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International Journal of Advancements in Computing Technology
Volume 1, Number 1, September 2009
The Combination of Knowledge Management and Data mining
with Knowledge Warehouse
Ming-Chang Lee
Department of Information Management, Fooyin University, Taiwan, ROC
Ming_li@mail2000.com.tw
doi: 10.4156/ijact.vol1.issue1.6
Abstract
Effective knowledge management (KM) enhances
products, improves operational efficiency, speeds
deployment, increases sales and profits, and creates
customer satisfaction. Data warehousing provides an
infrastructure that enables business to extract, and
store vast amounts of corporate data. The purpose of
data warehouse is to empower the knowledge workers
with information that allows them to make decision
based on a foundation of fact. The aim of this paper
is to integrate a framework of knowledge management
and data mining with knowledge warehouse. Therefore,
first, it will brief review the existing of knowledge
management, data mining, and decision support system.
We then present on knowledge management or
supplementary relationship between the knowledge
management, data mining and decision support system
with knowledge warehouse. Second, knowledge,
knowledge management and knowledge process is
defined. Third, we introduce decision support, data
mining and data warehouse support of knowledge
management, and point out data mining in data
warehouse environment. Four, the warehouse is
defined. Using this definition, it can drive framework
of knowledge warehouse. This framework contain 6
layers: knowledge input, knowledge activity, data store,
application server, application system data base, and
user client. In this paper some suggestions are made
to get knowledge with data mining, which will provide
the decision maker with an intelligent platform that
enhances all phase of knowledge management and
knowledge process.
Keywords
Knowledge Management, Data Warehouse, Data
Mining, Decision Support, Knowledge Warehouse
1. Introduction
A knowledge warehouse (KW) is the component of
an enterprise's knowledge management system, used to
develop, store, organize, process, and disseminate
knowledge. KW can be thought of as an "information
repository" in which knowledge components are
cataloged and stored for reuse. A knowledge
warehouse enables a variety of different views of
knowledge, useful in areas such as training or
documentation. These views could be pre-set and
organized by instructional designers or technical
writers. Additionally, the knowledge warehouse
could also support ad hoc queries, such as electronic
performance support systems, intelligent help, or
reference materials. Not incidentally, knowledge can
be stored in several physical places, although that is
not a requirement.
A data warehouse (DW) is a central repository for
all or significant parts of the data that an enterprise's
various. DW, an integral part of the process,
provides an infrastructure that enables businesses to
extract, cleanse, and store vast amount of corporate
data from operational systems for efficient and
accurate responses to user queries. DW empowers
the knowledge workers with information that allows
them to make decisions based on a solid foundation of
fact [5]. Data mining is a decision-making functions
(decision support tool). Data mining (DM) has as its
dominant goal, the generation of no-obvious yet useful
information for decision makers from very large data
warehouse (DW). DM is the technique by which
relationship and patterns in data are identified in large
database [8]. In DW environment, DM techniques
can be used to discover untapped pattern of data that
enable the creation of new information. DM and
DW are potentially critical technologies to enable the
knowledge creation and management process [2].
The DW is to provide the decision-maker with an
intelligent analysis platform that enhances all phase of
the knowledge management process. Decision
support system (DSS) or intelligent decision support
system (IDSS) and DM can be used to enhance
knowledge management and its three associated
processes: i.e., tacit to explicit knowledge conversion,
explicit knowledge leveraging, and explicit knowledge
conversion [14].
39
The Combination of Knowledge Management and Data mining with Knowledge Warehouse
Ming-Chang Lee
Figure 1. Framework of knowledge management, data mining and IDSS with knowledge warehouse
DSS is a computer-based system that aids the
process of decision-making [9]. DSS are interactive
computer-based systems that help decision makers
utilize data and models to solve unstructured problems.
DSS can also enhance the tacit to explicit
knowledge conversion by eliciting one or more what-if
cases (i. e., model instances) that the knowledge
worker wants to explore. That is, as the knowledge
worker changes one or more model coefficients or
right hand side values to explore its effect on the
modeled solution. That is, the knowledge worker is
converting the tacit knowledge that can be shared with
other workers and leveraged to enhance decision.
DSSs which perform selected cognitive
decision-making functions and are based on artificial
intelligence or intelligent agent’s technologies are
called Intelligent Decision Support Systems (IDSS)
[10]. Dhar and Stein [6] use term to characterize the
degree of intelligence provided by a decision support
tool. It describes intelligence density as representing
the amount of useful decision support information that
a decision maker gets from using the output from some
analytic system for a certain amount of time [6]. The
goal of KW is to provide the decision maker with an
intelligent platform that enhances all phase of
knowledge management and knowledge process.
Figure 1 is showed as a framework of knowledge
management, data mining and IDSS with knowledge
warehouse
2. Knowledge Management
Knowledge management is the process established
to capture and use knowledge in an organization for the
purpose of improving organization performance [16].
Knowledge management is emerging as the new
discipline that provides the mechanisms for
systematically managing the knowledge that evolves
with enterprise. Most large organizations have been
experimenting with knowledge management with a
view to improving profits, being competitively
innovative, or simply to survive ([4], [11], [13], [15]).
Knowledge management systems refer to a class of
information systems applied to managing organization
knowledge, which is an IT-based system developed to
support the organizational knowledge management
behavior: acquisition, generation, codification, storage,
transfer, retrieval [1]. There are two forms of
knowledge explicit knowledge and tacit knowledge.
Explicit knowledge is defined as knowledge that can
be expressed formally and can be easily communicated
or diffused throughout an organization [18]. Implicit
(tacit) knowledge is knowledge that is unmodified and
difficult to diffuse. The implicit knowledge is learned
through extended periods of experiencing and doing a
task, during which the individual develops a feel for
and a capability to make intuitive judgments about the
successful execution of the activity [3]. Nonaka and
Takeuchi [12] view implicit knowledge and explicit
knowledge as complementary entities. There contend
that there are four modes (Socialization,
Externalization, Combination, and Internalization) in
which organizational knowledge is created through the
interaction and conversion between implicit and
explicit knowledge.
Data
Warehouse
OLAP
Data Mining
Business Intelligence
Knowledge and
Knowledge management
Integrate scrub
Transform
Load
Discovery
Learn
Decision
Support
Enhance
Data Process
Knowledge
Warehouse
Extract
40
International Journal of Advancements in Computing Technology
Volume 1, Number 1, September 2009
OLAP
Source System
Operational systems
Enterprise data
Warehouse
Data
Mining
Figure 2. Data mining in data warehouse environment
Common knowledge management practices include:
(1) Creating and improving explicit knowledge
artifacts and repositories (developing better databases,
representations, and visualizations, improving the
real-time access to data, information, and knowledge;
delivering the right knowledge to the right persons at
the right time). (2) Capturing and structuring tacit
knowledge as explicit knowledge (creating knowledge
communities and networks with electronic tools to
capture knowledge and convert tacit knowledge to
explicit knowledge). (3) Improving knowledge
creation and knowledge flows (developing and
improving organizational learning mechanisms;
facilitating innovation strategies and processes;
facilitating and enhancing knowledge creating
conversations/dialogues). (4) Enhancing knowledge
management culture and infrastructure (improving
participation, motivation, recognition, and rewards to
promote knowledge sharing and idea generation;
developing knowledge management enabling tools and
technologies). (5) Managing knowledge as an asset
(identifying, documenting, measuring and assessing
intellectual assets; identifying, prioritizing, and
evaluating knowledge development and knowledge
management efforts; document and more effectively
levering intellectual property). (6) Improving
competitive intelligence and data mining strategies and
technologies.
3. Decision Support, Data mining and Data
Warehouse support of Knowledge
Management
3.1 Data mining vs. Data warehouse
Data mining includes tasks such as knowledge
extraction, data archaeology, data exploration, data
pattern processing, data dredging, and information
harvesting. Data mining is a process that uses
statistical, mathematical, artificial intelligence, and
machine learning techniques to extract and identify
useful information and subsequent knowledge from
large databases [17]. DM uses well-established
statistical and machine learning techniques to build
models that predict customer behavior. Today,
technology automates the mining process, integrates it
with commercial data warehouses, and presents it in a
relevant way for business users.
The data warehouse is a valuable and easily
available data source for data mining operations.
Data extractions the data mining tools work on come
from the data warehouse. Figure 2 illustrates how
data mining fits in the data warehouse environment.
Notice how the data warehouse environment supports
data mining.
3.2 Decision support system vs. Knowledge
In tacit to explicit knowledge conversion, the
literature of knowledge acquisition in expert
systems-based DSS is well established. DSS can
enhance that the explicit knowledge conversion
through the specification of mathematical models (e.g.,
linear programming models). The knowledge worker
also explicitly specifies the model constrains in terms
of the decision variables, and estimates both numerical
coefficients of decision variables in each constraint and
in the objective function (e. g. goal programming
models).
Flat files with
extracted and
transformed
data
Load image
files ready for
loading the
data
warehouse
Data selected, extracted,
transformed, and prepared for
mining
41
The Combination of Knowledge Management and Data mining with Knowledge Warehouse
Ming-Chang Lee
Knowledge Management Cyclical conversion of tacit
System to explicit knowledge
Enterprise
Knowledge Data
Portal Warehouse
Figure 3. Integration of KM and data warehouse
The knowledge worker’s tacit knowledge is converted
to explicit knowledge and stored in an appropriate
form; it can be leveraged by making it available to
others who need it. In adding, analyzing explicit
knowledge to produce new knowledge can further it.
Explicit knowledge stored in the form of instances of a
mathematical model (what-if-cases) can be leveraged
via deductive and/or inductive model analysis systems.
Another form of explicit leveraging is found in
case-based reasoning (CBR).
CBR is characterized by the knowledge worker
making his or her interfaces and decision based
directly on previous cases recalled from memory.
That is, the knowledge worker tries to avoid, or reduce,
the potential for failure by recalling previous similar
failures and avoiding the associated pitfalls or
changing key factors in those previous failures.
DSS or GSS can also provide valuable aids in
internalizing explicit and new knowledge. One mode
of internalizing explicit and / or new knowledge is
through modifying the internal mental model that a
knowledge worker uses to serve as a performance
guide in specified situations.
3.3 Knowledge management system vs. Data
warehouse
Knowledge management system (KMS) is a
systematic process for capturing, integrating,
organizing, and communicating knowledge
accumulated by employees. It is a vehicle to share
corporate knowledge so that the employees may be
more effective and be productive in their work.
Knowledge management system must store all such
knowledge in knowledge repository, sometimes called
a knowledge warehouse. If a data warehouse contains
structured information, a knowledge warehouse holds
unstructured information. Therefore, a knowledge
framework must have tools for searching and retrieving
unstructured information. Figure 3 is integration of
KM and data warehouse.
4. Knowledge Warehouse
The DW is a type of database managed by a DBMS.
Indeed, in its present form the DW is a database that
uses a relational DBMS. The KW is a type of
database managed by a Knowledge base Management
System (KBMS) and Artificial Knowledge Base
(AKB). An AKB is the portion of an organization’s
knowledge base expressed in the persistent storage and
non-persistent memory of computers. Unlike a
database which store records, an AKB stores a network
of objects and components, and these encapsulate data
and methods. KBMS is a computer application for
managing (creating, enhancing, and maintaining) the
AKB, just as a DBMS is a computer application for
managing a database. KW may be viewed as
subject-oriented, integrated, time-variant, and support
of management’s decision processes. But unlike the
DW, it is a combination of volatile and nonvolatile
objects and components, and, of course, it stores not
only data, but also information and knowledge.
In the DW, data about a subject is stored in a set
of tables. As we have designed the KW, the storage
structure is referred to as a knowledge base and is
constructed as a tree with objects at the nodes. The
KW can be accessed by executing stored knowledge.
It can serve as the repository to document a company’s
business knowledge. Since the KW contains a great
deal of information encapsulated in attributes and
methods, it can be dynamically queried again.
The KW can be through as six-layer processes of
Knowledge input, Knowledge activity, data store;
application server, application system database, and
user client (see Figure 4).
Layer 1: Knowledge input
According [7] future companies will be dominated
Internet
Intranet
Extranet
KM
Implicit
Explicit
Articulat
Internaliz
EKP
42
International Journal of Advancements in Computing Technology
Volume 1, Number 1, September 2009
by knowledge workers. In the next society,
information and knowledge will be acquired easily
through intranets, extranets, and the Internet.
Knowledge flow will converge toward the Enterprise
Information Portal (EIP). Knowledge flow also may
come from non-IT channels, such as tacit knowledge
(knowledge that is implicit by or inferred from actions
or statements) and explicit knowledge (knowledge that
is fully and clearly expressed, leaving nothing implied).
All knowledge will be integrated into knowledge
repositories.
Layer 2: Knowledge activity
The data warehouse “Extraction, transformation,
Migration and load” (ETML) process has a parallel in
the knowledge warehouse. KW also has logical
structure to store knowledge that is analogous to the
system tables that implement data store in the DW.
Knowledge is applied through a layered representation
that shields code until the bottom layer.
The spiral of knowledge postulates four interaction
processes – socialization, externalization, combination,
and internalization—that transfer individual employee
knowledge to company knowledge.
Layer 3: Data Stores
An operational data store (ODS) is a database
designed to integrate data from multiple sources to
make analysis and reporting easier. Because the data
originates from multiple sources, the integration often
involves cleaning, resolving redundancy and checking
against business rules for integrity. An ODS is
usually designed to contain low level or atomic
(indivisible) data (such as transactions and prices) with
limited history that is captured "real time" or "near real
time" as opposed to the much greater volumes of data
stored in the data warehouse generally on a less
frequent basis.
Data mart (DM) is a subset of an organizational
data store, usually oriented to a specific purpose or
major data subject that may be distributed to support
business needs. DM is analytical data stores designed
to focus on specific business functions for a specific
community within an organization. Data marts are
often derived from subsets of data in a data warehouse,
though in the bottom-up data warehouse design
methodology the data warehouse is created from the
union of organizational data marts.
Layer 4: Application Server
This layer contains application server (or engine),
such as BPE, KDD, MOLAP, and CTS. Middleware is
computer software that connects software components
or applications. The software consists of a set of
services that allow multiple processes running on one
or more machines to interact across a network. This
technology evolved to provide for interoperability in
support of the move to coherent distributed
architectures, which are used most often to support and
simplify complex, distributed applications. It includes
web servers, application servers, and similar tools that
support application development and delivery.
Middleware is especially integral to modern
information technology based on XML, SOAP, Web
services, and service-oriented architecture.
Business Process Engine (BPE) is an orchestration
tool that liberates data from process and process from
application, allowing your business to adapt quickly to
change. BPE can reduce transaction costs, accelerate
order fulfillment and enhance customer satisfaction. It
provides a flexible, robust and scalable system.
Knowledge Discovery and Data Mining (KDD) is an
interdisciplinary area focusing upon methodologies for
extracting useful knowledge from data. The ongoing
rapid growth of online data due to the Internet and the
widespread use of databases have created an immense
need for KDD methodologies. The challenge of
extracting knowledge from data draws upon research in
statistics, databases, pattern recognition, machine
learning, data visualization, optimization, and
high-performance computing, to deliver advanced
business intelligence and web discovery solutions.
MOLAP is an alternative to the ROLAP (Relational
OLAP) technology. While both ROLAP and MOLAP
analytic tools are designed to allow analysis of data
through the use of a multidimensional data model,
MOLAP differs significantly in that it requires the
pre-computation and storage of information in the cube
- the operation known as processing. MOLAP stores
this data in optimized multi-dimensional array storage,
rather than in a relational database (i.e. in ROLAP).
A transaction server (CTS) is a software component
that is used in implementing transactions. A
transaction involves multiple steps which must be
completed atomically. For example, when paying
someone from your bank, the system must guarantee
that the money is taken from your account and paid
into the other persons account. It would simply be
unacceptable for just one or the other action to take
place; both must occur in order for the transaction to
have taken place.
Layer 5: Application System
This layer contains application system, such as DSS,
ES, and EIS. An Executive Information System (EIS)
is a type of management information system intended
to facilitate and support the information and
decision-making needs of senior executives by
providing easy access to both internal and external
information relevant to meeting the strategic goals of
43
The Combination of Knowledge Management and Data mining with Knowledge Warehouse
Ming-Chang Lee
the organization. It is commonly considered as a
specialized form of a Decision Support System (DSS).
The emphasis of EIS is on graphical displays and
easy-to-use user interfaces. They offer strong reporting
and drill-down capabilities. In general, EIS are
enterprise-wide DSS that help top-level executives
analyze, compare, and highlight trends in important
variables so that they can monitor performance and
identify opportunities and problems. EIS and data
warehousing technologies are converging in the
marketplace.
An expert system (ES) is software that attempts to
reproduce the performance of one or more human
experts, most commonly in a specific problem domain,
and is a traditional application and/or subfield of
artificial intelligence. A wide variety of methods can
be used to simulate the performance of the expert
however common to most or all are (1) the creation of
a so-called "knowledgebase" which uses some
knowledge representation formalism to capture the
Subject Matter Experts (SME) knowledge and (2) a
process of gathering that knowledge from the SME and
codifying it according to the formalism, which is called
knowledge engineering. Expert systems may or may
not have learning components but a third common
element is that once the system is developed it is
proven by being placed in the same real world problem
solving situation as the human SME, typically as an aid
to human workers or a supplement to some information
system.
Layer 6: User client
A query is a form of questioning, in a line of inquiry.
A GUI is a graphical (rather than purely textual) user
interface to a computer. As you read this, you are
looking at the GUI or graphical user interface of your
particular Web browser. Today's major operating
systems provide a graphical user interface.
Applications typically use the elements of the GUI that
come with the operating system and add their own
graphical user interface elements and ideas.
Figure 4. Framework of knowledge warehouse
Internet Data Extranet Data Intranet Data
Extraction, Transformation, Migration and Loading
ETML
Middleware
ROLAP
CTS RDB BPE KDD MOLAP
Query & reporting client
Browsers
Client GUI
Web/ PUB
Layer 2:
Knowledge
activity
DDS
DW DM ODS
Layer 3:
Data Store
DSS
Model Base Knowledge Base
Layer 4:
Application
Server
Data Base
ES EIS
Layer 5:
Application
System
Data Base
Layer 6:
User Client
Layer 1:
Knowledge
input
44
International Journal of Advancements in Computing Technology
Volume 1, Number 1, September 2009
Elements of a GUI include such things as: windows,
pull-down menus, buttons, scroll bars, iconic images,
wizards, the mouse, and no doubt many things that
haven't been invented yet.
With the increasing use of multimedia as part of the
GUI, sound, voice, motion video, and virtual reality
interfaces seem likely to become part of the GUI for
many applications.
5. Summary and Conclusion
The knowledge warehouse, as an extension of the
data warehouse, provides a mechanism to capture,
store, and access knowledge. Knowledge can be
automated techniques such as are found in data mining.
Knowledge can be stored in a tree structure with
software objects placed at the tree nodes. Combined
with tree search algorithms and tree manipulation
methods, this provides a powerful built-in control
structure at the system level. KW can provide a
software object model of business process with
embedded intelligence. In this paper, we have
knowledge warehouse model, a knowledge warehouse
architecture which is an IT-based system developed to
support the organizational knowledge management
behavior: acquisition, generation, codification, storage,
transfer, retrieval. The framework of knowledge
warehouse consists of an of knowledge input,
knowledge activity, data store, application server,
application system database, and user client.
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