• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.199-202
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• 2009

Adaptive memory management is a serious issue in data stream management. Data stream differ from the traditional stored relational model in several aspect such as the stream arrives online, high volume in size, skewed data distributions. Data skew is a common property of massive data streams. We propose the predicted allocation strategy, which uses predictive processing to cope with time varying data skew. This processing includes memory usage estimation and indexing with timestamp. Our experimental study shows that the predictive strategy reduces both required memory space and latency time for skewed data over varying time.

• Journal of KIISE
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• v.44 no.11
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• pp.1209-1218
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• 2017

A variety of sensors is widely used these days, and it has become much easier to acquire spatial big data streams from various sources. Since spatial data streams have inherently skewed and dynamically changing distributions, the system must effectively distribute the load among workers. Previous studies to solve this load imbalance problem are not directly applicable to processing spatial data. In this research, we propose Adaptive Spatial Key Grouping (ASKG). The main idea of ASKG is, by utilizing the previous distribution of the data streams, to adaptively suggest a new grouping scheme that evenly distributes the future load among workers. We evaluate the validity of the proposed algorithm in various environments, by conducting an experiment with real datasets while varying the number of workers, input rate, and processing overhead. Compared to two other alternative algorithms, ASKG improves the system performance in terms of load imbalance, throughput, and latency.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.100-102
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• 2001

Intensive I/O bandwidth demand of the multimedia streaming service puts significant burden on file system. Different from the legacy text based or image data, the semantics of the data in multimedia format can be significantly affected if the data block is not delivered by the predefined deadline. The legacy file system used in Unix or Unix like environment is designed to efficiently handle the files who sizes range from few hundreds of byte to several tens of gigabytes. This fundamental design philosophy results in the file system based on multi level skewed tree structure. Multi level i-node structure has significant drawback when the application performs sequential read operation. In this article, we present the result of the performance study of the file system which is specifically designed for handling multimedia streams. We implemented the file system on Linux Operating System environment and examines the performance behavior of the file system under streaming I/O workload. The result of the study shows that the proposed file system performs much more efficiently than the ext2 file system of Linux does.

• Journal of Korea Water Resources Association
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• v.37 no.12
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• pp.979-991
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• 2004

In this study, RMA2 and RMA4, the 2-D depth-averaged models, were employed to simulate the two-dimensional mixing characteristics of the pollutants in the natural streams. The velocity and depth were first calculated using RMA2, 2-D hydrodynamic model, and then the resulting flow field was inputted to RMA4, 2-D water quality model, to compute the concentration field. RMA models were verified using the velocity and concentration data measured in S-curved meandering channel. The results showed that the RMA2 model simulated well the phenomenon that the maximum velocity line is located at the Inner bank of meandering channel, and the RMA4 model was well adapted to reproduce the general mixing behavior and the separation of tracer clouds. Comparing model simulations with measured data in the field experiments, RMA2 model simulated well general flow field and tendency that the maximum velocity line skewed toward the outer bank which were found in field experiments. The simulations of RMA4 model showed that the center of the tracer cloud tends to follow the path in which the maximum velocity occurs. In this study, the dispersion coefficients are fine-tuned based on the measured coefficients calculated using field concentration data, and the results show reasonable agreement with predictive equations.