PPT-Mining of Massive Datasets:

Course Introduction Mining of Massive Datasets Jure Leskovec Anand Rajaraman Jeff Ullman Stanford University httpwwwmmdsorg Note to other teachers and users of these slides We . SVD & CUR. 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. Latent Factor Models. 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:. MapReduce. Shannon Quinn. Today. Naïve . Bayes. with huge feature sets. i.e. ones that don’t fit in memory. Pros and cons of possible approaches. Traditional “DB” (actually, key-value store). MMDS . Secs. . 3.2-3.4. . Slides adapted from: . J. . Leskovec. , A. . Rajaraman. , J. Ullman: Mining of Massive Datasets, . http://www.mmds.org. October 2014. Task: Finding . Similar Documents. Goal:. Shannon . Quinn. (with thanks to William Cohen of . Carnegie Mellon and . Jure . Leskovec. of Stanford). “Big Data”. Astronomy. Sloan Digital Sky Survey. New Mexico, 2000. 140TB over 10 years. Large Synoptic Survey Telescope. (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. SVD & CUR. 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”. 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”. 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. 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 . Massive transfusion protocol (MTPs) . Established to provide rapid blood replacement in a setting of severe . hemorrhage. Early optimal blood transfusion is essential to sustain organ perfusion and oxygenation. Released October 2014 Table of ContentsIntroductionDevelopment of a Massive Transfusion Protocol: Engagement and ScopeTriggers for Initiating Massive TransfusionBlood Product Resuscitation in the Trau aspects and approaches. Fotis. E. . Psomopoulos. An EGI Virtual Team Project. As a field, bioinformatics relies heavily on public reference datasets and benefits from increasing compute capabilities to run algorithms.