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Questions
Answer: Question 1BENEFITS OF USING IT The term Information Work is defined as work performed by using Information Technology .Today’s computer and telecommunications technologies support the automation of many business processes. Information systems have been developed to improve many business operations.For example, Office Automation (OA), supports communication between office personnel ( as well as customers ), both near and far, through Email, Vmail and Fax. This allows employees and customers to communicate with one another effectively. It also allows the electronic scheduling of appointements and meetings. Computer, video and teleconferencing facilities allow business partners in diverse places to discuss business issues and make decisions easily without having to actually travel and converge in one physical location. IT can also facilitate the setting up virtual universities and colleges.Instead of students going far away places to attend a and a great expense, the college now can go to their door steps. They can be taught by eminent professors drawn from the academia. All this is possible with the introduction of high capacity transmission media (e.g. optocal fibers ) and ISDN. IT can also provide entertainment. It is possible to receive video on demand (VOD) from far far away places without having to go to a video shop to rent or buy a video tape. Business and research information from around the world can also be obtained though the Internet, Businesses can use Electronic Data Interchange ( EDI), to transact their business. For Example they can place their orders and pay for their purchases using EDI. Businesses can use Computer-aided-Design and Computer-Aided-Manufacture (CAD/CAM) and Manufacturing Resource Planning II (MRPII) to plan, design, produce and control inventories. Managers can use Executive and Decision Support Systems (ESS and DSS ) to help them in their decision-making. Similarly, marketing, sales, financial and human resource managers can use the Market Intelligence, Sales Monitoring, Financial and Human Resource Information Systems to help them in their respective tasks. Auditors can use audit packages to help them in their audit work. They permit auditors to perform compliance and substantive tests. Doctors can use expert systems (ES) to diagnose and treat patients ( or at least to get a second opinion). Similarly, bank officers can use expert systems to approve loans. Thus, the use of IT can benefit a firm in many ways : · It can raise productivity · It can increase sales · It can reduce costs. · It can use resources more efficiently · It can improve customer service. · It can help in better planning and decision-making. A firm can use IT in practically every area to improve its business operations. It can also help an organisation to be competitive in today’s increasingly global market place. Back to TopAnswer: Question 2The following are basic to office work : a) receiving or producing information. b) processing information ( or creating information from data- arranging it for its intended recipient’s purposes ) ; c) storing or communicating information- the most traditional and widely recognised office functions of typing, copying, telephoning, mailing etc. d) control- inspection, audit etc. Effectiveness is measured by the extent to which the activity fulfills its purpose And its users’ requirements. Efficiency is measured by the extent to which resources are utilised without wastage in the pursuit of effectiveness. Resource incluyde finance,materials, equipment and also human time, effort, skills and knowledge. Achieving and maintaining efficiency will usually invlove the elimination of delays in the provision of office services, and adherence to resource budgets set for the departments. Back to TopAnswer: Question 3An Expert System is an interactive software that mimics human experts in a specialized field ( e.g. loan approval) and assist the user in the decision-making process. It will prompt the user for additional information or to seek further clarification.Based on the information supplied and the information stored internally in the knowledge base (discussed below), it maked recommendations and thus generally assists the user in arriving at a decision. Expert systems have been developed to mimic doctors, financial analysts, tax experts, engineers, lawyers and geologists. Users consult these built-in “software experts” ( using PCs or workstations) for expert advice, opinions, recommendations, or solutions. An expert system thus enables the user to obtain the services of one or more real human experts without having to actually meet and consult them! An ES has several benefits : 1) It enables the knowledge of experts to be canned in a computer disk. That means valuable knowledge is still available even if the experts are not unavailable ( they may go on leave on retire) or their services too expensive. ( The services of a medical specialist or a professional tax consultant). 2) It enables users to seek expert advice or opinion without having to actually meet the experts. ( e.g., it is readily available ). 3) It enables decisions to be made in a consistent manner. Thus it can be an excellent training tool. 4) It is inexpensive compared to the charges of human experts. 5) It raises the productivity of decision-makers as they canobtain instant answers to questions. This gives them more time for other tasks. An expert system shell is normally used to develop an expert system. This a framework that helps a developer to build and use an expert system application. It consits of a knowledge acquisition facility, knowledge base, inference engine, explanation facility and user interface. KNOWLEDGE ACQUISITION FACILITY The Knowledge Acquisition Facility of an expert system shell allows a knowledge engineer to capture the knowledge of one or more human experts in a particular area of expertise and store that knowledge in a knowledge base in the form of facts and rules. The knowledge engineer is like a system analyst. H e must extract the relevant information from one or more domain experts ( i.e., the specialist).This task is by no means trivial. He must interact and spend a considerable amount of time with the experts in order to obtain the necessary information.The information collected are stored in the form of facts and rules that the experts use to arrive at a solution. The knowledge engineer may use the interview and/ or observation technique to obtain the information from the experts. Back to TopAnswer: Question 4
a) Only certain issues can be solved by expert system. b) Most of the expert systems require major development which are costly and time consuming. c) Most of the expert systems need to be updated regularly as there are changes in technologies. Back to TopAnswer: Question 5Fundamentals
of fuzzy sets and fuzzy logic Henrik
Legind Larsen Aalborg
University Esbjerg A
new theory extending our capabilities in modeling uncertainty Fuzzy
set theory provides a major newer paradigm in modeling and reasoning with
uncertainty.Though there were several forerunners in science and philosophy, in
particular in the areas of multivalued logics and vague concepts, Lotfi A. Zadeh,
a professor at University of California at Berkeley was the first to propose a
theory of fuzzy sets and an associated logic, namely fuzzy logic (Zadeh, 1965).
Essentially, a fuzzy set is a set whose members may have degrees of membership
between 0 and 1, as opposed to classical sets where each element must have
either 0 or 1 as the membership degree—if 0, the element is completely outside
the set; if 1, the element is completely in the set. As classical logic is based
on classical set theory, fuzzy logic is based on fuzzy set theory. Major industrial application areasThe
first wave: Process control
The
first industrial application of fuzzy logic was in the area of fuzzy
controllers. It was done by two Danish civil engineers, L.P. Holmblad and J.J.
Østergaard, who around 1980 at the company F.L. Schmidt developed a fuzzy
controller for cement kilns. Their results were published in 1982 (Holmblad
& Østergaard, 1982). Their results were not much notice in the West, but
they certainly were in Japan. The Japanese caught the idea, and applied it in an
automatic-drive fuzzy control system for subway trains in Sendai City. The final
product was extremely successful, and was generally praised as superior to other
comparable systems based on classical control. This success encouraged a rapid
increase in the Japanese’s interest in fuzzy controller during the late
1980s.This led to applications in other areas, like elevator control systems and
air conditioning systems. In the early 1990s, the Japanese began to apply fuzzy
controller in consumer products, like camcorders, washing machines, vacuum
cleaners, and cars. The Japanese success led to increased interest in Europe and
the US in fuzzy controller techniques. The
second wave: information systems The
second wave of fuzzy logic systems started in Europe in the early 1990s, namely
in the area of information systems, in particular in databases and information
retrieval. The first fuzzy logic based search engine was developed by the author
in collaboration with professor R.R. Yager, Machine Intelligence Institute, US.
It was aimed for application netbased commerce systems, namely, at that time the
only in the world, the French Minitel. It was first demonstrated to the public
at the Joint International Conference of Artificial Intelligence in 1992 in
Chambery, France. In 1999, the technique was adopted by the Danish search engine
Jubii. The several ideas and results applied in the technique were published;
see, for instance, (Larsen & Yager, 1993, 1997). Internet and the Web gave
new interest to application of fuzzy logic technology. In the net based society
we have an enormous amount of information and knowledge electronic accessible
for decision makers and human in general. Much of this information is inherently
uncertain—lack of precision, vague concepts, more or less reliable
information, etc. On the other hand, to be useful, users must be able to utilize
it, despite the uncertainties. Certainly imperfect information and knowledge
cannot be ignored; for instance:“He said that his department and commissioned
a study on the effect of requiring higher mileage cars, which would be smaller
and could be less safe.”(New York Times, February 22, 1991; source: E.H.
Ruspini, AI Certer, SRI Internaitonal). This is an example of information that
is inherently imprecise or vague and therefore not well suited for
representation and processing by classical binary logic or probabilistic based
techniques. Much information is two valuable to be ignored, but it requires a
human to identify it and utilize it. With this huge amount of information, we
need to computer based tools to find it, evaluate it, extract the meaning, and
partly utilize it for decision support. Here fuzzy logic is beginning and will
in the future play a major role as tool allowing us to model and reason with the
information and knowledge; in fact, fuzzy logic allows us to properly utilize
the information in the uncertainty. A newer application area in this line is
data mining (text mining, web mining, …) for discovery of knowledge. Hence,
the second wave is, in particular due to the internet and the web, likely to be
much greater then the first in the control area. How
fuzzy sets extends our modeling capabilities By
fuzzy set theory we can provide exact representations of concepts and relations
that are vague, that is, with no sharp yes-no borderline between cases covered,
and cases not covered, by the concept or relation. This allows us to represent,
for instance, that a document deals with a topic T1 to some degree (between 0
and 1), that a user is interested in a topic T2 to some degree, and that a topic
T3 implies a topic T4 to some degree. By fuzzy set theory and fuzzy logic, we
can not only represent such knowledge, but also utilize it to its full extent,
taking the kind and the form of the uncertainties into account. This does not
mean than fuzzy logic renders classical logic and probability theory obsolete.
On the contrary, though fuzzy sets and fuzzy logic extend membership degrees and
truth values from 0 and 1 to the real interval from 0 to 1, the definition of
the fuzzy logic formalism still rely on the classical logic. Further more, we
apply statistics based on probability theory in fuzzy data mining of knowledge
— the main difference being that probabilities now are associated to fuzzy
sets. Another advantage of fuzzy logic is that it allows fast processing of
large bodies of complex knowledge, since processing is performed by numerical
computations and not symbolic unification as in, e.g., logic programming
formalisms. As opposed to neural nets, fuzzy logic has the advantage that it
supports explicit representation of knowledge, like in symbolic formalisms,
allowing us to combine knowledge in a controlled way. 2.
Outline of the course The
direction taken in the course By
knowledge modeling in the framework of fuzzy sets we introduce a new powerful
basis fordevelopment of advanced information systems. It should be mentioned,
that though lots of research has been done if fuzzy logic theory that has been
much further developed since Zadeh’s seminal paper from 1965, works on fuzzy
logic software and knowledge engineering, and efficient algorithms for fuzzy
knowledge processing, have been very limited; the latter in particular due to
the fact that the well known classical algorithms assume classical binary logic.
I hope with this course in fuzzy logic (that had also been referred to as Fuzzy
Logic Information Technology, Fuzzy Systems, and Fuzzy Logic Engineering), to
contribute to an improvement of this situation.With respect to application
frameworks, we shall in particular consider information access in the broad,
including database querying, information retrieval, and object
recognition—that essentially are solved through some variant of classificatory
problem solving—and, further, data mining where “hidden” knowledge is
retrieved from the information base, as essentially represents some form of
inductive problem solving. Though the focus in this course is fuzzy logic, we
shall cover central aspects of information retrieval, database querying, and
search engines, as well as knowledge representation and algorithms, as related
to the engineering part. As opposed to tradition approaches in teaching these
topics (courses and text books), we adopt fuzzy logic as the basic logical
framework, and emphasizes algorithms for fuzzy knowledge processing. Back to TopAnswer: Question 6
Intelligent
Agents and XML - A method for accessing webportals
in both B2C and B2B E-Commerce Abstract. In E-Commerce today webportals are important and also intelligent agents grow in significance. Our
approach is to combine them in designing webportals and interfaces for both
users and agents. In this paper we discuss the problems in automatically accessing
portals and possible solutions for them through using OOM methods. The
solution selected by us, using an XML-based standard and dynamically
reconfigurable protocols, is described afterwards and the methods used are shown.
Afterwards we briefly present an example, a webportal for sports information. Keywords: Agents, OOM, OOP, XML, E-Commerce, Webportals 1
Introduction Both web portals and intelligent
agents are important factors in the Internet and of growing importance especially in
E-Commerce. However, combining these two is not that easy. The main problem is how
agents retrieve information from a webpage, which is formatted for reading by
people. Through the combination of using ebXML [2] and applying methods for
object-oriented design on all parts involved, this gap can be at least ameliorated. We propose
to use object-oriented modeling-techniques not only for the implementation of the
software, but also for the design of the data structure to be exchanged including dynamic
aspects of protocols. 2
Problems of automatically accessing webportals Webportals provide a unified access
to a large set of information on certain topics. However, different portals contain
different content data and different methods of access. It is therefore hard for
customers to locate and buy the information or goods they are interested in, as the
methods and relative location change with each supplier. At the same time, a unified portal
or organization is unrealistic (and probably not suited for all types of content).
This is partly done by providers on purpose to avoid competition through complicating
comparisons and therefore binding customers (if they can handle a portal, they will
not move to another one, where they have to learn the use anew), both of which are
especially important in B2C E-Commerce. In B2B E-Commerce a difficulty is
automation of procurement: Many goods can be bought cheaper or faster on the WWW,
however the work has to be done completely manually every time (in contrast to this in
conventional procurement pre-created forms and standardized procedures can be
used). Therefore the need arises for an a) unified method for locating, accessing and
buying information, which can be b) automated to a large degree, even including
payment [11]. Using the combination of storing data in XML [4] for presentation on
webpages and including the possibility for access by intelligent agents, both
difficulties can be overcome, leading to an enhancement of ECommerce. Specific problems of the current
design are: ?
Webpages are designed for different user groups having
distinct interests and varying habits, and according to
their content. Also the behavior of the users desiring information differs. Shoppers often
want detailed information on a specific product in a fast and easy way
(B2B), while visitors of e. g. sport portals just want to browse or become generally
informed (B2C). ?
Because of diverging interests automatic access to
webportals through programs is complicated, as there is no
standardization how data is presented, where data is located on a webpage, or which categories
define the organization of the website. ?
The HTTP-protocol is not ideally suited for automatic
access because the connection is closed after each request,
passing of parameters is complicated, and negotiations are not possible. ?
The content data of the communication introduces
difficulties: Both the syntax and especially the semantics (more of a
problem in automatic access than by humans) must be the same on both sides of
the communication link to allow meaningful interaction. Providers may not necessarily be
delighted by this as they will probably face more competition [3]. However, they can
also profit, e.g. through reaching more interested users or being able to present data
in a special way not only for one specific group (agents) but also for different
user groups as well (Personalization; different preferences or interests, people with
disabilities, etc.). 3
Steps towards solutions A possible solution would be
specifying and using a custom protocol for accessing data. On the one hand the advantage
of this solution is that all parties need only understand and implement one – the then
common – protocol. On the other hand this solution has the disadvantage that
a standard must be specified that is suitable for all areas and all users. In addition,
providers must agree upon it. Moreover, a single standard coping with everything would be
either very complicated, or useable for special tasks only with difficulties. Using a binary protocol for the
exchange of data is another possibility. This could be a protocol based on a certain
method of serializing data, like Java object serialization or serializeable MFC objects or a
specific program library. The advantage is gained speed in development and
processing, and a relatively low required bandwidth for communication. But the
disadvantage is too serious: This works only on one type of system and one platform – it
is not platform-independent. As another possibility we consider
mobile agents [1], [9] for searching and retrieving data [12]. They possess some
advantages concerning protocols: Communication with other partners (either other
agents or webportals) can be automatically adapted by the agents for a
meaningful interaction with their surrounding. Therefore they are a good choice for
retrieving data when negotiations are necessary. Also, the mobility of agents saves bandwidth
because the bulk of the communication is handled locally and only the agent (with
compressed and filtered results) needs to be transferred [10]. Agents are also relevant in
connection with protocols for payment: Although they are standardized (e. g. SET),
unique systems like vouchers, debiting or private E-Cash exist (see [15] for an overview of
different payment systems in connection with agents). They usually have many
things in common (like identification and transfer of some data), but the actual
data-objects and the sequence of messages differ. Agents can adapt themselves to these
protocols and allow in this way a wider diversity, also improving E-Commerce. Using XML for the representation of
data would be a good basis for retrieving data by the agents and also for the
provider of it: An agent can easily extract information from XML as it includes the concept
of an explicit definition of the data structure. So no additional transformation before
extraction of information (see [5] for an overview of products employing this
technique) is required. And using XSL (eXtensible Stylesheet Language) [6] allows
different views on and presentations of the same data, a benefit for providers. Even
when modeling the content data alone, an objectoriented representation is appropriate. The
reuse of components allows agents to understand the data at least partially, while
this is impossible when using unique and proprietary definitions. 4
Reconfigurable protocols for information retrieval Protocols are an important factor
in communicating with agents and always should be adapted to the business processes
and not the other way round. Our way to provide adaptable state-based protocols is
based on a two-fold object-oriented approach: We build a static implementation
hierarchy, as well as a dynamic hierarchy of calling other protocols as elements of one.
The latter can be stacked to any depth, but no interaction across different levels
is possible (subprotocols must terminate before the parent protocol can resume). This object-oriented approach also
allows extensions of existing protocols in two ways: First the protocol is
implemented object-oriented and can be extended through subclasses, which overwrite methods
(i. e. state transitions) in superclasses. Secondly, providing it with different
“plug-in protocols” as subprotocols at runtime to change its behavior in some details
(user-defined or negotiated with the partner). An advantage of this approach is
that if an agent does not understand a certain subprotocol, in some cases another one could be
substituted (e.g. a known superclass of the unknown one) or the subprotocol
simply be left out. This allows an agent to do transactions at least in a
rudimentary way, e.g. without reliable identification of the partner or without using special
discounts or options. Another advantage for the developer is that creating protocols
dynamically in a hierarchy allows using an objectoriented modeling approach for the protocol
itself and not only its implementation. 5
Modeling content data as a class hierarchy with OOMtechniques For modeling the content data,
OOM-techniques [14] are appropriate to use, too. With object-oriented analysis you can
easily analyze the content data for possible classes and attributes. These classes and
attributes can be transformed into XML representation without the necessity of an
additional encoding. Even though (pure) XML does not
offer the concept of object-oriented programming (OOP), with XML-Schema [16] it is
possible to work with inheritance and datatypes (like string, float, etc.). Also
different namespaces are supported. Including data types further improves the
reliability, as the agent can then (in some cases) retrieve information even from unknown data
types (e.g. retrieving the price by searching for the only element consisting of the
type “Currency”). The advantages of this
object-oriented modeling are that agents can retrieve at least some information from the data,
even though they cannot interpret the specification of the actual object: understanding
one of the superclasses may often suffice. This means for the owner of the agents that he
at least gets a feedback and can decide whether it makes sense to give the agent more
specific information (or abilities) or not. Another advantage of OOM [8] is
that older classes can be used as components if new data types are required. The
specification of classes is also written in XML and therefore can be distributed easily
(e.g. in addition to content data, so the recipient can manually interpret them through
comments or names, even though the agent cannot). 6
Sample implementation This system was implemented in Java
based on an agent system [13] developed at the institute, which has a special
focus on security [7]. The communication is based on ebXML-messages (a set of
specifications for using XML for a modular E-Commerce framework with a focus on business
processes), but also includes local broadcasts, which are not provided for in the
standard. The data transmitted is also modeled in XML. Currently, as a research
project a web-portal for sports is under development, which
will be also accessible by agents. As an example, the definition of
the content data specified for a member of a portal (called PortalMember) is presented
in Fig. 1. First of all, a namespace is declared, where all XML-Schemas and XML-Files
are included. Furthermore in this XMLSchema for the data of portal members, the
schema for members in general (e.g. portals, clubs, etc.) called MemberData is
included, which is used as a “base-class” for PortalMember. PortalMember inherits
from MemberData and is extended with additional elements and attributes that are
specifically needed for members of portals. <?xml
version="1.0" encoding="UTF-8"?> <xsd:schema
targetNamespace="http://www.fim.uni-linz.ac.at" xmlns="http://www.fim.uni-linz.ac.at" xmlns:xsd="http://www.w3.org/2000/10/XMLSchema" elementFormDefault="qualified"> <xsd:include
schemaLocation="MemberDataDoc.xsd"/> <xsd:element
name="PortalMember" type="PortalMember"
minOccurs="0" maxOccurs="unbounded"/> <xsd:complexType
name="PortalMember"> <xsd:complexContent> <xsd:extension
base="MemberData"> <xsd:sequence> <xsd:element
name="Fee" minOccurs="0" maxOccurs="unbounded"> <xsd:complexType> <xsd:sequence> <xsd:element
name="Amount" type="xsd:double"/> <xsd:element
name="Currency" type="xsd:string"/> </xsd:sequence> </xsd:complexType> </xsd:element> <xsd:element
name="DEntrance" type="xsd:date"/> <xsd:element
name="DWithDrawal" minOccurs="0" maxOccurs="1"
type="xsd:date"/> <xsd:element
name="UserID" type="xsd:string"/> <xsd:element
name="Password" type="xsd:string"/> </xsd:sequence> <xsd:attribute
name="FeePaid" use="required" value="No"> <xsd:simpleType
> <xsd:restriction
base="xsd:string" > <xsd:enumeration
value="Yes"/> <xsd:enumeration
value="No"/> </xsd:restriction> </xsd:simpleType> </xsd:attribute> </xsd:extension> </xsd:complexContent> </xsd:complexType> </xsd:schema> Figure 1: Schema for members of
portals (Extension of general members) The example above is used for
retrieving information on a portal member (which is only allowed after identification).
The agent can use the information provided e. g. for autonomously paying the recurring
fee or verifying the personal data. 7
Conclusion As explained, using XML and
hierarchical object-oriented modeling of data is not sufficient, because the semantics
also must be specified: Different implementers should not only create compatible
programs, they must also adhere to the same semantics, which is of special importance in
open systems like the Internet. ebXML with its specification of both
syntax and semantics and the storage of them in a public repository is an important step in
this direction. However, even though ebXML is
simpler than EDI, it is not trivial (and cannot be because of the complicated
requirements it should fulfill). It is therefore sensible to use intelligent agents to support
this, providing even more interoperability through automatic adaptation and
flexibility through fast adjustment to new requirements and platforms. But also agents benefit
from using XML: Analyzing and interpreting the data gets much easier compared to
HTML or text-files. The combination of using XML for
the data and intelligent agents for the processing seems therefore to be ideal, as
both benefit from each other. To fully realize them, however, all aspects, static and
dynamic ones, need to be modeled and implemented with a view on object orientation.
This combination allows extending the user-groups of web-portals to include agents
with little work. This includes the benefit of automatic information retrieval. It also
addresses the concerns of information robbery by competitors through the possibility
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Copyright © 2003 [Tan Liat Wee]. All rights reserved.
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