A Conceptual Model of a Knowledge Warehouse - Enterprise Information Systems Design, Implementation and Management: Organizational Applications - page 150

knowledge level as an extended DW concept
model with a knowledge perspective tier.

These modeling techniques address operation-
ally-related concepts of the transaction data bases
such as facts, dimensions, measures, hierarchies
and cross-dimension attributes. While they sup-
port the OLAP processes, which provide reports
to decision makers, they lack the KM dimensions
that may support integrating these passive knowl-
edge objects within the active decision making
processes. The DW conceptual modeling which
is presented in this study is designed to bridge this
gap with a specific KM layer that handles various
knowledge resources.

conceptual Modeling of
data Warehouse (dW)

By aggregating operational transactions that
represent events from various organizational data-
bases, DW makes it possible to implement online
analytical processing (OLAP) for supporting deci-
sion makers. The DW and OLAP are frequently
modeled conceptually with multidimensional
modeling (Rizzi, 2007), which represents the
transaction data in a cubic metaphor where each
aggregative transaction is considered a cell. Fur-
thermore, the cubic dimensions represent analysis
criteria, which consist of a hierarchy of attributes
that further describe them. This conceptual model
is often represented with a star schema including
a fact table with aggregated data, surrounded by
the source tables. In general, conceptual modeling
and representation facilitate the design process by
abstracting away implementation considerations.
Both designers as well as end-users can better
understand the higher level of abstraction in
describing the DW process and architecture that
this approach achieves.
Several approaches and variations have been
proposed for conceptually modeling DW systems.
For example, there are the entity/relationship
(E/R), the unified modeling language (UML) and
of the dimensional fact model (DFM) (Malinowski
& Zim´anyi, 2006; Rizzi, 2007). In addition, a
hierarchy conceptual model, named MultiDimER
(Malinowski & Zim´anyi, 2006), allows each di-
mension to be represented in hierarchy of attributes
that represents any organizational, geographic, or
other type of structure that is important for analy-
sis. For instance, a spatial dimension might have
a hierarchy with levels such as country, region,
city and office. Hierarchies are also required for
enabling roll-up and drill-down operations needed
for DW analysis.

knowledge Management (kM)

Knowledge is considered as the main competitive
asset of an organization, enabling the enterprise
to be productive and to deliver competitive
products and services (Druker, 1993). The or-
ganizational knowledge is embedded in people,
systems, procedures and products. Knowledge
workers are required to improve their work on
a daily basis in a process that culminates in a
significant improvement in performance for the
entire enterprise. One of the cornerstones of KM is
improving productivity by effectively sharing and
transferring knowledge, activities which tend to
be time-consuming and often impossible (Druker,
1993; Davenport & Prusak, 2000).
Nonaka (1986) distinguishes between tacit and
explicit knowledge. Explicit knowledge is stored
in textbooks, software products and documents;
implicit knowledge resides in the mind in the
form of memory, skills, experience, education,
imagination and creativity - manifestations of the
mind which are difficult to identify and manage.
Alavi and Leidner (2001) claim that information
is converted to knowledge once it is processed in
the mind of individuals and knowledge becomes
information once it is articulated and presented
in the form of a text, software product or other
means. Then, when the receiver cognitively pro-
cesses the information, it is converted back into
tacit knowledge.