobs based on process requirements.

• Journal of Computing Science and Engineering
• /
• v.10 no.1
• /
• pp.9-20
• /
• 2016

Several task clustering heuristics are proposed for allocating tasks in heterogeneous systems to achieve a good response time in data intensive jobs. However, one of the challenging problems is the process in task scheduling after task allocation by task clustering. We propose a task scheduling method after task clustering, leveraging worst schedule length (WSL) as an upper bound of the schedule length. In our proposed method, a task in a WSL sequence is scheduled preferentially to make the WSL smaller. Experimental results by simulation show that the response time is improved in several task clustering heuristics. In particular, our proposed scheduling method with the task clustering outperforms conventional list-based task scheduling methods.

• Journal of Information Processing Systems
• /
• v.17 no.3
• /
• pp.441-452
• /
• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2007.05a
• /
• pp.14-15
• /
• 2007

Now-a-days, data and task migration in collaborative work provides enormous facilities to users. Here, we propose an ontology-based information management scheme to facilitate data and task migration in collaborative work. This ontologybased model will help us to organize huge information (e.g. device status, runtime state etc.) efficiently.

• Journal of KIISE
• /
• v.43 no.6
• /
• pp.613-620
• /
• 2016

This paper presents a MOHA(Many-Task Computing on Hadoop) framework which aims to effectively apply the Many-Task Computing(MTC) technologies originally developed for high-performance processing of many tasks, to the existing Big Data processing platform Hadoop. We present basic concepts, motivation, preliminary results of PoC based on distributed message queue, and future research directions of MOHA. MTC applications may have relatively low I/O requirements per task. However, a very large number of tasks should be efficiently processed with potentially heavy inter-communications based on files. Therefore, MTC applications can show another pattern of data-intensive workloads compared to existing Hadoop applications, typically based on relatively large data block sizes. Through an effective convergence of MTC and Big Data technologies, we can introduce a new MOHA framework which can support the large-scale scientific applications along with the Hadoop ecosystem, which is evolving into a multi-application platform.

• Asia-Pacific Journal of Business
• /
• v.15 no.1
• /
• pp.113-129
• /
• 2024

Purpose - This study aims to empirically identify the quantitative effects of work from home (WFH) on employee productivity using field data. Design/methodology/approach - Based on large-scale field data from a South Korean company which introduced the WFH arrangement in 2020, we conducted fixed effect and moderating effect analyses using individual-level panel data over sixty-three weeks. Findings - The empirical analysis generated several findings. It was found that overall, WFH has a positive effect on productivity. However, the productivity impact of WFH was found to vary depending on task characteristics. Specifically, WFH led to over 20% increase in productivity for simple and repetitive tasks. On the other hand, no significant productivity impact was observed for professional and knowledge-based tasks. Research implications or Originality - As the first study based on field data from South Korea, this study offers convincing causal evidence of the moderating impact of task characteristics on the relationship between WFH and productivity. Further, the above findings provide managers with practical insights concerning their work arrangement decisions.