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holds artificial directories containing transactive
knowledge.
In order to establish organization-wide
memory, knowledge should be formalized to
establish a perceptional common ground for
sharing. Accordingly, an organizational ontology
that defines the domain of organizational knowl-
edge concepts and their relationships should be
constructed. By this ontology Nevo and Wand
(2005) refer to knowledge, which is represented
by generalized concepts (e.g. project plans) and
concept instances (e.g. project A plan) as well as
knowledge retainers, represented by roles (e.g.
project manager) and role instances (e.g. project
A's project manager, named “Linda”). In addition,
the ontology includes a tacit knowledge represen-
tation, entitled meta-knowledge that defines the
natural tacit perceptions possessed by members of
communities of practice, addressing issues such
as expertise, cognitive capabilities and source
credibility (Nevo & Wand, 2005).
In this paper, a similar approach for the KW
concept is adopted. The KW serves as a transac-
tive memory for organizing the OLAP reports,
using an organizational ontology for defining
stakeholders, business processes and IS services,
but without referring to these concept instances.
However, the tacit knowledge dimension is not
handled in a formalized manner, but rather through
a collaborative environment that fosters naturally
shared practices. Moreover, connecting the KW
to the business process context may improve
knowledge transfer and acquisition.
ning (ERP), customer relationship management
(CRM)) and unstructured (e.g. emails, wikis), as
well as incorporating collected Web data into their
information repositories. Therefore, DW research
should address the need for extending the DW
role in a broader perspective that relates not only
to operational transaction-oriented data, but also,
in order to facilitate business decision-making, to
knowledge created by knowledge-workers within
the enterprise.
Several studies refer to the notion of integrat-
ing KM with DW. Kerschberg (2001) discuss the
need to aggregate heterogeneous data, including
Web information into the data warehouse; Jongho
et al. (2003) propose a systematic conversion of
knowledge into hypermedia artifacts and data
warehouse components; and Nemati et al (2002)
argue that there is a need for a new expanded
DSS, which incorporates DW concepts as well
as a knowledge creation approach, according
to Nonaka's knowledge creation theory (1986).
These studies, however, deal mainly with architec-
ture and conceptual models for decision support
systems (DSS) where DW is included. This paper,
on the other hand, discusses an extended practical
conceptual modeling of DW that addresses KM
aspects and encompasses stakeholders, business
processes and IS.
The current literature review has shown that
none of the existing approaches for conceptual
modeling of DW addresses KM issues as part
of the DW model. This paper aims at bridging
this gap by introducing a knowledge warehouse
conceptual model (KW-CM) that extends the
multidimensional modeling methodology (Rizzi,
2007) with a knowledge layer. The purpose of
such a conceptual model is to ensure that deci-
sion makers will be able to retrieve and transfer
comprehensive and appropriate knowledge to
relevant stakeholders.
dW and kM
Current trends in DW (Kerschberg, 2001; Nemati
et al., 2002; Ralaivao & Darmont, 2007) indicate
that organizations are now becoming e-Enterprises
as they move their operations to the Internet. This
development is characterized by establishing
partnerships with external stakeholders such as
customers and suppliers; and handling complex
data, both structured (e.g. enterprise resource plan-
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