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Advanced Algorithms for Massive Datasets

Advanced Algorithms for Massive Datasets. The power of “ failing ”. 2. TTT. Not perfectly true but. Opt k = 5.45. m / n = 8. We do have an explicit formula for the optimal k. Other advantage: no key storage. Crawling.

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Advanced Algorithms for Massive Datasets

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  1. Advanced Algorithmsfor Massive Datasets The power of “failing”

  2. 2 TTT

  3. Not perfectly true but...

  4. Opt k = 5.45... m/n = 8 We do have an explicit formula for the optimal k

  5. Other advantage: no key storage

  6. Crawling What data structures should we use to keep track of the visited URLs of a crawler? • URLs are long • Check should be very fast • No care about small errors (≈ page not crawled) Bloom Filter over crawled URLs

  7. Vj i i+z F Anti-virus detection D is a dictionary of virus-checksum of some given length z. For each position i, check… • Brute-force check: O( |D| * |F| ) time • Trie check: O( z * |F| ) time • Better Solution ? • Build a BF on D. • Check T[i,i+z-1] єD, if BF answers YES then “warn the user” or explicitly scan D O(k*|F|) or even better...

  8. Upper bounds

  9. Upper bounds

  10. Recurring minimum for improving the estimate + 2 SBF

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