Internet measurement and data analysis
2016 Spring Semester, Monday (14:45-16:15)
Faculty: Kenjiro Cho (kjc at sfc.keio.ac.jp)
TA: Yusaku Sawai (sawai at sfc.wide.ad.jp)
SA: Yuma Kurogome (gomachan at sfc.wide.ad.jp)
Class home page: http://web.sfc.keio.ac.jp/~kjc/classes/sfc2016s-measurement/
Class support mail (Faculty, TA and SA): imda2016s at sfc.wide.ad.jp
It becomes possible to access a huge amount of diverse data through
the Internet. It allows us to obtain new knowledge and create new
services, leading to an innovation called "Big Data" or "Collective
In order to understand such data and use it as a tool, one needs to
have a good understanding of the technical background in
statistics, machine learning, and computer network systems.
In this class, you will learn about the overview of large-scale data
analysis on the Internet, and basic skills to obtain new knowledge
from massive information for the forthcoming information society.
Theme, Goals, Methods
In this class, you will learn about data collection and data
analysis methods on the Internet, to obtain knowledge and
understanding of networking technologies and large-scale data analysis.
Each class will provide specific topics where you will learn the
technologies and the theories behind the technologies.
In addition to the lectures, each class includes programming exercises
to obtain data analysis skills through the exercises.
The lecture slide materials will be provided online.
 Mark Crovella and Balachander Krishnamurthy.
Internet measurement: infrastructure, traffic, and applications.
 Pang-Ning Tan, Michael Steinbach and Vipin Kumar.
Introduction to Data Mining.
Addison Wesley, 2006.
 Raj Jain.
The art of computer systems performance analysis.
 Toby Segaran.
Programming Collective Intelligence.
O'Reilly Media. 2007.
 Allen B. Downey.
Think Stats: Probability and Statistics for Programmers.
O'Reilly Media. 2011.
 Chris Sanders.
Practical Packet Analysis, 2nd Edition
No Starch Press. 2011.
2 assignments and a final report.
The prerequisites for the class are basic programming skills and basic
knowledge about statistics.
In the exercises and assignments, you will need to write programs to
process large data sets, using the Ruby scripting language and the
Gnuplot plotting tool.
To understand the theoretical aspects, you will need basic knowledge
about algebra and statistics. However, the focus of the class is to
understand how mathematics is used for engineering applications.
Class 1 Introduction (4/11)
Big Data and Collective Intelligence,
Large-scale data analysis,
exercise: introduction of Ruby scripting language,
exercise script(count.rb, count-rubyish.rb),
(optional reading material)
Class 2 Data and variability (4/18)
How to make good graphs,
exercise: graph plotting by Gnuplot,
exercise script(mean.rb, stddev.rb, stddev2.rb,
Class 3 Data recording and log analysis (4/25)
Network management tools,
Log analysis methods,
exercise: log data and regular expression,
Class 4 Distribution and confidence intervals (5/2)
Confidence intervals and statistical tests,
exercise: confidence intervals,
data for assignment 1 (honolulu2015.txt) (3.6MB)
Class 5 Diversity and complexity (5/9)
Web access and content distribution,
Power-law and complex systems,
exercise: power-law analysis,
Class 6 Correlation (5/16)
Online recommendation systems,
exercise: correlation analysis,
Class 7 Multivariate analysis (5/23)
Data sensing and GeoLocation,
Principal Component Analysis,
exercise: linear regression,
Class 8 Time-series analysis (6/6)
Internet and time,
Network Time Protocol,
Time series analysis,
exercise: time-series analysis,
data for assignment 2:
Class 9 Topology and graph (6/13)
exercise: shortest-path algorithm,
exercise script (dijkstra.rb),
exercise data (topology.txt,
Class 10 Anomaly detection and machine learning (6/20)
SPAM filtering and Bayes theorem,
exercise: naive Bayesian filter,
exercise data (naivebayes.rb)
Class 11 Data Mining (6/27)
exercise scripts (k-means.rb,
Class 12 Search and Ranking (7/4)
exercise: PageRank algorithm,
exercise scripts (pagerank.rb,
sample-links.txt, links-100k.txt (38MB)),
wikimedia pageview count dataset for the final report
Class 13 Scalable measurement and analysis (7/11)
Distributed parallel processing,
Cloud computing technology,
exercise: MapReduce algorithm,
exercise script (wc-map.rb, wc-reduce.rb, wc-data.txt)
$Date: 2016/07/08 06:40:33 $