Visualization Lifecycle

Visualization
Lifecycle

datainsight
San Francisco 2011
Raffael Marty

“Transform a dataset into a captive story.”

‣ Assess Youʼre on your own Art
‣ Parse

‣ Clean

‣ Visualize

Visualization Tools and Libraries

pixlcloud | collect. visualize. understand. Copyright (c) 2011

Audience
Expert

Fun

Technical Overview

Boring

Beginner


Visualization Process
Contextual Data

iterations

Data Sources (Data Store) Structured Data Visual Representation

visualization

parsing
feature selection

ﬁles
database
ﬁltering
aggregation
cleansing


Data Sources
‣ File XML, JSON, CSV, TSV

‣Database mysql -u root -p mydatabase < dump.sql

‣ API
curl ‘http://freebase.com/api/service/
‣Factual search?query=al+gore&indent=1’

‣Freebase

‣Infochimps

‣OpenStreetMap


Explore Data
‣ What is the data about?
‣ What are the data features/columns?
‣ Is there a common structure in the data?
‣ What are the data types?
Nov 7 09:14:46 fwbox kernel: DROPPED IN=eth0 OUT= MAC=00:0c:29:e3:45:bd:00:0c:
29:b5:5c:ee:08:00 SRC=10.1.222.31 DST=10.1.222.202 LEN=60 TOS=0x00 PREC=0x00
TTL=64 ID=63849 DF PROTO=TCP SPT=58485 DPT=9111 WINDOW=5840 RES=0x00 SYN URGP=0

May 25 20:24:20 ram-laptop kernel: BLOCK any in: IN=eth1 OUT=
MAC=00:13:02:ac:d8:ea:00:09:5b:3d:df:00:08:00 SRC=213.175.90.24 DST=192.168.0.15
LEN=576 TOS=0x00 PREC=0x00 TTL=115 ID=23513 PROTO=TCP SPT=9030 DPT=56772
WINDOW=65535 RES=0x00 ACK URGP=0


Parsing and Normalization
‣ Parsing
‣ extraction of entities / features

‣ imposing structure
Oct 13 20:00:43.874401 rule 193/0(match): block in on xl0:
212.251.89.126.3859 >: S 1818630320:1818630320(0) win 65535 <mss
1460,nop,nop,sackOK> (DF)

‣ often use regexes Oct 13 20:00:43 fwbox local4:warn|warning fw07 %PIX-4-106023: Deny tcp
src internet: 212.251.89.126/3859 dst 212.254.110.98/135 by access-
group "internet_access_in"

‣ Normalize Oct 13 20:00:43 fwbox kernel: DROPPED IN=eth0 OUT=
MAC=ff:ff:ff:ff:ff:ff:00:0f:cc:81:40:94:08:00 SRC=212.251.89.126
DST=212.254.110.98 LEN=576 TOS=0x00 PREC=0x00 TTL=255 ID=8624
PROTO=TCP SPT=3859 DPT=135 LEN=556
‣ field normalization

‣ term normalization: block, deny, dropped

‣ Generate a common output format for vis-tools (e.g., CSV)


Parser
Oct 13 20:00:38.018152 rule 57/0(match): pass in on xl1: 195.141.69.45.1030 > 62.2.32.250.53: 34388 [1au][|domain] (DF)

Raw Oct 13 20:00:38.115862 rule 57/0(match): pass in on xl1: 195.141.69.45.1030 > 192.134.0.49.53: 49962 [1au][|domain] (DF)

Oct 13 20:00:38.157238 rule 57/0(match): pass in on xl1: 195.141.69.45.1030 > 194.25.2.133.53: 14434 [1au][|domain] (DF)

(.*) rule ([-d]+/d+)(.*?): (pass|block) (in|out) on (w+):
(d+.d+.d+.d+).?(d*) [<>]
Regex / Parser (d+.d+.d+.d+).?(d*): (.*)

Oct 13 20:00:38.018152,57/0,match,pass,in,xl1,195.141.69.45,1030,62.2.32.250,53,34388 [1au][|domain] (DF)
Normalized Oct 13 20:00:38.115862,57/0,match,pass,in,xl1,195.141.69.45,1030,192.134.0.49,53,49962 [1au][|domain] (DF)
(CSV) Oct 13 20:00:38.157238,57/0,match,pass,in,xl1,195.141.69.45,1030,194.25.2.133,53,14434 [1au][|domain] (DF)


UNIX Tools
‣ grep
‣cat file | grep –v “foo”

‣ awk
‣awk –F, ‘{printf(“%s,%sn”,$2,$1);}’

‣awk -F, -v OFS=, ‘{print $2,$1}’

‣ sed
‣sed -e 's/fubar/foobar/g' filename


Regular Expression Resources
‣ http://regexlib.com
‣ http://www.regular-expressions.info
‣ http://gskinner.com/RegExr


Data Cleansing
‣ Filter

‣ Normalize (see earlier)

‣ Aggregation


Load CSV into Database
# mysql -u <user> -p Sometimes you just load
your data into a tool,
and you can omit this
mysql> create database data; step

mysql> create table set1 (id int, address
varchar(20), ...);
mysql> LOAD DATA LOCAL INFILE 'input_file' INTO
TABLE set1 FIELDS TERMINATED BY ',' LINES
TERMINATED BY 'n';


Contextual Data
‣ Either dump into DB or use via API calls to augment

‣ IP -> Geo mapping
‣ Information about countries
‣ Port number -> service name


Feature Selection
‣ What are the fields you are interested in?
‣ Compute new fields
‣start time, end time -> duration

‣IP subnets [ 10.2.4.2 -> 10.0.0.0/8 or 192.168.1.2 -> 192.168.1.0/24 ]
‣ Entropy: H ( X ) = E ( I ( X ) )

‣ Dimensionality reduction
‣See Bryan’s talk!


Choose Your Poison


Ode to the Pie


A Good Visual
‣ Chose the right graph ‣ Simultaneous views

‣ Reduce non-data ink ‣ Interactivity


Visual Transformations
‣ keep iterating on visual transformations, change
‣color

‣shape

‣features display

‣ add new fields?
‣ add more context?
‣ is the output expressive?
‣ capture output and prettify it for presentation

Data Visualization Tools
and Libraries

Tools and Libraries
‣ http://datainsightsf.com/resources/
‣Choose what’s appropriate!

‣ Data Analysis and Visualization LInuX
‣davix.secviz.org

‣ GraphViz
‣graphviz.org

‣ AfterGlow (CSV -> DOT)
‣afterglow.sf.net


Libraries
‣ Reporting Libraries ‣Visualization Libraries
‣HighCharts ‣TheJIT
‣Flot ‣Graphael
‣Google Chart API ‣Protovis
‣Open Flash Chart ‣ProcessingJS
‣JQuery Sparklines ‣Flare
‣Polymaps ‣D3

-


HighCharts

‣ Click-Through

‣ On load
‣near real-time updates

‣ Zoom
www.highcharts.com


Google Visualization API

http://code.google.com/apis/visualization/interactive_charts.html

‣ JavaScript

‣ Based on DataTables()
‣ Many graphs
‣ Playground
‣ http://code.google.com/apis/ajax/playground


ProtoVis
‣ JavaScript based visualization library
‣ Charting

‣ Treemaps

‣ BoxPlots

‣ Parallel Coordinates
‣ etc.

http://vis.stanford.edu/protovis/

TheJIT http://thejit.org/

‣ JavaScript InfoVis Toolkit
‣ Interactive

‣ Link Graphs


Processing
‣ Visualization library
‣ Java based
‣ Interactive (event handling)
‣ Number of libraries to
‣ draw in OpenGL
‣ read XML ﬁles
‣ Processing JS
‣ JavaScript
‣ HTML 5 Canvas
‣ WebGL http://processingjs.org/
‣ Web IDE http://processing.org/


Visualization Tools
‣ Gephi

‣R

‣ Matlab

‣ Mondrian

‣ PicViz

‣ Treemap 4.1
‣ Google Earth

Gephi http://gephi.org

‣ reads: CSV, DOT, etc.
‣ graph analysis algorithms
‣ highly interactive


PicViz

http://www.wallinﬁre.net/picviz/


Treemap 4.1

http://www.cs.umd.edu/hcil/treemap/

Google Earth
• KML data format for
encoding data


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collect. visualize. understand.

@raffaelmarty

Visualization Lifecycle

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