Hypermedia modeling for linking knowledge to data warehousing system

Hypermedia modeling for linking knowledge to data warehousing system

Jongho Kima, Woojong Suhb, Heeseok Leea,*

aGraduate School of Management, Korea Advanced Institute of Science and Technology (KAIST), 207-43,

Chongryangri-dong, Dongdaemun-gu, Seoul 130-012, South KoreabDivision of Business Administration, College of Business and Economics, Inha University, 253, Yonghyun-dong, Nam-gu, Incheon 402-751, South Korea

Abstract

Today’s economy runs on knowledge and more companies work assiduously to capitalize on knowledge support systems. Hypermedia can

be used for effective coordination and sharing of knowledge. This paper proposes a methodology for capturing knowledge by the use of

hypermedia model. This hypermedia model can link knowledge to data warehousing systems. The methodology consists of three phases:

knowledge elicitation, hypermedia modeling, and system implementation. The emphasis is on systematic conversion of knowledge into

hypermedia artifacts and data warehouse components. A real-life case for a medical data warehousing system is illustrated to demonstrate the

usefulness of the proposed methodology. Our methodology is better able to help put the corporate knowledge into wider sharing.

q 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Knowledge management; Knowledge modeling; Methodology; Hypermedia; Data warehouse; Medical

1. Introduction

Knowledge is a competitive resource that allows

companies to function productively. Given the importance

of knowledge in virtually all aspects of commercial life, it is

becoming increasingly clear that at some point every

company will view itself as knowledge-intensive (Davenport

& Grover, 2001). Despite its importance, managing knowl-

edge is not a trivial task. One of the key issues in knowledge

management is the role of information technology in the

reuse of knowledge (Liebowitz, 2001; Markus, 2001).

Knowledge support systems are helpful for the effective

reuse of knowledge (Sveiby, 1997; Tapscott, Ticoll, &

Lowy, 2000).

For developing these systems, how to capture knowledge

and how to store it are frequent organizational concerns. The

poor track record of knowledge reuse suggests that linking

knowledge to information system is a challenging achieve-

ment. Hypermedia technologies can overcome these con-

cerns thanks to their adaptive capabilities of modeling

knowledge. Furthermore, its integration with data ware-

house (DW) can encourage managers’ competent decision-

making by the use of context-specific data, models,

statistics, and optimization techniques.

Currently, a variety of methodologies for developing

hypermedia application are available. These methodologies

include hypermedia design methodology (HDM; Garzotto,

Mainetti, & Paolini, 1995; Garzotto, Paolini, & Schwabe,

1993), relationship management methodology (RMM;

Balasubramanian, Isakowitz, & Stohr, 1994; Isakowitz,

Kamis, & Koufaris, 1997; Isakowitz, Stohr, & Balasubra-

manian, 1995), view-based hypermedia design method-

ology (VHDM; Lee, Kim, Kim, & Cho, 1999a), enhanced

object relationship model (EORM; Lange, 1994, 1996),

object-oriented hypermedia design method (OOHDM;

Schwabe & Rossi, 1995a,b), scenario-based object-oriented

hypermedia design methodology (SOHDM; Lee, Lee, &

Yoo, 1999b), index-driven hypermedia design methodology

(IHDM; Suh & Lee, 2001), and workflow-based hyper-

media development methodology (WHDM; Lee & Suh,

2001). Yet these methodologies are not extended to capture

organizational knowledge. Similarly, current DW design

methodologies fail to accommodate knowledge-intensive

hypermedia applications (Hackney, 1997; Inmon, 1993;

Lee, Kim, & Kim, 2001; Murtaza, 1998; Poe, 1997).

To surmount these difficulties, this paper proposes a

methodology by employing a hypermedia model. The

emphasis of our methodology is on a systematic conversion

of knowledge into DW components. Our hypermedia model

can help managers harvest the knowledge developed so

painstakingly.

The following is the organization of this paper. Section 2

explains the architecture and physical implementation

0957-4174/03/$ - see front matter q 2002 Elsevier Science Ltd. All rights reserved.

PII: S0 95 7 -4 17 4 (0 2) 00 0 88 -X

Expert Systems with Applications 24 (2003) 103–114

www.elsevier.com/locate/eswa

* Corresponding author. Tel.: þ82-2-958-3615; fax: þ82-2-958-3604.

E-mail addresses: [email protected] (H. Lee), [email protected].

ac.kr (J. Kim), [email protected] (W. Suh).

http://www.elsevier.com/locate/eswa

platform of the proposed methodology. Section 3 illustrates

the methodology. A real-life medical DW implementation is

illustrated to demonstrate the feasibility of the method-

ology. Section 4 highlights mapping rules among knowl-

edge, hypermedia, and DW components. Section 5

compares our methodology with others. Section 6 concludes

the paper.

2. A methodology

This section explores our methodology and illustrates the

system architecture as its implementation platform.

2.1. Methodology architecture

Our methodology is devised to develop knowledge-

intensive hypermedia applications. It attempts to link

knowledge analysis results to DW components via a

hypermedia model. Our methodology may be referred to

as knowledge-intensive hypermedia design methodology

(KHDM). KHDM consists of three phases: knowledge

elicitation, hypermedia modeling, and system implemen-

tation (Fig. 1). These phases are performed in an iterative

way, even though feedback is not depicted for the simplicity

of presentation.

The first phase of KHDM is knowledge elicitation, which

aims at analyzing knowledge requirements and capturing

relationships between knowledge instances and business

activities. Accordingly, this phase consists of two steps:

knowledge classification and knowledge management

episode (KME) design (Holsapple & Joshi, 2001). Knowl-

edge classification step identifies knowledge objects

according to a knowledge classification scheme by Holsapple

and Whinston (1987). As a result, a knowledge classification

table (KCT) is produced. KME design step specifies

operating scenarios and knowledge manipulation (KM)

activities through a value chain analysis (Holsapple &

Singh, 2001).

The hypermedia modeling phase transforms knowledge

into hypermedia model. This phase includes three steps:

hyperspace analysis, internal conceptual design, and

external navigation design. The hyperspace analysis step

defines the nodes and links as well as their types. This step

results in a node and link list (NLL). Then, hypermedia

modeling is performed from internal and external perspec-

tives. The internal conceptual design captures static

relationships among knowledge objects, while external

navigation design attempts to find navigational logic for the

interaction with users. The external navigation design step

produces an integrated hypermedia model (IHM).

The system implementation phase designs DW com-

ponents and develops hypermedia interfaces. Artifacts

included in IHM are transformed into DW components.

The conversion results are documented in the form of

system component list (SCL). These components are

implemented through the iterative process of class gener-

ation, componentization, and structured packaging.

Fig. 1. KHDM architecture.

J. Kim et al. / Expert Systems with Applications 24 (2003) 103–114104

2.2. Implementation architecture

Our methodology can help integrate knowledge-intensive

applications with DW. For this integration, conventional

DW architecture needs to be improved for better linking to

hypermedia knowledge artifacts. In this paper, we propose

an extended DW architecture, which includes a hypermedia

presentation layer as shown in Fig. 2. This architecture

highlights the dynamic nature of DW by incorporating five

flows as shown in Table 1 (Lee et al., 2001). DW is often

referred to as ‘data warehousing’ to emphasize its dynamic

characteristics (Hackathorn, 1995).

The implementation architecture consists of three layers:

operational data store (ODS)/DW layer, hypermedia

presentation layer, and linkage layer. The ODS/DW layer

is responsible for structuring, processing, and meaningful

grouping of knowledge contents; it keeps source data in

ODS and this data is aggregated in DW. The data in ODS is

usually integrated, current-valued, subject-oriented and

used to support day-to-day detailed operational decisions

(Inmon & Kelley, 1993). DW can be conceived as a set of

materialized views under the framework of relational data

model (Chaudhuri & Dayal, 1997). The hypermedia

presentation layer manages knowledge objects and their

chunking relationships. The linkage layer provides devel-

opers with a set of components to integrate these

heterogeneous layers. The system components of the

implementation architecture can be summarized as shown

in Table 2.

3. Methodology details

In this section, each phase of the methodology is

described in further detail by the use of a hospital-wide

data warehousing application in Asan Medical Center

(AMC). AMC, founded by the Hyundai group in Korea, is

the largest hospital in the country with 2200 beds, 1100

doctors, and 7000 average outpatient visits. In Korea,

several hospitals including AMC are trying to replicate best

practices and manage organizational knowledge by employ-

ing DW. The DW project attempts to achieve a leap to the

world-class hospital with the support of a medical

intelligence application on DW. The emphasis of the

application was on providing clinical knowledge for

medical practitioners. The DW project in AMC was carried

out by two project managers, five project leaders, and 15

developers, with the support of R&D funds from the Korean

government for 3 years. A total of 2200 classes were

implemented on the basis of Sybase IQ database engine,

Microsoft Windows 2000 COM þ engine, and Web

desktops.

Fig. 2. Implementation architecture.

Table 1

Dataflow and metaflow

Flows Description

Data flow

Inflow Data sources are cleansed and proceed

to informational database.

Ownflow

Upflow Detailed data are aggregated and summarized.

Downflow Useless data is archived or deleted

according to a purge criteria.

Outflow Users get data from informational databases

by running canned or ad hoc

queries.

Metaflow Users keep metadata up to date.

J. Kim et al. / Expert Systems with Applications 24 (2003) 103–114 105

3.1. Phase 1: knowledge elicitation

This phase analyzes knowledge requirements. Major

tasks are to classify knowledge and produce scenarios.

3.1.1. Knowledge classification

Knowledge classification begins with a rough investi-

gation and portfolio planning for organizational knowledge

resources. It is usually based on interviews, literature

survey, and feedback. As a result, a variety of knowledge

candidates may be produced. These knowledge candidates

can be categorized into six types proposed by Holsapple and

Whinston (1987), as shown in Table 3. This classification is

useful for capturing knowledge (Mirchandani & Packath,

1999; Wiig, 1995). Primary knowledge is mainly concerned

with the knowledge contents such as systematic descriptive

data, rules, and procedures, while secondary knowledge is

concerned with the design of sensory manifestation for

users. Secondary knowledge can be extracted from primary

knowledge and thus is likely to be more volatile.

For medical practitioners, patient cases and clusters are

useful knowledge objects; a patient case includes knowl-

edge about patient’s medical history, social history,

symptoms, physical examination, lab tests, diagnoses,

treatment, and outcomes, while a patient cluster consists

of patient cases having common clinical features (Hsu &

Ho, 1999). Patient cases are clustered so that they may have

statistical and practical significance (Kushniruk, Patel, &

Marley, 1998). Patient clusters are useful in tracing

pathological causes in a massive level.

In AMC case, knowledge objects for patient cases and

clusters can be categorized into six knowledge types as

shown in Table 4. In order to identify knowledge objects,

the project members interviewed four doctors in internal

Table 2

System components in implementation architecture

Implementation layer System component Description

ODS/DW ODS/DW Structured organization of knowledge contents

Ownflow Aggregation, summarization, computation, purge, and archive procedures

Package Meaningful group of ODS/DW components

Linkage Inflow Procedural implementation for feeding data from external sources to ODS/DW layer

Outflow Procedural implementation for the creation of hypermedia presentational items

Hypermedia presentation User view Customized partition for visualization based on knowledge presentational scheme

Ownflow Dynamics and interaction procedures based on knowledge navigational logic

Document Meaningful group of user view components

Table 3

Knowledge types

Category Description

Primary

Descriptive Information about actual or

possible occurrences related to

decision-making situation—‘knowing what’

Procedural Step-by-step procedures for accomplishing

tasks—‘knowing how’

Reasoning Development of valid conclusions

under a certain circumstance—‘knowing why’

Secondary

Presentation Sensory manifestation of knowledge

for external storage or

transmission

Linguistic Interpretation of communication received

Assimilative Information for maintaining knowledge

relationship

Table 4

Knowledge classification table (KCT) for AMC case

Category Knowledge object

Descriptive Symptom; physical finding; lab

finding; preliminary diagnosis; confirmed

diagnosis; therapeutic decision; pathogenesis;

estimated prognosis; outcome measurement;

cluster

Procedural Hypothesis test procedure; estimation;

clustering/aggregation; measuring outcome; therapy

development

Reasoning Conditional relationship between findings

and diagnostic disease; causal

relationship between pathogenesis and

diagnostic disease; response relationship

between therapy and outcome

Presentation Care pathway; time trend

of observation result; medication

history; patient cluster; relationship

among observation result and

age; frequency distribution for

a variable

Linguistic Computational logic and procedure

for transformation, data mapping,

and extraction

Assimilative Association of subjective findings;

association of objective findings;

diagnostic assessment; group of

care history; collection of

cluster statistics; care time

series; historical index of

care history; observation item

index; medication item index;

patient index; exponential moving

average computation logic; structural

knowledge aggregation/decomposition logic; regression

logic; frequency computation logic;

time series analysis logic


medicine and two doctors in surgery. In addition, they

analyzed the format of clinical writings such as case reports

and medical literature. These knowledge objects are likely

to be used in combination with other clinical decision

models for diagnosis, prescription, or prognosis (Velde,

2000).

3.1.2. KME design

This step investigates KM activities and produces

scenarios from KCT. Ongoing activities can illustrate

knowledge in a coherent and comprehensive way; several

methods such as knowledge flow analysis (Wiig, Hoog, &

Spek, 1997) or knowledge chain model (Holsapple & Singh,

2001) may be adopted. Our methodology employs KME

analysis. This KME analysis is better able to manipulate

knowledge (Holsapple & Joshi, 2001).

For this analysis, a business value chain model (Porter,

1985) with seven basic functions (as shown in Table 5) can

be used (Holsapple & Singh, 2001). This model has been

widely applied for leveraging competitive advantages.

KME is produced on the basis of KCT. Here, KM activities

for a specific business function are explored in detail. For

example, Table 6 shows two KMEs for service delivery.

Each KME has user activities, system activities, and

knowledge objects. KMEs were made through observation,

literature survey, and interview with doctors. In AMC

project, total 96 KMEs are prepared for seven basic

functions through interview with 24 practitioners from 17

departments. System activities help define the functionality

of medical DW.

In summary, KMEs are produced according to the

following procedure. First, a KME list is prepared to

accomplish specific business functions identified in the

value chain model. Next, for each KME, user KM activities

are explored in association with knowledge objects in KCT.

Finally, system activities are identified for implementation.

3.2. Phase 2: hypermedia modeling

This phase presents knowledge artifacts by the use of

hypermedia model.

3.2.1. Hyperspace analysis

Hyperspace can be defined as a structured space with

Table 5

Business value chain model

Category Business function

Primary activity Inbound logistics

Service delivery

Marketing and sales

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Human resource management

Technology development

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nodes and links; in a well-structured hyperspace, decision

makers are able to define authoritative pieces of knowledge

using the hypermedia artifacts (Nanard & Nanard, 1995;

Vanharanta, Kakola, & Back, 1995).

A node has been conceived as a unit of information

(Nielsen, 1993) or a navigational unit (Suh & Lee, 2001);

nodes can be categorized into four types as shown in Table

7. Representation nodes are sources for presentation and

association nodes; i.e. presentation and association nodes

are volatile. A presentation node is a unit of an entity for

user interaction. Association nodes implement a set of

anchors for navigation. A composite node is a container-like

mechanism that enables grouping. A link is a navigational

relationship among nodes; these relationships can have

several mechanisms for navigation. According to these

mechanisms, links can be divided into four types (Table 7).

Each node and link corresponds to a particular knowl-

edge type except for the assimilative knowledge. The

assimilative knowledge can be transformed into association

node, composite node, or navigation link.

For our AMC project, nodes and links are identified as

shown in Table 8. Knowledge objects in Table 4 are mapped

into nodes and links based on mapping relationship in

Table 7.

3.2.2. Internal conceptual design

This design step attempts to represent knowledge

contents. The internal conceptual model is a graphical

presentation for nodes and links captured in NLL. Fig. 3

depicts graphical notations used for internal conceptual

design and external navigational design. Fig. 4 shows the

hypermedia model for our AMC case. The left part

corresponds to the internal conceptual model; the right

part corresponds to the external navigation model.

Table 7

Relationship between node/link types and knowledge types

Classification Description Related knowledge

Node

Representation An internal form of information for inference, computation, and internal storage Descriptive

Presentation A sensory and perceptible manifestation of information Presentation

Association A presentation of the hypermedia network structure by grouping related anchors Assimilative

Composite Group of related nodes for aggregation Assimilative

Link

Reference Static relationship among nodes Reasoning

Transformation (intra) Link which contains computational logic producing another knowledge in homogeneous layer Procedural

Transformation (inter) Link between heterogeneous layers Linguistic

Navigation Link which helps navigate through locations in a hypermedia network Assimilative

Table 8

Node and link list (NLL) for AMC case

Category Node and link

Node

Representation Symptom node; PE node;

lab data node; preliminary

diagnosis node; confirmed diagnosis

node; therapy node; pathogenesis

node; prognosis node; outcome

node; cluster node

Presentation Care pathway node; observation

time trend node; medication

history node; patient cluster

node; regression of observation

node; frequency distribution node

Association Care history time table

node; care history index

node; observation list node;

medication list node; patient

list node

Composite Subjective finding node; objective

finding node; assessment node;

cluster analysis node; care

grouping node

Link

Reference Conditional link; causal link;

response link

Transformation (intra) Aggregation/clustering link; estimation link;

hypothesis test link; measurement

link; treatment development link

Transformation (inter) Extraction/transformation/loading (ETL) link

Navigation Moving average link; time

spread link; drill-up/down link; structural link;

regression link; frequency computation

linkFig. 3. Notations for hypermedia modeling.


KMEs can help build the model. For example, the

episode ‘disease assessment based on findings’ in Table 6

includes 10 knowledge objects. These objects belong to four

knowledge types such as descriptive, procedural, reasoning,

and assimilative knowledge. Accordingly, five correspond-

ent representation nodes such as ‘symptom’, ‘PE’, ‘lab

data’, ‘preliminary diagnosis’, and ‘confirmed diagnosis’

are depicted at first. Assimilative knowledge are used to

group representation nodes and depicted as composite node.

Finally, user and system activities for the KME can be

employed for relating nodes with links.

For designing the internal conceptual model, five KMEs

are employed. They are disease assessment based on

findings, ‘pathogenesis tracing’, ‘therapy development’,

‘prognosis estimation’, and ‘outcome evaluation’. First, 10

representation nodes in Table 8 are depicted to describe five

KMEs. Some of representation nodes are grouped into

‘object finding’, ‘subject finding’, and ‘assessment’ compo-

site nodes, which correspond to knowledge for association.

User and system activities in KME are used for relating

nodes with links. For example, ‘generating confirmatory

diagnosis’ in disease assessment based on findings provides

a basis for relating preliminary diagnosis and confirmed

diagnosis nodes with ‘hypothesis test’ link. Likewise,

‘objective finding’ and ‘subjective finding’ composite

nodes have ‘conditional’ relationships with the assessment

composite node. The assessment node also has a ‘causal’

relationship with the ‘pathogenesis’ node. Based on

assessment node, ‘prognosis’, ‘therapy’, ‘cluster’ nodes

are created using the ‘estimation’, ‘aggregation/clustering’,

and ‘treatment development’ intratransformation links,

respectively. Assessment, objective finding, subjective

finding nodes all affect the ‘outcome’ node via ‘measure-

ment’ link.

3.2.3. External navigation design

This design step incorporates navigational logic for user

interaction. This logic results in the external navigation

model. Furthermore, this step integrates the navigational

model with internal conceptual model by the use of

intertransformation links.

To design external navigation model, two KMEs,

‘intensive review of patient condition’ and ‘epidemic

study on a patient group’, are employed. Six presentation

nodes are used for explaining these two KMEs. Major nodes

in the external navigation model are ‘care pathway’ and

‘patient cluster’, which express patient case and cluster,

respectively. They are made by the ‘ETL’ intertransforma-

tion link containing the logic for fragmentation and

reassembly of representation nodes. Based on user and

system activities in KMEs, association nodes and navigation

links are added to the model. ‘Observation regression’ and

‘frequency distribution’ nodes are obtained by the use of

‘regression’ and ‘frequency computation’ links on the

Fig. 4. Integrated hypermedia model.


‘patient cluster’, respectively. In addition, ‘observation time

trend’ and ‘medication history’ nodes are produced from the

‘care pathway’ node in a similar way. ‘Drill-down’ and

‘structural query’ links with association nodes are used to

design various navigation methods such as indexed tour,

indexed-guided tour, and query-indexed tour.

3.3. Phase 3: system implementation

This phase designs components and implements DW

system.

3.3.1. System component design

This step transforms hypermedia modeling artifacts

into the system. This transformation rule is explained in

Table 9.

ODS/DW components transformed from representation

nodes maintain knowledge contents. Ownflow components

in the ODS/DW layer from the reference or transformation

links define the computational algorithms such as aggrega-

tion or clustering. The package acts as a meaningful group

of components, relationships, or computations. Inflow

components feed data into ODS/DW components. Outflow

component contains linkage logics for the integration of

ODS/DW and hypermedia presentation layer. The user view

components are customized partitions for better presen-

tation. Ownflow components in the hypermedia presentation

layer contain navigational logic. The documents specify

how the knowledge and its links are presented in a

consistent manner.

Our hypermedia model results in system components as

shown in Table 10. For example, representation nodes such

as symptom, PE, lab data, pathogenesis, preliminary

diagnosis, confirmed diagnosis, prognosis, cluster, therapy,

and outcome in Fig. 4 are transformed to ODS/DW

components. Similarly, composite nodes such as assess-

ment, objective finding, subjective finding, ‘cluster analy-

sis’, and ‘care grouping’ are transformed into package or

document components.

3.3.2. Component implementation

This step implements components in SCL. For our DW

system, 10 ODS/DW, seven ownflow and three package

components were implemented. ODS/DW and ownflow

components were implemented through Sybase IQ tables

and stored procedures, respectively. Sybase IQ database

scheme provides implementation methods for package

components. In addition, the hypermedia system includes

12 views, seven ownflows, and two document components

in the hypermedia presentation layer as well as one

Table 9

Relationship between system component and hypermedia modeling artifact

Implementation layer System component Hypermedia modeling artifact

ODS/DW ODS/DW Representation node

Ownflow Reference and intratransformation link

Package Composite node

Linkage Inflow Intertransformation link

Outflow

Hypermedia presentation User view Presentation and association node

Ownflow Navigation link

Document Composite node

Table 10

System component list (SCL) for AMC case

Category Component

ODS/DW layer ODS/DW component Symptom; PE; lab data; preliminary diagnosis; confirmed diagnosis;

therapy; pathogenesis; prognosis; outcome; cluster

Ownflow component Integrity check; matching; aggregation; clustering; estimation;

hypothesis test; treatment development

Package component Assessment package; physical finding package; lab finding package

Linkage layer Inflow component Extraction, transformation, and load component

Outflow component

Hypermedia presentation layer User view component Care pathway view; observation time trend view; medication history view;

observation list view; medication list view; care history index view;

timetable view; patient list view; patient cluster view; regression view;

frequency distribution view

Ownflow component Moving average generation; time series analysis; drill-up; drill-down;

structural query; regression; univariate distribution generation

Document component Cluster analyzer; care grouper


procedural component in the linkage layer. View and

document components were developed in the form of

ActiveX control and document components. Procedures in

the presentation layer were built as ActiveX DLL

components while procedures in the linkage layer were

built as DLL and ASP components on the COMþ

framework. HTTP and DCOM were employed as network

protocol to integrate the presentation layer with the linkage

Fig. 5. Care grouper document.

Fig. 6. Cluster analyzer document.


layer. Their communication is possible by the use of a

DBMS vendor specific protocol on TCP/IP.

For example, Figs. 5 and 6 highlight two document

components and 11 view components in the finally

implemented system. Fig. 5 shows the care grouper

document. It contains view components such as care

pathway (A), observation time trend (B), medication history

(C), observation list (D), medication list (E), care history

index (F), and timetable (G). This document enables

medical practitioners to evaluate a patient care history. It

shows chronic or visual presentation of patient’s medical

records for disease, consultation record, operation, blood

transfusion, and medication.

Fig. 6 shows the cluster analyzer document with user

view components such as patient cluster (A), frequency

distribution (B), patient list (C), and regression (D). Cluster

analyzer presents characteristics of patient groups visually.

It helps test hypothesis imposed on patient groups by

providing mean difference, variance ratio, and other useful

statistics.

4. A mapping rule

Here, we summarize a mapping rule for design elements

in the three major phases of our methodology as shown in

Fig. 7. This mapping highlights the transformation of

knowledge into system components via hypermedia model-

ing. Clearly, the hypermedia model helps link knowledge to

data warehousing system in a systematic fashion. Keeping

an eye towards this integration alleviates the design efforts

by relating each component to an overall enterprise view

through commonalities. Reusability is possible because the

design elements are connected. The redundancy is elimin-

ated because this common set of elements is used

throughout the development.

5. Comparison of hypermedia design methodologies

For the comparison of hypermedia design method-

ologies, we adopt evaluation criteria of Garzotto et al.

(1995). These criteria can accommodate critical features of

hypermedia design. Previous hypermedia design method-

ologies are compared with KHDM as shown in Table 11.

Previous methodologies adopt two popular contents

structuring techniques such as entity-relationship (E-R)

and object-oriented (O-O) model. RMM (Balasubramanian

et al., 1994; Isakowitz et al., 1995, 1997) and VHDM (Lee

et al., 1999a) employ E-R models while EORM (Lange,

1994, 1996), OOHDM (Schwabe & Rossi, 1995a,b), and

SOHDM (Lee et al., 1999b) employ O-O models. E-R-based

methodologies are useful for presentation-oriented appli-

cations; O-O models can provide rich semantics for

computation-intensive applications.

For user interaction, it is important to determine

presentational units and design their relationships. Although

all these methodologies employ the concept of user view for

the presentational unit, they use different determination

mechanisms and terminologies. Dynamics of applications is

Fig. 7. A mapping rule.


represented in the form of object interactions in the O-O

design methodologies or view/document relationships in

other methodologies.

In sum, KHDM differs in the following perspective.

First, the emphasis of KHDM is on knowledge reuse while

others focus on process or data requirements. Second, most

methodologies borrow design primitives from conventional

contents organization techniques such as E-R or O-O

models. In contrast, KHDM adopts customized hypermedia

design artifacts to accommodate a variety of knowledge

requirements. Third, KHDM and SOHDM collect user’s

navigational requirement in the form of operating scenarios.

These scenarios can help capture dynamic requirements

more easily. Fourth, KHDM provides systematic rules for

linking knowledge to the implementation system. This

linkage enables the customization of design artifacts and

thus supplies rich semantics for leveraging knowledge.

6. Conclusion

A variety of methodologies for hypermedia or DW

systems have been proposed. However, most of them are

not well suited for developing knowledge-intensive

applications.

Our proposed methodology puts an emphasis on (i)

analyzing corporate knowledge requirements and convert-

ing them into hypermedia artifacts (nodes and links) and (ii)

transforming these artifacts into system components. The

methodology supports a step-by-step migration from

conceptual knowledge to system elements. For its

implementation, this paper proposes a tailored DW

architecture including the hypermedia presentation layer.

To demonstrate the feasibility of our methodology, a

real-life medical application is illustrated. The methodology

is better able to help analyze and develop a knowledge-

intensive warehousing system. To enhance the practical

usefulness of our methodology, we are in the process of

developing a CASE tool. In addition, a metadata scheme

may be incorporated into our methodology for the reuse of

design knowledge. Reusability is possible because design

artifacts are interconnected.

Acknowledgements

This research was partially funded by the Korean

Ministry of Commerce, Industry, and Energy (Project ID:

A00-981-3302-09-2-2).

References

Balasubramanian, P., Isakowitz, T., & Stohr, E. A. (1994). Designing

hypermedia applications. Proceedings of the 27th Hawaii International

Conference on Systems Sciences (pp. 354–365).Tab

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