Object-Oriented Intelligent Mechanism
- Vital for the Success of E-Commerce
Myron Sheu, California State
University, Dominguez Hills, U.S.A.
Xin (James) He, Dolan School of Business, Fairfield
University, U.S.A.
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REFEREED
ARTICLE
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Abstract
Motivated by widespread applications of e-commerce, this paper addresses
the unique challenges of e-commerce by introducing object-oriented intelligence
to the user interfaces of e-commerce rather than by utilizing traditional
expert systems. Specifically, this research examines the intelligent
mechanism settings that are aimed largely at improving knowledge representation
of online transactions. In this paper, we, without loss of generality,
focus on online investment due to its complexity and popularity and
integrate a series of intelligent mechanism settings as a heuristic
intelligence system to make the online investment user-friendlier to online investors
and more affordable to online service providers.
1 INTRODUCTION
The Internet has made e-commerce increasingly more feasible and accessible.
However, the success of e-commerce depends not only on the infrastructure
underpinning the Internet but also on the user friendliness of the online
business environment that would profoundly affect the traditional shoppers’
attitude towards e-commerce. It is nevertheless challenging to balance
technological difficulties faced by online stores and user friendliness
sought by online customers. Some of the critical issues relevant to
e-commerce are highlighted below:
- Any attempts to facilitating such a business environment that requires
sophisticated reasoning methods would be viewed by customers who are
used to an intuitive shopping environment as non-user-friendly. Therefore,
these attempts are too technical to be considered practical in facilitating
online shopping.
- Most online shoppers expect to pay no more, if not less, than the
price that they would pay to a local traditional store. As a result,
it is financially infeasible for online storeowners to charge their
customers more than what traditional stores would charge in order
to offset any extra services provided by the online stores.
- Now that brevity and convenience are the inherent advantages of
e-commerce, complicated procedures with sophisticated tools and excessive
professional opinions would deprive these advantages from the online
shoppers.
Therefore, in this paper we first introduce a concept that infuses
intelligence to the user interfaces of e-commerce without having to
rely on the traditional expert systems. Then, based on this framework
we explore a series of elements that would effectively and efficiently
enhance the intelligence of e-commerce.
2 LITERATURE REVIEW
Although deploying a set of intelligent agents to monitor and integrate
vast volumes of dynamic information is helpful [Wang02],
improving the knowledge representation of e-commerce would help online
customers better understand the value of the products under consideration
and, consequently, more likely reach rational purchase decisions. Such
improved knowledge representation is badly needed as the number of alternatives
increase significantly [Jedets02]. Without loss
of generality, we focus our research efforts on the online investment
business environment due to its complexity and popularity. As the large
number of alternatives increase, the possibility for online investors
to make unwise decisions increase due to poor quality and limited availability
of relevant information to the investors. When a piece of information
is inaccurate, it would be worse than without it [Willia02].
At a typical online investment web site presently available on the Internet,
for instance, an online investor usually sees all the securities in
a tabular structure that merely lists primitive data and leaves the
digestion of data to the online shoppers. Such a tabular structure is
considered linear rather than composite, fragmented rather than granular,
and segregating rather than associative. Nonetheless, a comprehension
of measurable attributes on these securities, tailored by each individual's
financial needs, is much more important than the tabular structure because
the valuation of each security is perceived differently by various individuals.
As the information is disseminated to web sites in the tabular structure,
online customers may merely stare at the numbers without knowing how
to further process the data into meaningful information effectively
and efficiently. Consequently, if a potential online shopper cannot
correctly synthesize the data for his purchase decision, such data not
only possess little value, but also irritate that individual for he
may be victimized by the misinformation. Therefore, such data should
either be further processed to become comprehendible to the user or
be filtered out to avoid possible contradictions and redundancies [Benet02].
Hence, the ability to refine overwhelming financial data that constantly
come forward to the marketplace and represent them in a suitable manner
is critical to online investors. Moreover, online investors would want
to view the information represented at different levels of granularity
that is digestible and relevant to their concerns. Such preference subsequently
demands flexibility in representing the knowledge at a desirable resolution,
with various perspectives, and accessible to all the online end-users.
After all, suitable intelligent ingredients must be built into the
knowledge representation of an e-commerce web site such that it would
possess three desirable attributes: derivability, digestibility, and
integrity. By derivability, we mean that the knowledge is well organized
so that the whole representation facilitates an informed decision to
be readily derived. Digestibility is the intuitiveness of knowledge
representation perceived. Integrity is the quality of knowledge representation
that fosters a comprehensive understanding of security without being
excessively driven by current news in the market. The next section therefore
examines a framework that incorporates crucial intelligent components
aimed at effectively facilitating online investment in light of these
fundamental requirements.
3 FRAMEWORK FOR INTELLIGENT MECHANISM
Situating Knowledge in an Object-Oriented Schema
Before identifying a feasible framework for structuring the knowledge
that best represents merchandises, e.g., stock securities, we should
first think about how we should categorize the knowledge relevant to
investment in accordance with different functional roles of knowledge.
For example, marginal utility is a concept exercised often when one
considers his preference of risk avoidance before making any investment
decision. Marginal utility to an individual is usually profound and
does not change on a daily basis, which, though quite overwhelming,
should not be mixed with other less fundamental knowledge. Similarly,
the time horizon of investment to an individual does not vary significantly.
Knowledge, which can be applied to guide how other knowledge shall be
applied, is considered as meta-knowledge. When certain market news arrives,
one exercises an evaluation model in order to come up with some conclusions.
The chosen evaluation model is thus considered a part of heuristic knowledge.
Such knowledge should also deserve certain degree of recognition by
individuals whose investment decisions have been influenced by the model.
In search of a suitable representation framework, we think, first,
it has to deal with granularity of knowledge; namely, a knowledge entity
that can be primitive or composite. Second, since knowledge is often
temporary and relative due to its dynamic nature, it should be understood
in reference to history and trend. Third, knowledge entities are functionally
relevant to each other; i.e., one knowledge entity may influence another
by changing the related attributes. Fourth, knowledge entities possess
different states; that is, they can move from one state to another driven
by events. Finally, knowledge entities can be evaluated from various
perspectives in light of investment goals. Thus, we find that a truly
object-oriented model would provide a sound framework that naturally
delivers intelligence in a robust manner, representing not only all
the properties discussed but also their dynamic and customizable forms
[Minsky75]. While this conclusion is no surprise
to many computer scientists, it is worth noting that few investment
web sites have had their knowledge representation nearly object-oriented.
In the following, we elaborate a series of intelligent elements resulting
from such a representation model.
Implied Reasoning
Figure 1 shows that a primitive object class can be defined to describe
an instrument of investment in light of a general conception, where
an object instance of the instrument object class can be generated to
represent a bond holding, a stock holding, or even a portfolio of investment.
It is seen from Figure 1 that additional object classes can be derived
from the original object class: One derived object class is called Sector
while the other is called Industry.
Fig.1: Using an Object Model to Express Aggregation and Generalization.
As a result, inherent relationships in the object model facilitate
the expression of a set of knowledge entities and the resulting knowledge
representation naturally provides a reasoning bed. With multiple object
classes defined, the information describing the attributes of an object
instance at one level of a hierarchy can be derived from the instances
of these associated object instances at a lower level. This composed
information then can be used to characterize the security with an emphasis
on the fundamental facts of the security concerns. The implication is
that if an individual stock performs consistently as poorly as its industrial
index does but inconsistently worse than its sector index, an online
investor may interpret the poor performance of that stock as the overall
industrial weakness rather than the organizational incompetence. This
phenomenon may also indicate that the market situation remains sound
and, consequently, a wide spread of investment would render a fair market
return.
Fig. 2: Graphical Representation of Stock Portfolio in Light of Object
Orientation
Figure 2 illustrates that a graphical object-oriented representation
of an individual portfolio, when it is displayed on a web page, would
be more intuitive to ordinary investors and require less time and knowledge
to understand the information represented. If we assume that risk free
is indicated with green whereas high risk is expressed in red, we may
figure out a color close to yellow to indicate a market risk. An individual
portfolio then may be marked in a color somewhere between green and
red. Similarly, a preferred model portfolio would also be marked with
an appropriate color, determined by the individual risk tolerance and
marginal utility and calculated by collecting answers from a few questions
of one's financial situation or investment plan. It is seen from Figure
2 that a model portfolio is represented by a transparent rectangle surrounding
the actual portfolio and stock market index. Polymorphism plays a role
in this graphical representation by showing different colors to indicate
inconsistency between the model portfolio and actual portfolio. Abstraction,
a unary relationship, could help avoid dealing with unnecessary details
whereas projection, another unary relationship, generalizes the knowledge
of a security instrument by choosing a subset of attributes of concern.
The deviation of an individual risk from the market risk is indicated,
for example, with different resolutions. A low resolution of the portfolio
entity may send a warning signal about a possible deviation from an
average market return due to lack of portfolio diversification. The
more the portfolio diversifies, the better resolution an individual
investor can achieve. As far as an investment decision’s rationality
is concerned, an investor can model his or her portfolio by choosing
various securities to see if it matches with the model portfolio, as
well as the individual risk with the market risk. This simple illustration
of representing a stock portfolio with an object-oriented model can
be enhanced by incorporating more expressive interface components including
zooming, nesting, and multiple views [Perlin99].
The following discussion reveals that such a knowledge representation
would also inherently support other reasoning mechanism.
Default Reasoning on Constraints
The next intelligence element, naturally supported by the above-proposed
object model, is to apply constraints, which is particularly effective
for extracting knowledge from a mass of information. The resulting structure
is a constraint object, and constructed together, constraint objects
form an implicit reasoning bed at a chosen abstract level. This further
facilitates the abstraction of knowledge and thus derives a layered
structure of knowledge. The implementation of constraints initiates
from defining default constraints [Reiter80].
A default constraint is identified to depict the intrinsic value of
a security that should be less volatile because of the arrival of news
events concerning the security. To help investors stick with the intrinsic
value of a security, default constraints should also be arranged into
a hierarchy.
Figure 3 shows, in support of fundamental analysis, that the valuation
of a security such as GMH is assessed with a set of constraints, some
of which are self-declared exceptions to its parent object that represents
a typical valuation in the industry. In particular, DBS (direct broadcast
services) is also described by a set of default constraints, some of
which are overridden by its child object.
Fig. 3: Defaults at a Lower Layer May either Depict
Additional Attributes or
Supersede Defaults Adopted from a Higher Layer.
Furthermore, there could be another object above the DBS object, where
the grandparent object is created to describe a typical valuation in
the sector, e.g., telecommunication. A set of default constraints is
defined to typify a default valuation of an enterprise in the sector.
Equipped with such a hierarchy of defaults, the web site may be able
to remind an individual of the following issues implicitly:
- Is GMH a typical business entity in the telecommunication sector?
If yes, the valuation hall is heavily influenced by the top set of
default constraints.
- Is GMH a typical business entity in the DBS industry? If yes, the
valuation of GMH is entailed using the default constraints at the
second layer to supersede the top set of defaults if any conflicts
between two sets occur.
Any additional constraints appearing at the third layer would imply
that GMH either has additional attributes about its valuation or holds
exceptional constraints superseding some constraints at the parent layer.
Note that a constraint at a non-bottom layer is always considered a
default constraint.
The use of constraint objects in a hierarchical structure makes complex
constraints manageable, because a group of constraints may then be possibly
organized to constitute a macro concept with a desirable granularity.
In fact, a default hierarchy in correspondence with an object hierarchy
provides an inference mechanism, namely inheritance [Reiter85].
The hierarchy in this example infers that an industry in a depressed
sector should not be marked attractive unless the industry possesses
some exceptional qualities. Similarly, a business entity within a depressed
industry shall likely deliver the depressing results, unless it has
peculiar strength to supersede those defaults attributed by the industry
to which it belongs.
Two observations are worth mentioning in regard to the default hierarchy.
First, the knowledge represented with a default hierarchy reminds an
investor of the valuation of a typical corporation in the sector or
industry, but it does not enforce that most corporations in the sector
or industry should have to be so evaluated. Instead, the force of the
default hierarchy implies that in the absence of any evidence to the
contrary, a corporation possesses such a typical valuation. Assisted
with a default-reasoning framework, an individual investor may not speculate
whether the individual observes contrary facts. If a P/E ratio is displayed
in green but the growth rate for the same business entity is displayed
in red, that contrast in color should serve as an alert to individual
investors. Such assistance should be more effective than finding the
facts elsewhere. Secondly, with the default hierarchy, reasoning only
proceeds down to a preferred layer by an individual. A mutual fund investor
may only need to know which sector or industry holds better potentials.
Likewise, an investor who is interested in value stocks may like to
dig into one or two layers below a depressed industry or sector to find
out some outstanding corporations underneath.
The advantages resulting from the adoption of a hierarchy of constraint
objects are primarily as follows. The complexity of the financial market
has to be highly abstracted so that an average online investor can perceive
fundamentals of a security of his interest. The online performance dictates
that a lengthy reasoning process is impractical, and thus a reasoning
bed in which logical implications naturally entail shall circumvent
needs for explicit reasoning. The intent to achieve the user-friendly
knowledge representation demands no maintenance for reasoning process
from end-users. Thus, default reasoning fits the scenario nicely. The
probabilistic nature of true or false in predicating the financial future
mandates the arbitrary reasoning be unsuitable. The constraint-based
reasoning, supported with inheritance indicated through a hierarchy
of objects, offers a flexible structure in which default reasoning conducts
inherently.
Dynamic Indices
Applying multiple indices to compiling temporal behavior is another
intelligent mechanism that shall make the framework express the properties
of investment flexibly. In order to express the temporal data at various
levels of abstraction and projection, a supportive KR framework should
offer a variety of chains for individuals to compile historical events
and future indications that jointly help individual investors arrive
at a comprehension of investment [Ladkin86].
Well-organized indices could associate supporting premises to the conclusive
knowledge so that the logic expressed through implicit reasoning could
be justified upon request. While a fundamental analysis mainly focuses
on the current financial situation of a company, supported by the KR
framework presented thus far, the appropriate indices shall extend from
the current snapshots of a company to its historical data and thus facilitate
the technical analysis. In addition, many distinctive perspectives of
a portfolio may be analyzed through multiple chains that help reveal
causes and effects. For example, the instant dissemination of available
events may make online investors believe what they see are current and
that the resulting effect has not yet reflected by the security price.
As a matter of fact, quite often, the just arrived information has already
excessively impacted the price of a related security. Therefore, it
is desirable for an online investor to capably see the events and the
price changes in the same time dimension.
A feasible format to express them in terms of the time dimension is
the before/after chain since most events naturally fall into a sequence.
In consistence with the object-oriented paradigm, events should be chained
in parallel with reactions. Furthermore, the before/after chain should
be organized to express the fundamentals of the events; i.e., these
events should be delivered at an appropriate level of economic entity.
In accordance with importance, the various abstractions of time interval
and event granularity should also be made available for end-users to
navigate efficiently. For example, at a higher-level abstracted chain,
there could be a chain of the more significant events during a time
interval parallel with the sub-chains of these events that are less
significant.
In modeling temporal behavior, the indices should also enable an online
investor either to scan many snapshots of a changing object or to summarize
data based on a chosen time interval. An index based on time stamp can
satisfy the queries concerning the specific moments associated with
an event. The former supports a point-based representation while the
latter assists an interval-based representation. Both index structures
are useful: one can effectively support fundamental analysis whereas
the other is often helpful in support of technical analysis.
Exception Handler
This default hierarchy, however, has to incorporate exceptional valuations
of a security derived from changing perspectives. Consequently, the
hierarchy may extend from the default valuation of a security that is
estimated, for example, by its S&P 500 beta value along with the
S&P 500 return and risk-free return. The branched layers of the
hierarchy may display exceptional valuations [Beck99].
While the methods of expanding a default hierarchy may vary, depending
on the degree of supervision of learning, this paper just examines how
to represent exceptions when they have come into existence. As shown
in Figure 4, the default hierarchy may extend another branch at the
Sector layer to adapt the Sector defaults. An example in hand is an
exceptional valuation for the Sector entity in the event of a significant
increase of interest rate.
Fig. 4: A Default Hierarchy Can Have Exceptions at Each
Layer,
and Defaults at Each Child Layer Are Considered Exceptional to Defaults
at Their Parent Layer.
When exceptions arise, properly incorporating their existence in the
KR framework would enhance the knowledge base that initially consists
of defaults but, theoretically speaking, incomplete. One way to exhibit
exceptions is to display them in a pull-down list in which the default
is at the top while the exceptions are listed in order of confidence.
In this format, constraints are applied in terms of priorities determined
in accordance with confidence. However, the exceptions that violate
a joint consistency should warrant a special indication. For example,
a strong buy recommendation of a stock in an industry that is known
to be avoided should be informed along with an alert if neither the
growth rate nor earnings of the underlying business is positively exceptional.
The theory exercised here again is called the constrained default logic
but the constraints in this example come from siblings rather than from
parents. In the absence of direct evidence to confirm or to suppress
the exception, the KR framework should reflect the inconsistency without
correcting it based upon inadequate information. Such an alert signal
may be delivered by showing inconsistent colors among sibling attributes
of the stock. The exceptions that violate joint constraints shall consequently
be cautiously promoted and shall probably rarely replace defaults. In
support of multiple levels of confidence, exceptions could be organized
into subclasses within a list of values; those satisfy joint constraints
may be separated from the others that do not. As more attributes take
exceptional values in a consistent manner, non-monotonic reasoning should
allow an exceptional valuation of the stock to replace a typical valuation
that is originally adopted as default.
4 CONCLUSION
The intelligent mechanism, proposed in this research to facilitate
online customers in a dramatically different approach, has addressed
the challenges unique to e-commerce by improving the knowledge representation
of online investing as a typical e-business environment. In response
to peculiar characteristics of online investing, the paper has demonstrated
how heuristic intelligence can be developed in a robust knowledge representation
framework. Departing from the traditional approaches, such an intelligent
e-commerce environment enforces no rules but merely guidance to online
customers, which should best suit the present e-commerce environment.
In anticipating significant improvement on facilitating online investors,
the described intelligent mechanism is bound to have limitations. Primarily,
the resulting knowledge representation framework may be unable to respond
to certain specific events quickly enough. While the model can be effective
in preventing online investors from making common mistakes, it could
also be cumbersome when the financial market is turbulent. However,
this framework, which can be applied flexibly, should benefit certain
segments of online investors who are naive and participating for a long-term
capital appreciation. In addition, online investing is a representative
example of e-commerce and hence the framework of intelligent mechanism
proposed here can readily benefit other sectors of e-commerce.
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About the authors
Myron Sheu
is Assistant Professor of Computer Information Systems at California
State University, Dominguez Hills. Previously, he was systems and
applications architect at Boeing Space & Communications and
Hughes Electronics where he played a key role in promoting enterprise
information systems integration. His research interests include
artificial intelligence, object orientation, and e-business. He
received his Ph.D. in computer science from Old Dominion University.
He can be reached at msheu@csudh.edu.
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Xin (James) He is Associate
Professor at Dolan School of Business, Fairfield University, USA.
His current research interests include supply chain management,
ERP implementation, total quality management, and the integration
between information systems and operations management. He received
his Ph.D. from the Smeal College of Business Administration, Pennsylvania
State University. He can be reached at xhe@mail.fairfield.edu.
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Cite this article as follows: Myron Sheu and Xin He: “Object-Oriented
Intelligent Mechanism -Vital for the Success of E-Commerce”, in
Journal of Object Technology, vol. 2, no. 4, July-August 2003,
pp. 101-112. http://www.jot.fm/issues/issue_2003_07/article1
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