Abstract: The objective of this paper is to review and assess the
methodological issues and problems in marketing research, data and
knowledge mining in Turkey. As a summary, academic marketing
research publications in Turkey have significant problems. The most
vital problem seems to be related with modeling. Most of the
publications had major weaknesses in modeling. There were also,
serious problems regarding measurement and scaling, sampling and
analyses. Analyses myopia seems to be the most important problem
for young academia in Turkey. Another very important finding is the
lack of publications on data and knowledge mining in the academic
world.
Abstract: To investigate the correspondence of theory and
practice, a successfully implemented Knowledge Management
System (KMS) is explored through the lens of Alavi and Leidner-s
proposed KMS framework for the analysis of an information system
in knowledge management (Framework-AISKM). The applied KMS
system was designed to manage curricular knowledge in a distributed
university environment. The motivation for the KMS is discussed
along with the types of knowledge necessary in an academic setting.
Elements of the KMS involved in all phases of capturing and
disseminating knowledge are described. As the KMS matures the
resulting data stores form the precursor to and the potential for
knowledge mining. The findings from this exploratory study indicate
substantial correspondence between the successful KMS and the
theory-based framework providing provisional confirmation for the
framework while suggesting factors that contributed to the system-s
success. Avenues for future work are described.
Abstract: Text Mining is an important step of Knowledge
Discovery process. It is used to extract hidden information from notstructured
o semi-structured data. This aspect is fundamental because
much of the Web information is semi-structured due to the nested
structure of HTML code, much of the Web information is linked,
much of the Web information is redundant. Web Text Mining helps
whole knowledge mining process to mining, extraction and
integration of useful data, information and knowledge from Web
page contents.
In this paper, we present a Web Text Mining process able to
discover knowledge in a distributed and heterogeneous multiorganization
environment. The Web Text Mining process is based on
flexible architecture and is implemented by four steps able to
examine web content and to extract useful hidden information
through mining techniques. Our Web Text Mining prototype starts
from the recovery of Web job offers in which, through a Text Mining
process, useful information for fast classification of the same are
drawn out, these information are, essentially, job offer place and
skills.
Abstract: The state of the art in instructional design for
computer-assisted learning has been strongly influenced by advances
in information technology, Internet and Web-based systems. The
emphasis of educational systems has shifted from training to
learning. The course delivered has also been changed from large
inflexible content to sequential small chunks of learning objects. The
concepts of learning objects together with the advanced technologies
of Web and communications support the reusability, interoperability,
and accessibility design criteria currently exploited by most learning
systems. These concepts enable just-in-time learning. We propose to
extend theses design criteria further to include the learnability
concept that will help adapting content to the needs of learners. The
learnability concept offers a better personalization leading to the
creation and delivery of course content more appropriate to
performance and interest of each learner. In this paper we present a
new framework of learning environments containing knowledge
discovery as a tool to automatically learn patterns of learning
behavior from learners' profiles and history.