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J. of Business
Data
Communications
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2(2), 1-20, April-June 2006 1
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This paper appears in the publication, International Journal of Business Data Communications and Networking, Vol. 2, Issue 2
edited by Jairo Gutierrez © 2006, Idea Group Inc.
Strip-Mining the Web with SAIM:
A System for the Analysis of Instant Messaging
David G. Schwartz, Bar-Ilan University, Israel
Zac Sadan, Bar-Ilan University, Israel
ABSTRACT
There is no shortage of knowledge waiting to be mined on the Web. But there is a
difference between mining to the depths of the Web and examining what is occurring
on its surface. In this article, the authors argue that the search for actionable
knowledge should start as close as possible to the user, which, in today’s
technological environment, means tapping into Instant Messages. They present a
System for the Analysis of Instant Messages, explain the architectural and
operational motivation, and show how instant messages can be mined to produce
a new source of Web-based knowledge.
Keywords: communications; data mining; instant messages; knowledge
management
INTRODUCTION
Most research on data mining the
Web for actionable knowledge inherently
assumes that the knowledge in question
has been buried deeply. Why, otherwise,
would we be required to mine for it? Digging deeply into Web-based knowledge
presents significant challenges ranging
from extracting and formatting data for
human use (Knoblock, Lerman, Minton,
& Muslea, 2000) to correlating the discovered data with the current application
action (Ramakrishnan & Grama, 1999)
to improving a search by mining the link
structure of the Web (Chakrabarti et al.,
1999) to mining data from mobile users
(Goh & Taniar, 2005). A different approach
to finding actionable knowledge is to tap
into that knowledge as it is being created
— knowledge in action, so to speak. To
retain the mining analogy, this could be referred to as the information analog to stripmining — the process of extracting ore from
wide yet shallow mineral deposits. Finding knowledge, or minerals, for that matter, closer to the surface has clear economic
Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc.
is prohibited.
2
Int’l J. of Business Data Communications and Networking, 2(2), 1-20, April-June 2006
advantages both in terms of time and
equipment costs. Fortunately, the main
disadvantage to strip mining in the physical world — ecological damage — does
not play a part in strip mining for knowledge.
Data mining is fundamentally an applied science that focuses on how to find
useful knowledge in data (Wu et al., 2003).
Taking the view that data mining should
be application-driven, we stand to benefit
from moving our mining activity as close
as possible to that application rather than
in logs or databases to be analyzed postapplication. To clarify, our approach is not
one of data mining; rather, we contend that
an alternative to data mining must be
sought in order to better utilize the vast
quantities of knowledge that flow across
the Web. Harnessing tacit knowledge is
recognized as one of the most challenging
aspects of knowledge management. Tacit
knowledge embodies what the knower
knows, based on experience, beliefs, and
values (Marwick, 2001; Nonaka &
Takeuchi, 1995). It is actionable knowledge at its finest, and the text of instant
messages (IM) is the closest we can hope
to get to a stream-of-consciousness in
which tacit knowledge is first exposed. The
ultimate aim of our approach is to enable
the structured analysis of IM in real time
and to enable a correlation between message content and internal corporate data.
The applications of such an approach will
be to link IM to the corporate operational
fabric rather than have it function as a
stand-alone communications medium.
We are addressing the challenge of
harnessing the tacit knowledge to be found
in IM. In turning our attention to knowledge closer to the surface, we have developed and are experimenting with a System for the Analysis of Instant Messages
(SAIM). The activities performed by the
system combine packet-sniffing techniques
with a knowledge management system in
order to improve the performance of IM
activity based on message content and
accumulated knowledge. On the one hand,
capture the knowledge related to user
behavior and usage patterns, and on the
other hand, act on this knowledge before
it gets buried in the depths of a database
or log file.
Earlier work on actionable knowledge (Schwartz & Te’eni, 2000) focuses
on tying knowledge to action in an e-mail
environment, where e-mail messages are
augmented with organizational memories
to add information richness. The current
research takes this in a new direction by
performing real-time analysis of IM content correlated with historical message
content and organizational knowledge.
IM is a Web-based technology that
enables users to synchronously send and
receive messages, files, and other data
within seconds of transmission. Initially
developed as a method to keep in touch
with friends in real time, the technology
underlying these functions has become increasingly important to business. IM now
extends its reach from the enterprise to
the home to mobile devices with alwayson connections. IM offers the convenience
of e-mail, combined with the immediacy
of a telephone conversation and the potential for file and voicemail message transmission, but perhaps most importantly, it
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is prohibited.
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