PPT-An Optimal Algorithm for Finding Heavy Hitters
Author : briana-ranney | Published Date : 2018-03-01
David Woodruff IBM Almaden Based on works with Vladimir Braverman Stephen R Chestnut Nikita Ivkin Jelani Nelson and Zhengyu Wang Streaming Model Stream of
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An Optimal Algorithm for Finding Heavy Hitters: Transcript
David Woodruff IBM Almaden Based on works with Vladimir Braverman Stephen R Chestnut Nikita Ivkin Jelani Nelson and Zhengyu Wang Streaming Model Stream of elements a 1 a. Yehuda. . Afek. Anat. . Bremler. -Barr. . Shir. Landau . Feibish. This work is part of the . Kabarnit. –Cyber Consortium (2012-2014) under Magnet program, funded by the chief scientist in the Israeli ministry of Industry, Trade and Labor. . Li . Tak. Sing(. 李德成. ). Lectures 20-22. 1. Section 4.3.3 Well-founded orders. Well-founded. A . poset. is said to be well-founded if every descending chain of elements is finite. In this case, the partial order is called a well-founded order.. Vladimir . Braverman. . (JHU). Stephen R. . Chestnut . (ETH). Nikita . Ivkin. . (JHU). David P. . Woodruff . (IBM). Streaming Model. Stream of elements a. 1. , …, a. m. . in . [n] = {1, …, n. David Woodruff . IBM . Almaden. Based on works with Vladimir . Braverman. , Stephen R. Chestnut Nikita . Ivkin. , Jelani Nelson, and . Zhengyu. Wang. Streaming Model. Stream of elements a. 1. , …, a. David Woodruff . IBM . Almaden. J. oint works with Arnab Bhattacharyya, Vladimir . Braverman. , Stephen R. Chestnut, . Palash. . Dey. Nikita . Ivkin. , Jelani Nelson, and . Zhengyu. Wang. Streaming Model. Evaluation. . Sequential: runtime (execution time). . Ts. =T (. InputSize. ). . Parallel: runtime (. s. tart-->last PE ends). . Tp. =T (. InputSize,p,architecture. ). . Note: Cannot be Evaluated in Isolation from the Parallel architecture. Vibhaalakshmi Sivaraman. Srinivas Narayana, Ori . RottenSTREICH. , MUTHU . MuthuKRSISHNAN. , JENNIFER REXFORD. 1. Heavy Hitter Flows. Flows above a certain threshold of total packets. “Top-. k. ” flows by size. Revere . .294. Span . .283. Doumit. . .275. Carroll . .268. Top Pitchers. Pitcher Wins Losses ERA. Diamond 12 9 3.54. Deduno. 6 5 4.44. Gray 6 1 5.71. Top Hitters. Player Average. Mauer. .. 319. Revere .. David Woodruff . IBM . Almaden. J. oint works with Arnab Bhattacharyya, Vladimir . Braverman. , Stephen R. Chestnut, . Palash. . Dey. Nikita . Ivkin. , Jelani Nelson, and . Zhengyu. Wang. Streaming Model. David Woodruff . IBM . Almaden. J. oint works with Arnab Bhattacharyya, Vladimir . Braverman. , Stephen R. Chestnut, . Palash. . Dey. Nikita . Ivkin. , Jelani Nelson, and . Zhengyu. Wang. Streaming Model. Greedy algorithms, coin changing problem. Haidong. . Xue. Summer 2012, at GSU. What is a greedy algorithm?. Greedy algorithm. : “an algorithm always makes the choice that looks best at the moment”. David Woodruff . IBM . Almaden. Based on works with Vladimir . Braverman. , Stephen R. Chestnut Nikita . Ivkin. , Jelani Nelson, and . Zhengyu. Wang. Streaming Model. Stream of elements a. 1. , …, a. Vibhaalakshmi Sivaraman. Srinivas Narayana, Ori . RottenSTREICH. , MUTHU . MuthuKRSISHNAN. , JENNIFER REXFORD. 1. Heavy Hitter Flows. Flows above a certain threshold of total packets. “Top-. k. ” flows by size. Why Social Graphs Are Different. Communities. Finding Triangles. Jeffrey D. Ullman. Stanford University/. Infolab. Social Graphs. Graphs can be either directed or undirected.. Example. : The Facebook “friends” graph (undirected)..
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