Hypermedia modeling for linking knowledge to data warehousing system
Jongho Kim
a, Woojong Suh
b, Heeseok Lee
a,*
aGraduate School of Management, Korea Advanced Institute of Science and Technology (KAIST), 207-43, Chongryangri-dong, Dongdaemun-gu, Seoul 130-012, South Korea
b
Division 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. q2002 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 knowlknowl-edge 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
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* 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).
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 knowlKnowl-edge objects according to a knowledge classification scheme byHolsapple 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, componentizgener-ation, and structured packaging.
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 inTable 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 inTable 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 COMþ 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.
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 byHolsapple and Whinston (1987), as shown inTable 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;
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 inTable 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
Secondary activity Corporate infrastructure
Human resource management Technology development Procurement Table 6 An exam ple for K MEs Knowl edge manage ment episode KM activit y Knowl edge objec t User Sys tem Disease assessm ent base d o n find ings Exami ning a patie nt’s phys ical stat us and sym ptom St oring PE and sympt om Prelim inary diag nosis; confirmed diag nosis; diag nostic asse ssment; sympt om; associ ation of subjecti ve find ings; phys ical find ing; lab find ing; associ ation of obje ctive findin gs; hypo thesis test pr ocedure; condition al relationship between find ings and diagnost ic dis ease Prelim inary diag nosis of patie nt’s diseas e name Ent ering dis ease nam e Coll ecting lab find ings and testin g condi tional rel ationsh ip with preli minary diag nostic hypo thesis St oring lab data and final assessm ent Gene rating confir matory diag nosis Epidem ic stu dy on a patie nt group Exami ning mu ltidimensi onal aspe ct of a spec ific cluster Pat ient sampli ng; group ing; aggregat ion Patient clust er; rela tionsh ip among observat ion res ult and ag e; frequ ency distrib ution for a variable; patient index; colle ction of cluster stat istics; regres sion log ic; freque ncy co mputati on log ic Reaso ning observat ion and age relati onship fo r a specific cluste r Regre ssion analy sis of age and a specific obser vation Real izing frequency dis tribution on a spec ific variabl e V isually dis tributing numerical observat ion on a certain ax is Searchi ng for specific pa tient care his tory D rilling down and present ing a patient ’s care pathwa y
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 inTable 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 knowlknowl-edge. 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 inTable 8. Knowledge objects inTable 4are 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 link
KMEs can help build the model. For example, the episode ‘disease assessment based on findings’ inTable 6
includes 10 knowledge objects. These objects belong to four knowledge types such as descriptive, procedural, reasoning, and assimilative knowledge. Accordingly, five correspond-ent represcorrespond-entation 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 inTable 8are 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 fragmentaintertransforma-tion 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
‘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 inTable 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
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.
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 inTable 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
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).
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