PPT-Scalable Mining of Massive Networks: Distance-based

Centrality Similarity and Influence Edith Cohen Tel Aviv University Graph Datasets Represent relations between things Bowtie structure of the Web Broder et al 2001 Dolphin interactions. Shuo Guo. , Ziguo Zhong and Tian He. University of Minnesota, Twin Cities. Background. Importance of fault detection in WSNs. Node failures => performance degradation. Two types of faults. Function fault. Course Introduction. Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . Distance-based Mining. Spyros Zoumpoulis. Joint work with Michalis Vlachos, Nick Freris and Claudio Lucchese. Mathematical & Computational Sciences. August 18, 2011. IBM ZRL. Problem. Want to distribute datasets, but maintain ownership rights. Joseph Jensen, Utah Valley University. John Blakeslee, Herzberg Astrophysics. July 4, 2016. . Understanding the systematic limitations of distance measurement techniques is key to solving many problems in cosmology and astrophysics.. (Part 1). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. (Part . 2). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. CS246: Mining Massive Datasets. Jure Leskovec, . Stanford University. http://cs246.stanford.edu. Recap: Finding similar documents. Task:. . Given a large number (. N. in the millions or billions) of documents, find “near duplicates”. 2). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. CS246: Mining Massive Datasets. Jure Leskovec, . Stanford University. http://cs246.stanford.edu. Recap: Finding similar documents. Task:. . Given a large number (. N. in the millions or billions) of documents, find “near duplicates”. Decision Trees on MapReduce CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu Decision Tree Learning Give one attribute (e.g., lifespan), try to predict the value of new people’s lifespans by means of some of the other available attribute Frequent Itemset Mining & Association Rules Mining of Massive Datasets Jure Leskovec, Anand Rajaraman , Jeff Ullman Stanford University http://www.mmds.org Note to other teachers and users of these Ranking Nodes on the Graph. Web pages are not equally “important”. www.joe-schmoe.com. vs. . www.stanford.edu. . Since there is large diversity . in the connectivity of the . web graph we can . Ashvin Goel. Electrical and Computer Engineering. University of Toronto. ECE 1724, Winter 2021. Web-Scale Apps. Applications that are . hosted in massive-scale . computing infrastructures . such as data centers.