Dr. Dietmar Janetzko
National College of Ireland
Mayor Street, IFSC, Dublin
Telephone +353 1 4498-610
Fax: +353 1 406 0559
Mobile +353 8640 82891
E-Mail: dietmarja@gmail.com
Dietmar Janetzko
Non-reactive Data Collection
on the Internet
Outline
•
The Concept of Non Reactive Data Collection
•
The Technical Perspective
•
The Methodological Perspective
•
Thin and Rich Descriptions
•
Two ways to deal with thin descriptions: Horizontal and vertical
enlargement of data sets
•
•
2
Extensions & Recent Developments
•
The Enron Data Set (E-Mail)
•
The AOL Data Set (Search Requests)
•
Collection all Data about a Person
Discussion
The Concept of Non
Reactive Data Collection
3
The Concept of Non
Reactive Data Collection
• Non reactive data collection is conducted in a
naturalistic setting in such a way that persons
studied are not aware of it.
• Thus, non-reactivity is not a characteristic of the
data or the data collection procedure per se, but of
the awareness of the persons (not) studied.
• Three kinds of non reactive data:
• Environmental (PhysicaL) Traces
• Simple Observations
• Archival Sources (Frankfort-Nachmias & Nachmias, 2000).
4
Motivation for Non Reactive
Data Collection on the Internet
A) Why non reactive?
B) Why using the Internet/Internet technologies?
C) What are the limits of using non reactive data
collected on the Internet?
5
Why non reactive?
• Often, a reactive equivalent to NRD does not
exist. It would be cumbersome to develop or it
would severely interfere with the phenomena
studied.
• The phenomenon of interest would be distorted
or disappear if studied in a reactive way
Example: Studying dating on the Internet via
reactive measures would defeat its purpose
and/or would be open to criticism that an
unsuitable method has been used.
6
Why using the Internet/
Internet Technologies?
• Today, many social phenomena (e.g., communication in
organisations) unfold especially or even exclusively via the
Internet.
• NRD collected on the Internet highlight behavioral & social
phenomena, but is also indispensable for organising online
research.
Example: Using cookies, IP-Addresses or time stamps to
control if persons participate several times in an online study
• Data collection
7
–
–
–
–
–
is relatively simple
not limited to a fixed area/time
may yield Data may be collected in large quantities
can be done in an automated and objective way
may cover “sub-symbolic information” (e.g., hesitations to make a
decision, Hofmann, Reed, & Holz, 2006)
Limits of Non Reactive Data
Collection on the Internet
• Many techniques used for NRDCI have not been designed for
online research studies in the first place.
Example: Log files have been devised to allow technical staff
to control the proper working of systems like web server
• NRDC techniques facilitate studying a very small part of the
spectrum of behavioral or social phenomena.
• Person characteristics like appearance, height and weight,
attire, gender, age, ethnic group, facial expressions, eye
contact, body language, gestures and emotive responses are
filtered away (Dholakia & Zhang, 2000).
8
Perspectives on Non
Reactive Data Collection
on the Internet
Non reactive data collection on the Internet can be
viewed from different vantage points, e.g.,
•
•
•
•
•
Technological Perspective
Privacy Perspective
Methodological Perspective
Data Mining Perspective (Web Usage Mining)
Commercial Perspective (e.g., marketing)
…
9
Technological
Perspective
10
Techniques used for Non-Reactive
Data Collection on the Internet
Non-Reactive Data
Collection
Server-Side
Techniques
Client-Side
Techniques
Environment
Variables
Log Files
Log Files
HTTP Logs
REMOTE_ADDR
(IP Address)
Time/Clickstream
Measurement
E-Mail Logs
Referrer
Cookies
Instant Messenger
Logs
DATE_GMT
11
Persistent Cookies
NonPersistent Cookies
(Session IDs)
Client & Server
Client
Client
Web-Server
Client
12
Client
Client & Server (cont.)
2
Browser asks (=requests) a document from a server
1 available via a URL (Unique Resource Locator)
4
2 Server allocates values to environment variables
3 Server retrieves the document (usually HTML
code), generates a header, sends it to the client
4 Server writes entries into its log-files
5 Client presents the HTML code in a readable way
(“renders the HTML code”)
5
13
1
Browser may also call and thus cause to execute a program (-> CGI)
2
1 - 2
If the access fails, the server will generate error-reports instead
Client-Server Interaction follows HTTP (Hypertext Transfer Protocol)
Environment Variables
• To pass data about the information request from the
server to the script, the server uses environment
variables as well as the standard input and output
streams of a CGI-script.
• Environment variables are set when the server
executes the gateway program. There are some
environment variables set for request-specific and
some of set for all requests.
• Selection of some environment variables
14
–
–
–
–
SERVER_SOFTWARE
SERVER_NAME
REQUEST_METHOD
REMOTE_ADDR
Environment Variables (cont.)
• The Common Gateway Interface (CGI) is a standard
for external gateway programs to interface with
information servers such as HTTP servers. A HTTP
server usually supports all environment variables of
the CGI-Version to which it complies.
•
The current version is still CGI/1.1. The CGI/1.2
("Next Generation") Specification is still in the limbo.
• http://www.w3.org/CGI/
• http://hoohoo.ncsa.uiuc.edu/cgi/intro.html
15
Log Files
• Servers (HTTP Server, Web server) or clients keep
a track of system or user activities by generating socalled log files. Log files provide valuable information,
e.g., on the security of the server or the activities of
the user
• There are different types of log files
–
–
–
–
16
Standard log files that follow a specific format
Vendor-specific Log Files
Client-Side or Server-Side Log-Files
Specifically Tailored Log Files
Log Files
• There are different Types of Standard Log Files
generated by a HTTP Server (Web server):
– Access/Transfer Log
– Error Log
occurred
– Referer Log
referred
– Agent Log
17
information about who visited a sit
information on the errors that
while accessing the server
information on the source that
a visitor has visited before
information on the client´s
browser and operating system
Log-Files / Entries („Tokens“)
of Log-Files (Selection)
AG
B
BR
D
S
H
I
NTSC
O
P
SA
SC
SN
REF
18
Type of Browser used (Agent)
Number of Bytes transferred from Server to Client
Number of Bytes transferred from Client to Server
Data/Time of the Request
Service Requested
Client’s domain Name or IP-Address
Identification of the User on the Client Side
Status Code (Win NT)
Operation carried out (e.g., GET)
Files (including Path) requested
IP-Address of the Server
Status Code
Name of the Server
URL of the Site where the Client has been immediately before
Standard Access Log Formats
19
Log Format
Entries
Common Log Format
H, I, A, D, REQ, S, B
Combined Log Format
H, I, A, D, REQ, S, B, REF, AG
Agent Log Format
AG
Referrer Log Format
REF, P
Microsoft IIS Log Format
H, I, A, D, T, S, SN, SA, PT, BR, B,
SC, NTSC, O, P
IP Addresses
•
•
20
Every computer connected to the Internet has a 32 Bit IP or Internet
Protocol address. It consits of 4 octets (Bytes) separated by dots
(e.g., 192.168.1.1).
IANA (Internet Assigned Names Authority, http://www.iana.org/) is
responsible for the world wide administration of IP-Addresses
•
An IP-Address is unique, but one computer may have several IPAddresses – one for each connection to the Internet. Vice versa, one
IP Address can be used by several computers to access the Internet.
•
„Behind“ one computer there may be a complete network. This is the
idea of a gateway. The gateway has a address that is visible from the
outside. Thus, other computers within the network are not visible.
Cookies: Introduction
• What is a Cookie?
“A cookie is an element of data that a Web site can send to
your browser, which may then store it on your system. You
can set your browser to notify you when you receive a cookie,
giving you the chance to decide whether to accept it.”
Source: http://www.w3.org/2001/10/glance/doc/privacy.html
• Why are Cookies so popular?
Not the kind of information per se that is managed by cookies
makes them interesting. This means, cookies do not give you
a privileged access to some pieces of information that you can’t
access via other techniques. What makes cookies interesting
is the kind of information management they allow.
21
Non-Persisten Cookies
(Session IDs)
Session identification URIs permit HTTP transactions to be linked within a limited domain. This
allows a content provider to track activities within sites on their network but does not permit data
from different sites to be correlated without specific user authorization in advance.
Example of a session-id:
http://www.sun.com/2000-1121/wlc/;$sessionid$AY2D5XQAAB42RAMTA1LU45Q
http://subscriptions.sun.com/optin?id=7289675917258240725
http://www.amazon.com/exec/obidos/subst/home/home.html/103-6371678-0789449
You will often see session-IDs as a string of numbers in the browser address bar. These
numbers will track you via cookies and serve pages specific to your "session". A
session can be any time limit and then it expires. Sites use these sessions to serve
custom content, defeat browser caching, and to direct the flow of visitors through the
website.. http://www.webmasterworld.com/glossary/session_id.htm
http://www.w3.org/TR/WD-session-id.html
22
Generation of Cookies
• How are cookies generated?
– Cookies can be sent by the server with a HTTP-response
or they can be set by a server-side (CGI) or client-side
(JavaScript) program. JavaScript can also be used to read
cookies - in accordance to the limitation of cookie usage.
• There are different types of Cookies
– Persistent Cookies vs. Non-persistent cookies.
23
Methodological
Perspective
24
Thin Descriptions – Rich
Descriptions
• Usage of NRD leads to thin descriptions.
• Like all behavioral data, NRD gives no access to
internal states.
Example: E-mail logs might indicate the
“intensity” of a relationship between two
communication persons. In itself, however, they
do not indicate why they communicate in the
first place. Likewise they do not reveal the
content of the communication.
25
Two ways to deal with thin
descriptions
Enlargement of a 2-dimensional Data Set
Vertical Enlargement
26
Horizontal Enlargement
(„data enrichment“)
Two ways to deal with thin
descriptions (cont.)
 Vertical Enlargment
 Merging same-format data of different sources
 Horizontal Enlargement
 Merging data / Triangualation (e.g., Webb et al., 2000)
 Inferring attributes
Example: Horizontal enlargement may violate the user‘s privacy, e.g,
when click-stream information is linked registration information. In this
way data become personally identifiable
27
Combining two ways to deal
with thin descriptions
Step1:
Validation
Vertical & horizontal
Enlargement („validation
Step2:
Prediction
& class prediction“)
28
Example: How can one predict the size of a household on the basis of
the web sites viewed?
1. Record the web sites viewed, the time spend etc. (non-reactive)
2. Find out the size of the household (reactive)
3. Record the web sites viewed, the time spend etc. (non-reactive)
4. Predict the size of the household
Two ways to deal with thin
descriptions (cont.)
 Vertical Enlargment
 Merging same-format data of different sources
 Buying addresses
 Horizontal Enlargement
 Merging data (online & online, e.g., registration information; online
& offline, e.g., operational data)
 Infering attributes
 GOFAST, e.g., regression analysis,
 data mining, e.g., probabilistic techniques,
 others, e.g., affinity scoring
29
Extensions and Recent
Developments
30
The Enron Data Set
(E-Mails)
• In December 2001, the Enron Corporation, an American energy
company based in Houston Texas, collapsed and had to declare
bankruptcy.
• Originally made public by the Federal Energy Regulatory
Commission as part of the legal proceedings against the Enron
Corporation.
• The data cover a huge collection of real e-mail messages sent
and received by employees of the Enron corporation.
• The data set was purchased by Leslie Kaelbling of MIT, who
discovered that it had integrity problems.
• People at CMU, led by Melinda Gervasio corrected these
problems and deleted too sensitive/personal e-mails.
• Distributed in its present form by William Cohen.
http://www.cs.cmu.edu/~enron/
• The Enron Data Set has become a kind of Drosophila for data
mining researchers who want to use non reactive data.
31
The AOL Data Set
(Search Requests)
• In August 2006, AOL (America Online) published a huge data
set of search requests of 650,000 subscribers. Making this data
set public was motivated partly in compliance to requests by US
state authorities, partly due to errors by employees (Wray,
2006).
• The data have been sorted by anonymous user IDs. But soon it
became obvious that it is possible that the queries in the data
set can be traced back to the persons that entered them
(Barbaro & Zeller, 2006).
• As a consequence, AOL quickly closed down the Web site
where the data has been published.
• In the meantime, the data set has been downloaded several
hundred times. A number of mirror sites have been set up such
that the data is in fact available.
• The AOL data set provoked a debate among the privacy “The
number of things it reveals about individual people seems much
too much. In general, you don’t want to do research on tainted
data.” (Hafner, 2006)
32
Collecting all Data
about a Person
A number of projects work towards tracking a person’s entire
existence
• DARPA´s LifeLog Project (2003-2004)
Cancelled for an unknown reason. It is possible, however, that
LifeLog is still, but clandestinely still in development.
• Microsoft‘s MyLifeBit Project
• ACM Workshop on Continuous Archival & Retrieval of
Personal Experiences (CARPE)
33
Vannevar Bush’s
Memex Vision (1945)
• “A memex is a device in which an individual stores all his
books, records, and communications, and which is mechanized
so that it may be consulted with exceeding speed and
flexibility”
• Full-text search, text & audio annotations, and hyperlinks
34
A Personal Transaction Processing
System for Everything
Inspired by Memex
www.MyLifeBits.com
35
MyLifeBits Software
Import files
GPS import &
Map display
SenseCam
VIBE
logging
MyLifeBits
Shell
Text
annotation
tool
Voice
annotation
tool
Screen saver
MyLifeBits
store
Radio
capture
& EPG
Internet
Browser
tool
Legacy
applications
database
IM capture
MAPI
interface
files
PocketPC
transfer
tool
Outlook
interface
TV capture
tool
PocketRadio
36
player
Telephone
capture tool
TV EPG
download tool
Legacy
email client
Discussion
• Often, when studying social phenomena on the
Internet, there is hardly any alternative to non
reactive data.
• Non reactive data may shed light on new social
phenomena and facilitates studying the inner life of
institutions
• There are, however, many challenges
– Turning thin data into rich and meaningful data
(horizontal/vertical enlargement of data sets, usage of data
mining techniques)
– Addressing privacy issues carefully.
37
• Data catastrophes (Enron, AOL) may give insight into
social processes and/or institutions but raise severe
ethical questions.
References
Dholakia, N., & Zhang, D. (2000). Online Qualitative Research in the Age of E-Commerce:
Data Sources and Approaches. Forum Qualitative Sozialforschung / Forum: Qualitative
Social Research, 5 (2). Retrieved September 4, 2006 from
http://www.qualitative-research.net/fqs-texte/2-04/2-04dholakiazhang-e.htm.
Frankfort-Nachmias, C., & Nachmias, D. (2000). Research methods in the social
sciences (6th ed.).New York, NY: Wadsworth. Hofmann, K., Reed, C., & Holz, H. (2006).
Unobtrusive Data Collection for Web-Based Social Navigation.In Workshop on the Social
Navigation and Community-Based Adaptation Technologiesin Conjunction with Adaptive
Hypermedia and Adaptive Web-Based Systems (AH’06) June 10 20th, 2006, Dublin,
Ireland.
Hafner, K. (2006). Researchers Yearn to Use AOL Logs, but They Hesitate. New York Times,
August, 23.
Webb, E. J., Campbell, D. T., Schwartz, R. D. D., & Sechrest, L. (2000). Unobtrusive
measures. Thousand Oaks, CA: Sage.
The Enron E-Mail Data Set
http://www.cs.cmu.edu/~enron/
Environment Variables on HTTP Servers
http://publib.boulder.ibm.com/infocenter/iseries/v5r3/index.jsp?topic=/rzaie/rzaieenvvar.htm
Microsifts MyLifeBits Project
www.MyLifeBits.com
38
Comments on the End of the Lifelog Project
http://www.defensetech.org/archives/000757.html