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DSS News
D. J. Power, Editor
March 25, 2007 -- Vol. 8, No. 6
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* Ask Dan: What are the features of a knowledge-driven DSS?
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Ask Dan!
What are the features of a knowledge-driven DSS?
by Dan Power
Editor, DSSResources.com
This is installment #4 about features of computerized decision
support systems. The focus is on knowledge-driven Decision Support
Systems (KDSS). The goal is to identify major, observable aspects,
attributes or elements from a user's perspective that distinguish
knowledge-driven DSS from other types of DSS (Power, 2002) and from
other computerized information systems.
In general, a knowledge-driven DSS suggests or recommends actions to
targeted users. This type of DSS has specialized problem-solving
expertise relevant to a specific narrow task. The "expertise"
consists of knowledge about a particular problem domain,
understanding of problems within that domain, and "skill" at solving
one or some of these problems. The system has subject-specific
knowledge of one or more human experts. These systems have also been
called advisory systems, consultation systems, suggestion systems
(Alter, 1980), knowledge-based systems (Klein and Methlie, 1995),
recommender systems, rule-based DSS, and management expert systems.
Knowledge-driven DSS can store and apply knowledge for a variety of
specific problems/tasks that would otherwise be resolved by a human
expert. The generic tasks include classification, configuration,
diagnosis, interpretation, planning and prediction. Historically,
diagnosis has been the most popular DSS application area. Building a
specific knowledge-driven DSS depends upon the demand for the system
and the anticipated benefits. As with all DSS, the goal is supporting
a human decision maker in completing the task, rather than replacing
the decision maker. The programming and development tools used to
build these systems are from Artificial Intelligence and Statistics.
The systems may be rule-based, statistics-based, heuristic,
object-based, logic-based, or induction based. Some systems use more
than one technology
Classification involves separating a specific instance into a broader
class based upon characteristics. Configuration involves creating an
arrangement of objects given performance criteria or constraints.
Diagnosis involves hypothesizing a cause given symptom and
situational information. Interpretation refers to adding meaning,
explanation and possibly understanding in a specific situation or
context. Planning usually involves sequencing an assortment of
actions or means to achieve desired ends in a constrained situation.
Finally, prediction refers to identifying and forecasting a future
state of a system.
For many years, I have been interested in building this type of DSS
(cf., Power, 1985). The major barrier to making progress has been
associated with deployment of the DSS to make the knowledge readily
available more so than with resistance to the use of such systems. In
recent years, Web technologies and hand-held and tablet PCs have made
deployment of knowledge-driven DSS much easier and much less
expensive.
Case study examples of knowledge-driven DSS at DSSResources.com
include Huntington (2006), Biss (2002), Exsys Staff (2002) and Pontz
and Power (2002). Huntington, CEO at Exsys, explains how the U.S.
Small Business Administration is using his company's product to share
knowledge using the Web. Biss reports a prototype medical decision
support system built using Bayesian technology from Hugin
(www.hugin.com). The EXSYS Staff case reports a project to support
families in making decisions about overseas staff assignments. The
Pontz and Power case reports building a knowledge-driven DSS called
EASE at the Pennsylvania Department of Labor and Industry.
Two recent press releases at DSSResources.com document a number of
significant developments. On October 10, 2006, Exsys announced it was
adapting its Corvid® software to run on HP iPAQ pocket PCs and
other personal digital assistants (PDAs). The "new capability enables
companies to capture and provide decision support expertise on
handheld mobile devices in a wide variety of areas including
diagnostics, sales support and regulatory compliance." On January 10,
2006, Epocrates Inc., provider of mobile and desktop clinical
applications, announced the launch of the Epocrates SxDx(TM) disease
diagnosis and treatment reference and symptom assessment tool. The
application, developed in collaboration with Massachusetts General
Hospital's Laboratory of Computer Science, "provides intelligent
decision support to clinicians throughout the diagnosis process.
Developed for mobile devices, including personal digital assistants,
the new product allows healthcare professionals to enter patient
symptoms and findings to generate a clinically useful diagnosis index
to the Epocrates disease reference."
The following are major features of knowledge-driven DSS from a
user's perspective:
1) Asks questions. Historically knowledge-driven DSS attempt to
create an interactive dialogue with users that simulates an
interrogation by a "real" expert. A key feature is interactivity with
the user and contingent branching based upon responses. In some
systems, the initial questions may be implied on an input form.
2) Backtrack capability. Users can often move backward through the
questions and alter responses. This feature makes it easy to go back
over the course by which one reached a result and then change a
subjective judgment and change a recommendation/result. In some
systems it may be useful to record that backtracking occurred.
3) Display confidence or certainty information. Numeric values called
confidences, likelihoods or ranks can be calculated in some systems. A
confidence interval is a statistical range with a specified
probability that a given result lies within the range. The DSS may be
able to create a confidence interval for a recommendation or
diagnosis. When these capabilities are present in the design and
development environment, a user can display the confidence
information.
4) Explain HOW. After a knowledge-driven DSS has reached a solution
or conclusion for the problem, the user can often request an
explanation of how the solution/conclusion was reached. This is one
of the most powerful features and is commonly present in a
knowledge-driven DSS. This feature can enhance user confidence in the
recommendation and hence acceptance of the system.
5) Explain WHY. Users should be able to ask why the system is asking
a specific question. This feature helps explain the process to the
user and enhances the user's expertise.
6) Initiate actions. In some knowledge-driven DSS, users can send an
email and or otherwise implement a recommendation. A good example of
this feature is in Pontz and Power (2002). Claims examiners using
EASE can send unemployment claims determinations directly to a
centrally located printer where the determination is inserted
automatically into an envelope with the appropriate postage.
7) Output selection. In addition to displaying confidence
information, some knowledge-driven DSS have multiple formats for
displaying outputs. For example, it may be possible to display a
diagram of a configuration or a graphical depiction of a plan.
8) Resume analysis. In some situations it may be desirable to pause
during an analysis and resume the analysis at a later point in time.
The system needs to display active and incomplete analyses.
9) Retrieve data about a specific case or instance. Data used in some
knowledge-driven DSS may come from other computerized sources and the
KDSS user must be able to retrieve data from external sources. For
example, a diagnostic system may retrieve information about a patient
from laboratory tests or a configuration system may need data from an
inventory system. When this feature is implemented, users also need
access to data source field definitions.
10) Store inputs, results and user actions. This may be a
discretionary or mandatory feature of the system. If the feature is
mandatory the user should be aware that data is being captured by the
system for monitoring and control purposes.
11) Train users. Some KDSS have sample or test cases to help train
users for future use of the system in actual situations. The KDSS
would then have a feature to start a training or tutoring mode.
Please note: Decisions made using knowledge-driven DSS can be biased
by the responses provided by the user. Potentially a KDSS can reduce
the risk of human errors in well-structured situations where experts
perform better than novices, but novice users must avoid distorting
or biasing responses to KDSS questions. Decision making is a
cognitive process and knowledge-driven DSS attempt to support and
enhance a person's reasoning and thinking in a specific narrow
domain. If novice users begin using a KDSS with a preconceived notion
or conclusion, the confirmation bias phenomenon will often result in
biased responses to confirm the prior decision, conclusion or
hypothesis.
The prospects and benefits for managing knowledge and supporting
decision making using knowledge-driven DSS is evolving. KDSS can
increase the distribution of expertise; broaden job descriptions for
individual workers; and create a new communication channel for
knowledge. As Pontz and Power (2002) suggest, using a KDSS can result
in more consistent decisions and can create efficiencies and reduce
the time needed to solve problems. KDSS can also reduced training
costs and rapidly disseminate and update large amounts of knowledge.
Finally, KDSS can help centralize control of repetitive, structured
decision making processes.
As always your comments, questions and suggestions are welcomed.
References
Alter, S.L. Decision Support Systems: Current Practice and Continuing
Challenge. Reading, MA: Addison-Wesley, 1980.
Biss, Andrew, "Dynasty Triage Advisor Enables Medical Decision
Support", 2002, posted at DSSResources.COM December 21, 2002.
EXSYS Staff, "IAP Systems Using Exsys CORVID Expert System
Software to Support Corporate Families on Overseas Assignments",
EXSYS, Inc., 2002, posted at DSSResources.COM May 12, 2002.
Georgia Tech Research Institute Staff, "Assistance to the
Emergency Response Community", Georgia Tech Research Institute,
2001.
Huntington, D., "From Information to Answers: Transferring
Expertise at the SBA", posted at DSSResources.com, November
3, 2006.
Klein, M. and L. B. Methlie, Knowledge-based Decision Support
Systems with Applications in Business. Chichester, UK: John
Wiley & Sons, 1995.
Pontz, C. and D. J. Power, "Building an Expert Assistance
System for Examiners (EASE) at the Pennsylvania Department
of Labor and Industry", November 2002, posted at
DSSResources.COM November 14, 2002.
Power, D.J., Using the Symptoms, Problems and Treatment
Framework to Structure Knowledge for Management Expert
Systems, In: Methlie, L. and Sprague, R. (Ed.), Knowledge
Representation for Decision Support Systems, New York:
North-Holland Publishing Co., 1985, pps. 245-254.
Power, D. Decision Support Systems: Concepts and Resources for
Managers, Westport, CT: Quorum/Greenwood, 2002.
Press Releases
Epocrates, "Epocrates collaborates with Massachusetts General
Hospital to offer unique decision support tool,"
http://dssresources.com/news/1216.php, 01/10/2006.
EXSYS, "On-the-go employees and customers can now access expert
corporate advice via handheld mobile devices,"
http://dssresources.com/news/1653.php, 10/10/2006.
Web links
http://www.era.lib.ed.ac.uk/bitstream/1842/654/2/saoud04.pdf
http://www.nait.org/jit/Articles/salim100402.pdf
http://www.planware.org/salepwe.htm
http://www.vanguardsw.com/technology/decision-analysis-methods/
expert-systems.htm
http://en.wikipedia.org/wiki/Decision_making
http://en.wikipedia.org/wiki/Expert_system
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DSS Conferences
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Check http://www.iscram.org .
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morning, May 18, 2007, check http://mis.uis.edu/MWAIS2007/ .
3. Crystal Ball User Conference, May 21-23, 2007 Denver.
Check http://www.crystalball.com/cbuc/index.html.
4. AMCIS 2007, Americas Conference on Information Systems,
Keystone, CO USA, August 9-12, 2007. SIG DSS mini-tracks.
Check http://www.biz.colostate.edu/amcis07/ .
5. DaWaK 2007, 9th International Conference on Data
Warehousing and Knowledge Discovery, Regensburg, Germany,
September 3-7, 2007. Full papers due: April 13, 2007.
Check http://www.dexa.org/ .
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