WEB-BASED DECISION SUPPORT SYSTEMS
AS KNOWLEDGE REPOSITORIES FOR
KNOWLEDGE MANAGEMENT SYSTEMS
Minnesota State University Moorhead, USA
North Dakota State University, USA
Problem solving and learning processes conducted on the basis of contemporary Web-
based DSS provide for development and enhancement of knowledge management
systems. Knowledge objects form the foundation of the conceptual approach to the
knowledge management based on the contemporary Internet technologies and
knowledge accumulated in DSS.
Keywords: knowledge management systems, decision support systems.
1 INTRODUCTION popularity, particularly with businesses.
Collaborating on projects with co-workers across the
Knowledge management (KM) has become an world is easier, since information is stored on a Web
important theme as managers realize that much of server instead of on a single desktop.
their firm’s value depends on ability to create and Rich Internet Applications (RIAs) are Web
manage knowledge. To transform information into applications that offer the responsiveness, “rich”
knowledge a firm must use additional resources to features and functionality approaching that of
discover patterns, rules, and context where the desktop applications. RIAs are the result of today’s
knowledge works [1-3]. more advanced technologies (such as Ajax) that
Knowledge that is not shared and applied to the allow greater responsiveness and advanced GUIs.
practical problems does not add business value. Web services have emerged and, in the process,
Today people can share their knowledge in three have inspired the creation of many Web 2.0
primary ways. Organizational information systems businesses. Web services allow you to incorporate
(IS) that store, manage, and deliver documents are functionality from existing applications and Web
called content management systems (CMS). With the sites into your own applications quickly and easily.
arrival of modern communications technology, Web 2.0 companies use “data mining” to extract
people can share their knowledge via collaborating as much meaning as they can from XHTML-encoded
knowledge management systems (KMS). In addition pages. XHTML-encoded content does not explicitly
to content management and collaboration, the convey meaning, but XML-encoded content does. So
knowledge can be shared via expert systems. if we can encode in XML (and derivative
Comprehensive discussion of important dimensions technologies) much or all of the content on the Web,
of knowledge, the knowledge management value we’ll take a great leap forward towards realizing the
chain, and types of KMS can be found in [2, 3]. Semantic Web.
Web 2.0 companies use the Web as a platform Many people consider the Semantic Web to be
to create collaborative, community-based sites (e.g., the next generation in Web development, one that
social networking sites, blogs, wikis, etc.). The Web helps to realize the full potential of the Web – the
has now become an application, development, “Web of meaning”. Though Web 2.0 applications are
delivery, and execution platform . finding meaning in the content, the Semantic Web
Software as a Service (SaaS) - application (heavily depended on XML and XML-based
software that runs on a Web server rather than being technologies) will attempt to make those meaning
installed on the client computer – has gained clear to computers as well as humans .
These trends in the Web Science – the new combination of internally developed taxonomies and
science of decentralized information systems – search engine techniques.
provide for new opportunities in the KM. Organizations acquire knowledge in a number
In this paper we consider contemporary of ways, depending on the type of knowledge they
Decision Support Systems (DSS) as knowledge seek. Once the corresponding documents, patters, and
repositories that can be expanded to KMS using the expert rules are discovered they must be stored so
Web 2.0 software development technologies and they can be retrieved and used. Knowledge storage
tools. This paper is based on a series of previous generally involves databases, document management
authors’ publications [6-11]. systems, expert systems, etc. To provide a return on
investment, knowledge should become a systematic
2 KNOWLEDGE MANAGEMENT AND part of the organizational problem solving process.
DECISION SUPPORT SYSTEMS Ultimately, new knowledge should be built into a
firm’s business processes and key application
The AI representation principle states that once a systems.
problem is described using an appropriate KMS and related knowledge repositories should
representation, the problem is almost solved. Well- facilitate the problem solving process (Figure 1).
known knowledge representation techniques include During the process of solving problems managers
rule-based systems, semantic nets and frame systems engage into decision making, the act of selecting
. from alternative problem solutions.
KM refers to the set of business processes The different levels in an organization
developed in an organization to create, store, transfer (strategic, management, and operational) have
and apply knowledge. KM increases the ability of the different decision-making requirements. Decisions
organization to learn from its environment and to can be structured, semi-structured or unstructured.
incorporate knowledge into business processes. There The structured decisions are clustered at the
are three major categories of KMS: enterprise-wide operational level of the organization, and
KMS, knowledge work systems (KWS), and unstructured decisions at the strategic level.
intelligent techniques [2, 3]. Management information systems (MIS)
Enterprise-wide KMS are general purpose, provide information on firm performance to help
integrated, firm-wide efforts to collect, store, managers monitor and control the business, often in
disseminate, and use digital content and knowledge. the form of fixed regularly scheduled reports based
Such systems provide databases and tools for on data summarized from the firm’s transaction
organizing and storing structured and unstructured processing systems (TPS). MIS support structured
documents and other knowledge objects, directories decisions and some semi-structured decisions.
and tools for locating employees with experience in a DSS combine data, sophisticated analytical
particular area, and increasingly, Web-based tools for models and tools, and user-friendly software into a
collaboration and communication. single powerful system that can support semi-
KWS (such as computer-aided design, structured and unstructured decision making [3, 13,
visualization, and virtual reality systems) are 14].
specialized systems built for engineers, scientists, and The main components of the DSS are the DSS
other knowledge workers charged with discovering database, the user interface, and the DSS software
and creating new knowledge for a company. system (Figure 2). The DSS database is a collection
Diverse group of intelligent techniques (such as of current data from a number of applications and
data mining, neural networks, expert systems, case- groups. Alternatively, the DSS database may be a
based reasoning, fuzzy logic, genetic algorithms, and data warehouse that integrates the enterprise data
intelligent agents) have different objectives, from a sources and maintains historical data.
focus on discovering knowledge (data mining and The DSS user interface permits easy interactions
neural networks), to distilling knowledge in the form between users of the system and the DSS software
of rules for a computer program (expert systems and tools. Many DSS today have Web interfaces to take
fuzzy logic), to discovering optimal solutions for advantages of graphics displays, interactivity, and
problems (genetic algorithms). ease of use.
It is said that effective KM is 80% managerial The DSS software system contains the software
and organizational, and 20% technology. One of the tools that are used for data analysis. It may contain
first challenges that firms face when building various OLAP tools, data mining tools, or a
knowledge repositories of any kind is the problem of collection of mathematical and analytical models that
identifying the correct categories to use when easily can be made accessible to the DSS users.
classifying documents. Firms are increasingly using a
(Desired state) Problem
Figure 1: Elements of the problem solving process.
The dialog manager is also in charge for the and especially a time-series of internal company data
information visualization. Finally, access to the and sometimes external data. Relational databases
Internet, networks, and other computer-based systems accessed by query and retrieval tools provide an
permits the DSS to tie into other powerful systems, elementary level of functionality. Data warehouse
including the TPS or function-specific subsystems. systems that allow the manipulation of data by
There are many kinds of DSS. The first generic computerized tools tailored to a specific task and
type of DSS is a Data-Driven DSS. These systems setting or by more general tools and operations
include file drawer and management reporting provided additional functionality. Data-Driven DSS
systems, data warehousing and analysis systems, with Online Analytical Processing (OLAP) provide
Executive Information Systems and Spatial DSS. the highest level of functionality and decision support
Data-Driven DSS emphasize access to and that is linked to analysis of large collections of
manipulation of large databases of structured data historical data.
DSS Software System
Data Mining Tools
Figure 2: Main components of the DSS.
A second category, Model-Driven DSS, includes Document-Driven DSS are evolving to help
systems that use accounting and financial models, mangers retrieve and manage unstructured documents
representational models, and optimization models, and Web pages. A Document-Driven DSS integrates
and optimization models. Model-Driven DSS a variety of storage and processing technologies to
emphasize access to and manipulation of a model. provide complete document retrieval and analysis.
Simple statistical and analytical tools provide an WWW provides access to large document databases
elementary level of functionality. Some OLAP including databases of hypertext documents, images,
systems that allow complex analysis of data may be sounds and video. Examples of documents that would
classified as hybrid DSS providing modeling, data be accessed by Document-Driven DSS are policies
retrieval, and data summarization functionality. and procedures, product specifications, catalogs, and
Model-Driven DSS use data and parameters provided corporate historical documents, including minutes of
by decision-makers to aid them in analyzing a meetings, corporate records, and important
situation, but they are not usually data intensive. correspondence. Search engines are powerful
Very large databases are usually not needed for decision-aiding tools associated with Document-
Model-driven DSS. Driven DSS.
Knowledge-Driven DSS or Expert Systems can Group DSS (GDSS) came first, but now a
suggest or recommend actions to managers. These broader category of Communications-Driven DSS or
DSS are human-computer systems with specialized groupware can be identified. These DSS includes
problem-solving expertise. The expertise consists of communication, collaboration and related decision
knowledge about a particular domain, understanding support technologies. These are hybrid DSS that
of problems within that domain, and skills at solving emphasize both the use of communications and
some of these problems (AI algorithms and solutions decision models to facilitate the solution of problems
can be used). A related concept is data mining. It by decision-makers working together as a group.
refers to a class of analytical applications that search Groupware supports electronic communication,
for hidden patterns in a database. Data mining is the scheduling, document sharing, and other group
process of sifting through large amounts of data to productivity and decision support enhancing
produce data content relationships. Tools used for activities.
building Knowledge-Driven DSS are sometimes A DSS model that incorporates Group Decision
called Intelligent Decision Support methods. Support, OLAP, and AI is shown on Figure 3.
Relational Knowledge Multidimensional
Database Database Database
Relational Inference Multidimensional
DBMS Engine DBMS
Periodic Outputs from Outputs and from
and mathematical from explanations OLAP
special models groupware
Figure 3: A DSS model that incorporates GDS, OLAP, and AI.
DSS facilitate the decision-making. Decision Business logic (domain) layer that implements
making is an integrated part of the overall problem the rules and procedures of the business
solving process. KMS should facilitate the problem processing.
solving process. In the next section we are going to View layer that accepts input and formats and
discuss how Web-enabled DSS can be integrated into displays processing results.
contemporary KMS. RIA have two key attributes – performance and
rich GUI. RIA performance comes from Ajax
SYSTEMS client-side scripting to make Web applications more
responsive by separating client-side user interaction
All types of DSS can be deployed using Web and server communication, and running them in
technologies and can become Web-based DSS. parallel. Various ways to develop Ajax applications
Managers increasingly have Web access to data are discussed in .
warehouses and analytical tools. To discuss the Web services promote software portability and
recent trends in this area the latest achievements in reusability in applications that operate over the
the three-layer design, Rich Internet Applications Internet. Web service is a transition to service-
(RIA), and Web services should be taken into oriented, component-based, distributed applications.
account. Web services are applications implemented as Web-
Three-layer design is an effective approach to based components with well-defined interfaces,
development robust and easy maintainable systems. which offer certain functionality to clients via the
The corresponding architecture is appropriate for Internet. Once deployed, Web services can be
systems that need to support multiple user interfaces. discovered, used/reused by consumers (clients, other
Contemporary Web applications are three-layer services or applications) as building blocks via open
applications. industry-standard protocols. Web service architecture
The most common set of layers includes the is built on open standards and vendor-neutral
following: specifications. Services can be implemented in any
Data layer that manages stored data, usually in programming language, deployed and then executed
one or more databases. on any operating system or software platform.
provide access to
DSS Software System
Figure 4: Web-enabled DSS.
The service-oriented architecture (SOA) provides consisting of different software components working
the theoretical model for all Web services. The model together. Consuming Web services is based on open
behind Web services is a loosely coupled architecture, standards managed by broad consortia (e.g., World
Wide Web Consortium, Organization for the 2). RIA provide for efficient implementation of the
Advancement of Structured Information Standards, Dialog Manager GUI for DSS. Web services allow
Web Services Interoperability Organization). incorporating functionality from existing applications
What makes Web services different from ordinary and due to this providing for access to the DSS
Web sites is the type of interaction that they can Software System through the SOA. The components of
provide. Most of the enthusiasm surrounding Web the Web-enabled DSS are shown on Figure 4.
services is based on the promise of interoperability. We can call a group of the following related
Every software application in the world can potentially components a knowledge object (Figure 5). Discussed
talk to every other software application. This techniques allow to create new Web services (based on
communication can take place across the old the existing ones and contemporary DSS software
boundaries of location, operating system, language, systems), and Ajax-enabled application interacting with
protocol, and so on. these Web services. So we can talk about creation and
Three-layer architecture maps well on the modification of the knowledge objects.
structure of main components of the DSS (see Figure
Figure 5: Structure of a knowledge object.
Web-enabled DSS provide for expandable built up over the years. This organizational knowledge
collections of the knowledge objects that constitute the can be captured and stored using case-based reasoning
knowledge repository of the corresponding KMS. From (CBR). In CBR description of the past experiences of
this point of view the knowledge objects can be human specialists, represented as cases, are stored in a
considered as a knowledge representation technique. database for the later retrieval when the user encounters
a new case with similar parameters. The system
4 PROBLEM SOLVING AND LEARNING searches for stored cases with problem characteristic
similar to the new one, finds the closest fit, and applies
AI distinguishes two general kinds of learning. The the solution of the old case to the new case. Successful
first kind is based on coupling new information to solutions are tagged to the new case and both are stored
previously acquired knowledge. Typical examples together with the other cases in the knowledge base.
include learning by analyzing differences, by managing Unsuccessful solutions are also appended to the case
multiple models, by explaining experience, and by database along with explanations as why the solutions
correcting mistakes. The second kind is based on did not work.
digging useful regularity out of data; a practice often Problem-based learning (PBL) is (along with active
refers as data mining. Typical examples include learning and cooperative/collaborative learning) one of
learning by recording cases, by building identification the most important developments in contemporary
trees, by training neural nets, by training perceptrons, higher education. PBL is based on the assumption that
by training approximation nets, and by simulation human beings evolved as individuals who are motivated
evolution (e.g. genetic algorithms). to solve problems, and that problem solvers will seek
Expert systems primarily capture the tacit and learn whatever knowledge is needed for successful
knowledge of individual experts, but organizations also problem solving. PBL is a typical example of an
have collective knowledge and expertise that they have
application of the first type of learning in higher Combining the main ideas of CBR and PBL the
education . following problem solving and learning process can be
depicted as it’s shown on Figure 6.
User describes User learns
the problem about the knowledge objects
the problem solving
Repository of Repository of
knowledge objects for knowledge objects
the suitable ones (based on a
System asks user
to narrow search
System finds the problem description
the closest fit and and the knowledge
provides access to object in the repository
New knowledge object
is created to
better fit the problem
Figure 6: Problem solving and learning with knowledge objects.
5 CONCLUSIONS 6 REFERENCES
Knowledge is a complex phenomenon, and there are  V. Supyuenyong, N. Islam: Knowledge
many aspects to the process of managing knowledge. Management Architecture: Building Blocks and
Knowledge-based core competencies of firms are key Their Relationships, Technology Management for
organizational assets. Knowing how to do things the Global Future, Vol. 3, pp. 1210-1219 (2006).
effectively and efficiently in ways that other  K.C. Laudon, J.P. Laudon: Management
organizations cannot duplicate is a primary source of Information Systems. Managing the Digital Farm,
profit and competitive advantage that cannot be Prentice Hall, pp. 428-508 (2006).
purchased easily by competitors in the marketplace.  R. McLeod, G. Schell: Management Information
This paper discusses Web-enabled DSS, related Systems, 10th Edition, Prentice Hall, pp. 250-274
knowledge repositories, and KMS that facilitate the (2006).
problem solving and learning. The knowledge objects  P.J. Deitel, H.M. Deitel: Internet and World Wide
approach to the knowledge representation allows Web. How to Program, 4th Edition, Prentice Hall,
considering contemporary DSS as integrated parts of pp. 50-117 (2008).
the corresponding KMS.  T. Berners-Lee, et al: A Framework for Web
Science, Foundations and Trends in Web Science,
Vol. 1, No 1, pp. 1-130 (2006).
 Y. Boreisha, O. Myronovych: Web-Based Decision  Y. Boreisha, O. Myronovych: Knowledge
Support Systems in Knowledge Management and Navigation and Evolutionary Prototyping in E-
Education, Proceedings of the 2007 International Learning Systems, Proceedings of the E-Learn
Conference on Information and Knowledge 2005 World Conference on E-Learning in
Engineering, IKE’07, June 25-28, Las Vegas, Corporate, Government, Healthcare, and Higher
USA, pp. 11-17 (2007). Education, October 24-28, Vancouver, Canada, pp.
 Y. Boreisha, O. Myronovych: Web Services-Based 552-559 (2005).
Virtual Data Warehouse as an Integration and ETL  P.H. Winston: Artificial Intelligence, Addison-
Tool, Proceedings of the 2005 International Wesley, pp. 15-228 (1992).
Symposium on Web Services and Applications,  S. French, M. Turoff: Decision Support
ISWS’05, June 27-30, Las Vegas, USA, pp. 52-58 Systems, Communications of the ACM, Vol. 50,
(2005). No 3, pp. 39-40 (2007).
 Y. Boreisha, O. Myronovych: Data-Driven Web  Chien-Chih Yu: A Web-Based Consumer-
Sites, WSEAS Transactions on Computers, Vol. 2, Oriented Intelligent Decision Support System for
No 1, pp. 79-83 (2003). Personalized E-Services, ACM International
 Y. Boreisha: Database Integration Over the Web, Conference Proceeding Series, Vol. 60, pp. 429-
Proceedings of the International Conference on 437 (2004).
Internet Computing, IC’02, June 24-27, Las Vegas,
USA, pp. 1088-1093 (2002).
 Y. Boreisha: Internet-Based Data
Warehousing, Proceedings of SPIE Internet-Based
Enterprise Integration and Management, Vol. 4566,
pp. 102-108 (2001).