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Frequent Pattern Mining for Kernel Trace Data Christopher LaRosa, Li Xiong, Ken Mandelberg

Explore the use of low-impact kernel-level tracing tools for transparent reporting of system activity. This paper covers module breakdown, subsequence mining, and experimentation. Learn about window folding, slicing methods, and frequent pattern mining techniques.

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Frequent Pattern Mining for Kernel Trace Data Christopher LaRosa, Li Xiong, Ken Mandelberg

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  1. Frequent Pattern Mining for Kernel Trace DataChristopher LaRosa, Li Xiong, Ken Mandelberg SAC’08, March 16-20, 2008, Fortaleza, Ceará, Brazil.

  2. outline • Introduction • Module • Window Folding and Slicing • Subsequence mining • Experimentation

  3. Introduction • The introduction of low-impact kernel-level tracing tools allows for comprehensive and transparent reporting of process and operating system activity. • In Linux trace toolkit(LTT) • 達到human-readable

  4. Introduction(cont.) • LTT 可以配合其他工具的使用ex:trace time 可以分析系統performance LTT可以透過設置參數,在特定時段裡,篩選所需要記錄的事件類型,透過合理的參數配置,可使系統負擔減少一定的程度 LTT提供自定函數跟紀錄使他可以用來debug跟自己編寫內核代碼

  5. Module 1.find frequent itemset 2.Analysis pattern

  6. Module(cont.) • In Real world A and B can no order only C after B

  7. Module(cont.) 此篇合用兩個方法 • Window Folding • Window Slicing

  8. Window Folding

  9. Window Folding(con.)

  10. Window Folding(con.)

  11. Window Slicing • There are two ways to cut the long access stream into short sequences – overlapped cutting The overlapped cutting divides an entire access stream into many short sequences and leaves some overlapped regions between any two consecutive sequences .

  12. Window Slicing(con.) • non-overlapped cutting • 重疊的可以得到比較多的sequence但algorithm處理時間較長

  13. Window Slicing(con.) Stream abcabdabeabf into short sequences with length of 4 using overlapped cutting results in 5 short sequences is :

  14. Window Slicing(con.) Overlapping ab的sup=4 Non Overlapping ab的sup=3

  15. Window Slicing(con.) 不同於一般的 sequential pattern

  16. Subsequence mining 根據windows Slicing找到frequent Itemset 之後 再做事件判斷 W要多大?

  17. Experimentation

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