Lecture 22: Laplacian Mesh Editing
COMPSCI/MATH 290-04
Chris Tralie, Duke University
4/5/2016
COMPSCI/MATH 290-04
Lecture 22: Laplacian Mesh Editing
Announcements
B First project milestone Monday 4/11/2016
B First milestone 20%
B Group Assignment 3 Out Th
CS290-01: How to Submit Assignments
Junghoon Kang
1
Intro
In this course, we will be using Bitbucket to submit assignments.
2
Create Bitbucket Account
Bitbucket is a web-based hosting service for projects that use Git revision control system.
You can crea
How to Run Spark Application
Junghoon Kang
Contents
1 Intro
2
2 How to Install Spark on a Local Machine?
2.1 On Ubuntu 14.04 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
2
3 How to Run Spark Application on a Local Machine?
3.1
Data Engineering
Query Optimization (Costbased optimization)
Shivnath Babu
Query Optimization Problem
Pick the best plan from the space of
physical plans
Cost-Based Optimization
Prune the space of plans using heuristics
Estimate cost for remaining plans
Data Engineering
How MapReduce Works
Shivnath Babu
Lifecycle of a MapReduce Job
Map function
Reduce function
Run this program as a
MapReduce job
Lifecycle of a MapReduce Job
Map function
Reduce function
Run this program as a
MapReduce job
Lifecycle of a M
Data Engineering
Introduction to Parallel Execution
Shivnath Babu
Introduction to Parallel Execution
Query or
Spark Program Q
Answers
Spark
Driver
Data resides
on one or
more
Data Partition
machines
Data Partition
Translates Q into
an execution plan
and r
MapReduce Algorithms
2009 Cloudera, Inc.
Algorithms for MapReduce
Sorting
Searching
Indexing
Classification
Joining
TF-IDF
2009 Cloudera, Inc.
MapReduce Jobs
Tend to be very short, code-wise
IdentityReducer is very common
Utility jobs can be composed
Data Engineering
SQL Query Processing
Shivnath Babu
SQL! Declarative Big Data Processing
:
Let Developers Create and Run Spark Programs
Faster:
Write less code
Read less data
Let the optimizer do the hard work
2
Write Less Code: Compute an Average
priv
Lecture 25: Geodesic Paths
COMPSCI/MATH 290-04
Chris Tralie, Duke University
4/14/2016
COMPSCI/MATH 290-04
Lecture 25: Geodesic Paths
Announcements
B Group Assignment 3 Out: First Deadline Monday 4/18.
Final Deadline Tuesday 4/26
B Final Project Final Dea
Lecture 26: MDS / Canonical Forms
COMPSCI/MATH 290-04
Chris Tralie, Duke University
4/19/2016
COMPSCI/MATH 290-04
Lecture 26: MDS / Canonical Forms
Announcements
B Group Assignment 3 Final Deadline Tuesday 4/26
B Guest Lecture Thursday
B No office hours T
Lecture 24: Heat Flow
COMPSCI/MATH 290-04
Chris Tralie, Duke University
4/12/2016
COMPSCI/MATH 290-04
Lecture 24: Heat Flow
Announcements
B Group Assignment 3 Out: First Deadline Monday 4/18.
Final Deadline Wednesday 4/27 (Sakai says 4/26 but thats
wrong.
Lecture 13
Nonlinear Systems - Newtons Method
An Example
The LORAN (LOng RAnge Navigation) system calculates the position of a boat at sea using signals from
fixed transmitters. From the time differences of the incoming signals, the boat obtains differenc
Lecture 15: High Dimensional Data Analysis,
Numpy Overview
COMPSCI/MATH 290-04
Chris Tralie, Duke University
3/3/2016
COMPSCI/MATH 290-04
Lecture 15: High Dimensional Data Analysis, Numpy Overview
Announcements
B Mini Assignment 3 Out Tomorrow, due next F
Lecture 14: Shape Google: Rigid Shape
Statistics
COMPSCI/MATH 290-04
Chris Tralie, Duke University
3/1/2016
COMPSCI/MATH 290-04
Lecture 14: Shape Google: Rigid Shape Statistics
Announcements
B Group Assignment 1 Full Submission Due Tomorrow
(Wednesday) 11
Lecture 23: Spectral Meshes
COMPSCI/MATH 290-04
Chris Tralie, Duke University
4/7/2016
COMPSCI/MATH 290-04
Lecture 23: Spectral Meshes
Announcements
B First project milestone Monday 4/11/2016
B Final Project Rubric Up
B Group Assignment 3 Out Tomorrow or
Lecture 13: Procrustes, ICP
COMPSCI/MATH 290-04
Chris Tralie, Duke University
2/25/2016
COMPSCI/MATH 290-04
Lecture 13: Procrustes, ICP
Announcements
B Group Assignment 1 Full Submission Due next Tuesday
11:55 PM
B Hackathon Saturday 2/27 4:00 PM - 10:00
Data Engineering
Solutions
Problem 1
1 The plans are equivalent.
2 The plans are inequivalent. If S or T has a duplicate value of A that will match
a record in R, then the nal result will also have duplicate values of A, which will never
happen in plan (a