• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.377-382
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• 2014

Analysis techniques of the data over time from the mobile environment and IoT, is mainly used for extracting patterns from the collected data, to find meaningful information. However, analytical methods existing, is based to be analyzed in a state where the data collection is complete, to reflect changes in time series data associated with the passage of time is difficult. In this paper, we introduce a method for analyzing multi-block streaming data(AM-MBSD: Analysis Method for Multi-Block Stream Data) for the analysis of the data stream with multiple properties, such as variability of pattern and large capacitive and continuity of data. The multi-block streaming data, define a plurality of blocks of data to be continuously generated, each block, by using the analysis method of the proposed method of analysis to extract meaningful patterns. The patterns that are extracted, generation time, frequency, were collected and consideration of such errors. Through analysis experiments using time series data.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.5_6
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• pp.238-248
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• 2007

As the demand of employing various PC software on mobile embedded devices which have limited storages has been increased, software streaming services are needed. However it takes too much time to launch software on them because it is transferred through wireless networks. To address this problem, prefetch algorithms are needed. We examined 'Last successor (LS)' algorithm and PPM-based prefetch algorithm as prefetch algorithms for software streaming services. We present 'Probability-base Multi Successor (PMS)' algorithm which is contrived through analyzing evaluations of previous algorithms and characteristics of software streaming services. While LS has one successor per each block, PMS has N successors based on probability which is calculated by PPM-based prefetch algorithm. The hit rate of PMS is similar to that of PPM-base prefetch algorithm and the space overhead is similar to that of LS. We can get good efficiency at the point of memory usage when PMS is applied to software streaming services.

• 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.