PPT-Big Data, Stream Processing & Algorithms

Author : kittie-lecroy | Published Date : 2015-12-01

Supun Kamburugamuve For the PhD Qualifying Exam 12 19 2013 Advisory Committee Prof Geoffrey Fox Prof David Leake Prof Judy Qiu Outline Big Data Analytics

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Big Data, Stream Processing & Algorithms: Transcript

Supun Kamburugamuve For the PhD Qualifying Exam 12 19 2013 Advisory Committee Prof Geoffrey Fox Prof David Leake Prof Judy Qiu Outline Big Data Analytics Stack Stream Processing. Amitabha. Roy. Ivo . Mihailovic. Willy . Zwaenepoel. 1. Graphs. 2. + . HyperANF. Pagerank. ALS. ….. Interesting information is encoded as graphs. Big Graphs. Large graphs are a subset of the big data . Lesson 1. Bernhard Pfahringer. University of Waikato, New Zealand. 2. Or:. Why . YOU. should care about Stream Mining. Overview. 3. Why is stream mining important?. How is it different from batch ML?. Antonio . Gomes. 31 August 2017. Outline. Disruptive. . Innovation. Competition. and . Regulation. Challenges. to . C. ompetition. Agencies. Big. Data. Algorithms. . and. . collusion. Competition. Problem - a well defined task.. Sort a list of numbers.. Find a particular item in a list.. Find a winning chess move.. Algorithms. A series of precise steps, known to stop eventually, that solve a problem.. Arnav Agarwal. Srujun Thanmay Gupta. Shivam. . Bharuka. What is Stream Processing?. Infrastructure for continuous processing of data. Various applications for analysis of live data streams. Two types of operations:. Harvey et al., . 1996. Two Approaches to Characterize . Hyporheic. Exchange. Hydrometric approach. Sub-reach-scale . measurement. of hydraulic heads and hydraulic conductivity to compute streambed . Pipelining. Instruction Pipeline. Pipeline Hazards and their solution. Array and Vector Processing. Pipelining and Vector Processing. Parallel Processing. It refers to techniques that are used to provide simultaneous data processing.. Lecture . 10: . Sublinear. Algorithm. Zhu Han. University of Houston. Thanks for Professor Dan Wang’s slides. 1. outline. Motivations. Inequalities and classifications . Examples. Applications. 2. Keynote. BigDat. 2015: International Winter School on Big Data. Tarragona, Spain, January 26-30, 2015. January 26 2015. Geoffrey . Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. Lecture 17: Shortest Paths. Catie Baker. Spring . 2015. Announcements. Homework . 4 due next Wednesday, May . 13th. Spring 2015. 2. CSE373: Data Structures & Algorithms. Graph Traversals. For an arbitrary graph and a starting node . Lecture . 17: More . Dijkstra. ’s. and. Minimum Spanning Trees. Aaron Bauer. Winter 2014. Dijkstra’s. Algorithm: Idea. Winter 2014. 2. CSE373: Data Structures & Algorithms. Initially, start node has cost 0 and all other nodes have cost . Lecture . 18: Comparison Sorting. Dan Grossman. Fall 2013. Introduction to Sorting. Stacks. , queues, priority queues, and dictionaries . all . focused on providing one element at a time. But often we know we want “all the things” in some order. Start Here--- https://bit.ly/41cD43F ---Get complete detail on 301B exam guide to crack F5 Certified Technology Specialist - Local Traffic Manager (F5-CTS LTM). You can collect all information on 301B tutorial, practice test, books, study material, exam questions, and syllabus. Firm your knowledge on F5 Certified Technology Specialist - Local Traffic Manager (F5-CTS LTM) and get ready to crack 301B certification. Explore all information on 301B exam with number of questions, passing percentage and time duration to complete test. Operator State Management. with . Raul Castro Fernandez*. Matteo. . Migliavacca. +. and Peter . Pietzuch. *. *. Imperial College London, . +. Kent . Univerisity. Big data … . … in numbers: . 2.5 billions on gigabytes of data every day .

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