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Author Akioka, Sayaka
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Abstract Acceleration of huge data analysis, especially an analysis of huge, and fast streaming data is one of the major issues in recent computer science. Proper modeling, and understanding of streaming data analysis are indispensable for speed-up, scale out, and faster response time of streaming data analysis. Especially for the research on scheduling, or load balancing algorithms, a model of the target application truly impacts on the performance of the scheduling, or load balancing algorithms, however, there is no study on the realistic models, or the actual behaviors of streaming data analysis yet. This paper proposes a task graph for stream mining algorithms with some examples of actual applications. A task graph represents a workload of the target application with data dependencies, and control flows. This is the first proposal of task graphs for stream mining algorithms, and the task graphs play an important role as a benchmarking tool for the development of scheduling, or load balancing algorithms targeting on stream mining algorithms. 1.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article