1. An Architecture for Decision Support in the Age of Semantic Web
Fuhua Lin and Qin Li
Athabasca University, Canada
Sponsored by
National Science and Engineering Research Council (NSERC) of Canada
The Faculty of Business Administration
University of Macao, April 27, 2005

2. Outline Introduction Intelligent agents Service-Oriented Architecture
Graduate Program Scheduling Service
Conclusions and Future Work

3. Computing, Organizations, and BIS
need
adaptive
on-demand
businesses and processes to meet dynamic business strategies.
Trends (M. Wooldridge, 2001)
Ubiquity
Interconnection
Delegation
Human-Orientation
Intelligence
Business Information Systems
Focus on:
Collaboration
Integration

4. Businesses and Services
Organizations
Businesses need new tools to connect with customers and partners by providing
The right quality service
To the right customer
Through the right channel
At the right price
At the right time
Of all sizes

5. Software Agents help the user in different ways
hide the complexity of difficult tasks
perform some tasks on behalf of their users
teach the users
monitor events and procedures
help the users collaborate and cooperate
The user doesn't necessarily “listen” to what the agent "says"

6. Agents and their Environments
User
Preference/Profiles/Tasks
Percepts
Sensors
Networked
Agent
Environment
Actions
Effecters
Agent-Ready?

7. What is the Semantic Web?
A mesh of information linked up in such a way as to be easily processable by machines, on a global scale.
An efficient way of representing data and services on the Web, or as a globally linked database.
Tim Berners-Lee, inventor of the WWW.
Today's Web is oriented toward presentation to people: this is the purpose of html tags <bold>PRINT THIS IN BOLD</bold> on Web pages. After HTML came Extensible Markup Language (XML), which separates the presentation from the meaning <LastName>Obrst</LastName>. However, to a computer this is the same as A machine needs something that helps interpret what the tags mean. That's were "machine-interpretable" semantics comes in.
The Semantic Web is a more efficient way of representing data on the Web. A major thrust of this effort is developing languages for explicitly describing the "meaning" of Web resources, both information and services, so that they can be "understood" by software that needs to select among them or combine them in appropriate ways.

8. Web Services Technologies
HTTP
Web Service
Web Service
WSDL
SOAP/HTTP
HTTP
Registry
Web Service
Web Service
(UDDI)
WSDL
A Web Service is a software system designed to support interoperable machine-to-machine interaction over a network.
It has an interface described in a machine-processable format (WSDL)
Other systems interact with the Web Service in a manner prescribed by its description using SOAP-message, typically conveyed using HTTP with an XML serialization in conjunction with other Web-related standards.
The Power of Web Services, in addition to their great interoperability and extensibility thanks to the use of XML, is that they can be combined in order to achieve complex operations
Web Services Description Language (WSDL)
Universal Description, Discovery and Integration (UDDI)
HTTP
Web Service
Simple Object Access Protocol (SOAP)

9. Integrating Agents with Web Services
HTTP
Web Service
Web Service
WSDL
HTTP
Registry
Web Service
Web Service
WSDL
A Web Service is a software system designed to support interoperable machine-to-machine interaction over a network.
It has an interface described in a machine-processable format (WSDL)
Other systems interact with the Web Service in a manner prescribed by its description using SOAP-message, typically conveyed using HTTP with an XML serialization in conjunction with other Web-related standards.
The Power of Web Services, in addition to their great interoperability and extensibility thanks to the use of XML, is that they can be combined in order to achieve complex operations
SOAP
HTTP
Agent
Web Service

10. Web Services
Services have their properties, capabilities, interfaces, and effects, all of which must be encoded in an unambiguous, machine-understandable form.
Services have ontologies that include
a machine-readable description of services (as to how they run),
the consequences of using the service (e.g., the fee), and
an explicit representation of the service logic (e.g., automatic invocation of another service).
form, to enable agents to recognize the services and invokes them automatically

11. Ontologies Are text-based
Are put somewhere on the Web for agents to consult it when necessary
Are represented using the syntax of an ontology-representation language such as (DARPA Agent Markup Language) DAML, OIL, DAML+OIL, and OWL.
provide a set of knowledge terms, including
the vocabulary
the semantic interconnections
some simple rules of inference
logic for some particular topic or services.
Technically, an ontology is a text-based piece of reference-knowledge, put somewhere on the Web for agents to consult it when necessary, and represented using the syntax of an ontology-representation language.
Every such an ontology should provide a set of knowledge terms, including the vocabulary, the semantic interconnections, and some simple rules of inference and logic for some particular topic or services.

12. Protégé 2000
An extensible infrastructure and allows the easy construction of domain ontology, customized data entry forms.
Provides an API that can easily be extended as Web Services.

13. Applications in Decision Support
Scheduling
Planning
Consulting
Advising
Intelligent Search
FAQ-Answering

14. An Example: eAdvisor Course selection Program planning
Collaboration between universities
Integration of educational services

15. Background to meet the students’ needs
to reduce the workload of the advisors
On behalf of the students’ advisors, the agent will assist them in generating up-to-date, personalized, and optimal study plans by constantly monitoring and utilizing related information resources.

16. Related Work
Most research has focused on techniques for adaptation at navigational level and at content level.
E.g, Dolog, et al. (2004) have proposed Smart Space for Learning which supports access to individualized learning materials and experiences using Personal Learning Assistants.
At the program planning level, California State University has set the implementation of the PeopleSoft degree audit system at all campuses to actively monitor a student’s course decisions during registration and to provide information that informs course decisions.

17. Related Work (Con’t)
Kay, Chen & Mizoguchi, have noted the advantage of using ontologies for learner/user models.
Razmerita, et al., (2003) proposed a generic ontology-based user modeling architecture, OntobUM, based on the IMS LIP.
Planning Advisor on curriculum and enrollment (Gunadhi, et al., 1995)

18. … University 2 University N Learner UI Learner Information Web Service
Notification
Web Service UI
notify
update
Advisor/Registrar
/Administrator
Learner Information
Web Service
request
delegate
Scheduling
Agent DB
Monitoring
Agent
MSc IS Ontology
Web Service
Learner
Data Base UI
monitor
Brokering
Services
delegate/
notify
request/respond
Course Schedule
Web Service
Course Schedule
Web Service
Program/Course
Data Base
Ontology
Course
Schedule
Program/ …
University 2
University N
Program/Course
Data Base

19. AU's MSc-IS Program
Based on the MSIS 2000 Model Curriculum, with the additional rigor of a technology specialization.
MSIS 2000: Model Curriculum and Guidelines for MS Degree Programs in Information Systems
sponsored by the Association for Computing Machinery (ACM) and the Association for Information Systems (AIS).
It is endorsed by the leading information systems organizations.

20. MSIS Ontology

21. MS IS Ontology (Con’t)

22. Protégé 2000 classes relate to each other

23. User Interface

24. Knowledge Representation
Job Objectives [MSIS 2000]:
JO01: Advancement in current job
JO02: First or middle IS management: 603, 602, 605, 604, 607, 610
JO03: Management consultant: the same as JO02
JO04: Internal consultant/senior staff: the same as JO10
JO05: CIO: 605, 610, 603, 602, 604, 607
JO06: Business analyst: 607, 603, 602, 610, 605, 604
JO07: Entrepreneur: the same as JO06
JO08: Outsourcer/systems integrator: the same as JO06
JO09: Project manager: the same as JO05
JO10: Systems specialist: 603, 607, 610, 604, 602, 605
JO11: Technical specialist: 604, 602, 610, 603, 605, 607
JO12: IT Liaison: the same as JO05
JO13: A Ph.D. program leading to teaching: the same as JO11
JO14: Electronic commerce specialist: the same as JO11
J02, J03: , 602, 605, 604, 607, 610
J04, J10: , 607, 610, 604, 602, 605
J05, J09, J12: 605, 610, 603, 602, 604, 607
J06, J07, J08: 607, 603, 602, 610, 605, 604
J11, J13, J14: 604, 602, 610, 603, 605, 607
Career Tracks [MSIS 2000]
CT01: Academia (path to Doctorate)
CT02: Consulting
CT03: Data Management and Data Warehousing
CT04: Decision Making
CT05: Electronic Commerce
CT06: Enterprise Resources Planning
CT07: Global IT Management
CT08: Human Factors
CT09: Knowledge Management
CT10: Managing the IS Function (Internal to IS)
CT11: Management of the IS Function (external to IS)
CT12: New Ways of Working
CT13: Project Management
CT14: Systems Analysis & Design
CT15: Technology Management
CT16: Telecommunications

25. Job Objectives [MSIS 2000]:
JO01: Advancement in current job
JO02: First or middle IS management
JO03: Management consultant
JO04: Internal consultant/senior staff
JO05: CIO
JO06: Business analyst
JO07: Entrepreneur
JO08: Outsourcer/systems integrator
JO09: Project manager
JO10: Systems specialist
JO11: Technical specialist
JO12: IT Liaison
JO13: A Ph.D. program leading to teaching
JO14: Electronic commerce specialist
Specialization:
S1: System Development
S2: E-Services
S3: Theory
Groups of Job Objectives:
G1= {J02, J03, J04, J09}
G2= {J04, J10}
G3= {J13}
G4= {J05, J06, J07, J08}
G5= {J11, J12, J14}
JO = Job Objective
SP = Specialization
CC = Core Courses
EL = Elective Courses
IN = Integration Project/Thesis
AS = assessment style
G1, G2, G3, G4, G5
S1, S2, S3 AS JO SP AS
G1: 603, 602, 605, 604, 607, 610
G2: 603, 607, 610, 604, 602, 605
G3: 605, 610, 603, 602, 604, 607
G4: 607, 603, 602, 610, 605, 604
G5: 604, 602, 610, 603, 605, 607 CC IN EL
S1: 610, 617, 667, 607
S2: 636, 641, 648, 660, 689, 607
S3: 674, 607
(G1, Si), (i=1, 2, 3): E, P
(G2, Si), (i=1, 2): P, E
(Gi, S3), (i=2, 3, 5) : E, P
(G3, S1), (i=1, 2): P, E
(G4, Si) (i=1, 2, 3): E, P
(G5, Si) (i=1, 2): P, E

26. G5 ^ S3 ^ E
G4 ^ S3 ^ E
G5 ^ S3 ^ P
G3 ^ S3 ^ E
G4 ^ S3 ^ P
G5 ^ S2 ^ P
G2 ^ S3 ^ E
G3 ^ S3 ^ P
G4 ^ S2 ^ E
G5 ^ S2 ^ P
G1 ^ S3 ^ E
G2 ^ S3 ^ P
G3 ^ S2 ^ P
G4 ^ S2 ^ P
G5 ^ S1 ^ E
G1 ^ S3 ^ P
G2 ^ S2 ^ E
G3 ^ S2 ^ E
G4 ^ S1 ^ E
G5 ^ S1 ^ P
G1 ^ S2 ^ E
G2 ^ S2 ^ P
G3 ^ S1 ^ P
G4 ^ S1 ^ P
G1 ^ S2 ^ P
G2 ^ S1 ^ P
G3 ^ S1 ^ E
G1 ^ S1 ^ E
G2 ^ S1 ^ E
G1 ^ S1 ^ P

27. G5 ^ S3 ^ E
G4 ^ S3 ^ E
G5 ^ S3 ^ P
G3 ^ S3 ^ E
G4 ^ S3 ^ P
G5 ^ S2 ^ P
G2 ^ S3 ^ E
G3 ^ S3 ^ P
G4 ^ S2 ^ E
G5 ^ S2 ^ P
G3 ^ S2 ^ P
G5 ^ S1 ^ E
G1 ^ S3 ^ E
G2 ^ S3 ^ P
G4 ^ S2 ^ P
G1 ^ S3 ^ P
G2 ^ S2 ^ E
G3 ^ S2 ^ E
G4 ^ S1 ^ E
G5 ^ S1 ^ P
G1 ^ S2 ^ E
G2 ^ S2 ^ P
G3 ^ S1 ^ P
G4 ^ S1 ^ P
G1 ^ S2 ^ P
G2 ^ S1 ^ P
G3 ^ S1 ^ E
G2 ^ S1 ^ E
G1 ^ S1 ^ E
G1 ^ S1 ^ P

28. Representing the relations between the job objectives and the career tracks in MSIS:
OB01 | ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?
02 | x x x x x
03 | x x x x x x x
04 | x x x x x x x
05 | x x
06 | x x x x x x
07 | x x x
08 | x x x x x
09 | x x x
10 | x x
11 | x x x x x x x
12 | x x x
13 | x
14 | x
A[xij]14x16

29. Career Tracks Career Tracks [MSIS 2000]
CT01: Academia (path to Doctorate)
CT02: Consulting
CT03: Data Management and Data Warehousing
CT04: Decision Making
CT05: Electronic Commerce
CT06: Enterprise Resources Planning
CT07: Global IT Management
CT08: Human Factors
CT09: Knowledge Management
CT10: Managing the IS Function (Internal to IS)
CT11: Management of the IS Function (external to IS)
CT12: New Ways of Working
CT13: Project Management
CT14: Systems Analysis & Design
CT15: Technology Management
CT16: Telecommunications

30. Mapping between Career Tracks and Courses*
X X X X X * X X X X X X X
X X X X X X X X X
X X X X
X X
CT05 X X X X X X X X X
CT06 X X X X X X X X
CT07 X X X X X X X X X
CT08 X X X X X
CT X X X X X X X X
CT X X X X X X X
CT X X X X X X X X X X X
CT X X X X X X
CT13 X X X X X X X
CT X X X X X X X X
CT X X X X X
CT X X X X X X
CT01
CT02
CT03
CT04
CT05
CT06
CT07
CT08
CT09
CT10
CT11
CT12
CT13
CT14
CT15
CT16
B[yij]16x19
Preferences ?

31. Mapping between Career Tracks and Courses
COMP
CT01 X X X X X X X X X X X X X X X X
CT02 X X X X X X X X X X X X X
CT03 X X X X X X X X
CT04 X X X X X X
CT05 X X X X X X X X X X X X X
CT06 X X X X X X X X X X X X
CT07 X X X X X X X X X X X X X
CT08 X X X X X X X X X
CT09 X X X X X X X X X X X X
CT10 X X X X X X X X X X X
CT11 X X X X X X X X X X X X X X X
CT12 X X X X X X X X X X
CT13 X X X X X X X X X X X
CT14 X X X X X X X X X X X X
CT15 X X X X X X X X X
CT16 X X X X X X X X X X
B[yij]16x19
Preferences ?

32. IMS LIP Student Models interoperability of Learner Information systems
student’s basic information,
the student’s learning goals , learning styles, concerning every MSc IS Ontology’s curriculum building block and courses together with the student’s Job objectives and backgrounds.
Be updated by the system every time the student completes a course. √ √ √ √
GPSS traces the learner’s profile by building a Student Model. We use the IMS LIP standard ( suitably extended to represent the student’s profile.
IMS LIP addresses the interoperability of Internet-based Learner Information systems and supports the exchange of learner information.
This model represents, in addition to the student’s basic information, the student’s skills concerning every MSc IS Ontology’s curriculum building block and courses together with the student’s Job objectives and backgrounds.
The representation of the student’s profile is updated by the system every time the student completes a course.

33. Web page relationship in eAdvisor
Display previous study Plan (s)
if the student already has it (them)
Login
page
Front Controller
Servlet (control tier)
Enter preferences and plan parameters (Initialize Petri nets)
Generate new
study plans
Model tier
(A class using Connection pool to
Get data and return tag and result set)
Tag 1: successful or not
Tag 2: already have plan or not
Tag 3: have taken course or not
Student basic
information
Transcript
Learner
model
Plan parameters DB
Connection Pool
Program Ontology
Curriculum
model
Business Rules

34. Preprocessing and real-time processing
Learner preferences
Score node
representation
scheme
initialization
Score node
representation
scheme
Score
evaluation
Requirement Parameters
Learner
Model DB
Decision
logic
Score node
selection
Curriculum
model
Course
Metadata
Plan

35. Student Model RDF file <student rdf:ID="KB_657368_Instance_52">
<identificationslot>
… …
<goalslot>
<accessibilityslot>
<transcriptslot>
<transcript rdf:ID="KB_657368_Instance_62">
<coursePNML rdf:datatype
="
pnml
</coursePNML>
</transcript>
</transcriptslot>
</student> >
We build our student model based on IMS Learner Information Specifications. There are eleven categories in the IMS Learner Information Specifications. For the sake of simplicity and usability, we adapt four categories in our student model. They are identification, goal, accessibility and transcript. These four categories are enough for us to generate study plans and contact student.
Our student model extends the IMS Learner Information Specifications in the transcript category. This category in IMS is only defined as an interface, no any detailed specification. We define transcript in student model as the file name corresponding to the student’s PNML file. That file contains all the information of student’s study progress. And it also includes the course prerequisite relationships. We believe that prerequisite relationship is unique for each student, because that relationship can update with time progresses. We should use this prerequisite relationship to generate study plan that can match this relationship.

36. Program Modeling using Petri Nets
Places (local states) and transitions (local activities).
Concurrent behavior by partially ordered sets.
Graphical representation
Formal: Graph theory, Linear algebra.  Both links can be exploited for the verification of systems.
The Petri Net Kernel (PNK) provides an infrastructure for bringing ideas for analyzing, for simulating, or for verifying Petri Nets into action.
Petri nets sharply distinguish between states and activities (the latter being defined as changes of states).  This corresponds to the distinction between places (local states) and transitions (local activities).
Global states and global activities are not basic notions, but are derived from their local counterparts.  This is very intuitive and suits well the description of a distributed system as the sum of its local parts.  It also facilitates the description of concurrent behavior by partially ordered sets.
While being satisfactorily formal, Petri net also come with a graphical representation which is easy to grasp and has therefore a wide appeal for practitioners interested in applications.
By their representation as bipartite graphs, Petri nets have useful links both to graph theory and to linear algebra.  Both links can be exploited for the verification of systems.

37. Overall procedure of adding a course into a curriculum Petri net
Add a transition for the course
Does the new
course have prerequisite
courses?
Start No
Yes
Read Petri net into myNet from Course.PNML
Find the outPlace of one prerequisite course
Input New Course Name
Get the inscription information for that prerequisite course from Process of getting logical relationship between prerequisite courses
Add InPlace, place name “Ready+CourseName
Input all the
prerequisite courses
Add an inArc connecting that outPlace of prerequisite course with the new course transition. Set inscription information for that prerequisite course
Indicate the logical
relation between
pre-requisite courses
Add InArc to connect Inplace and new course transition.
Add Transition, Place
and Arcs into the Petri net
according to the previous
information
Find all
The prerequisite
courses ? No
Yes
Add outPlace
Save Petri net (myNet)
Into Course.PNML
Add OutArc to connect new transition and outPlace. No inscription
End

38. Determine the logical relationship between those prerequisite courses
Start
Display the prerequisite courses you have entered
Input, among those
Prerequisite courses,
totally student need to finish
at least how
many courses
Display four types of logical relationship between those prerequisite courses (AND, OR, X OUT OF TOTAL, X OUT OF TOTAL while some are mandatory
Get your choice
Switch choice

39. Courses >= Expected Courses? No
Start
Available
Courses >= Expected
Courses? No
Read planning parameters
Yes
Calculate all the combinations
of selecting expected courses
from available courses
Read course KB
Already
Try all the combination
For this semester ?
Yes
Yes
Is this
the first semester ?
Read the student profile No No
Determine the
Ready-to-take courses
Select next combination of
courses to be taken in this
semester, and save this
combination and Petri Net
Move back to the
Previous semester
Get all available courses
among the ‘Ready-to-take’
courses
Restore the previous Petri Net,
Modify the course Petri net to
simulate the activity of taking
those selected courses
End
Use
CPT(C), CPT(E), CPT(I)
to determine the
Preferred courses
Finished
All the required credits ?
Yes
Add all the study records
of all the semesters into
one study plan
Move into next
semester No

40. Courses >= Expected Courses? No
Start
Available
Courses >= Expected
Courses? No
Read planning parameters
Yes
Calculate all the combinations
of selecting expected courses
from available courses
Read course KB
Already
Try all the combination
For this semester ?
Yes
Yes
Is this
the first semester ?
Read the student profile No No
Determine the
Ready-to-take courses
Select next combination of
courses to be taken in this
semester, and save this
combination and Petri Net
Move back to the
Previous semester
Get all available courses
among the ‘Ready-to-take’
courses
Restore the previous Petri Net,
Modify the course Petri net to
simulate the activity of taking
those selected courses
End
Use
CPT(C), CPT(E), CPT(I)
to determine the
Preferred courses
Finished
All the required credits ?
Yes
Add all the study records
of all the semesters into
one study plan
Move into next
semester No

41. Ranking the result
Rank the result according to the Induced Preference Graph from the best to the worst
For each preference, if we find only one plan, end; if we get more than one plans,

42. optional optional optional optional optional mandatory optional (5)
DONE
COMP602
DONE
COMP603
DONE
COMP604
DONE
COMP605
DONE
COMP607
DONE
COMP610
DONE
COMP695
optional
optional
optional
optional
optional
mandatory
optional
(5)
COMP617
DONE
COMP617

43. o o o 5 4 ISC o ISF o o 3 CTE 1 CTP patterns m m m m m COMP602 COMP501
Done
ISC 4
ISF
Done
ISF o
COMP605
COMP504 m o
COMP607
COMP601 m o
COMP610 m
COMP695
COMP667
COMP667
COMP617
COMP617
COMP636
COMP636
COMP648 3
CTE
Done
CTE
COMP648 1
CTP
Done
CTP
COMP660
COMP660
COMP689
COMP689
COMP641
COMP641
COMP674
COMP674

44. Program Done COMP696 1 1 COMP697-9 Choice pattern. COMP697 COMP698
CTE
COMP696
Done
ISF 1
Program
Done
Done
ISC 1
COMP697-9
Done
CTP
Choice pattern.
Done
COMP697
Done
COMP698
COMP697
COMP698
COMP699
Sequential pattern

45. PN COMP636 COMP695 ISC DONE COMP617 COMP605 COMP667 COMP602 COMP504
READY
COMP695
ISC
DONE
COMP617
COMP501
READY
COMP605
COMP667
COMP504
DONE
COMP602
COMP501
READY
COMP504
COMP648
COMP503
DONE
DONE
CTP
COMP603
COMP503
READY
COMP660
COMP503
COMP504
DONE
COMP604
DONE
COMP607
ISF
DONE
COMP689
COMP610
COMP601
READY
COMP504
DONE
CTE
COMP501
READY
COMP641
COMP604
COMP504
READY
COMP601
COMP503
READY
COMP601
DONE
COMP674

46. Program Structure Represented by Petri Nets
mandatory 1 5
COMP695
p11
COMP636 1
p20 p9
optional m 1
COMP605
p12
p28
p10
COMP617
p21 1 m 1 1
COMP602
p13 1 1
COMP667
p22 1
COMP696 m
COMP603
p14 1 1 1 1 m
COMP648
p23 p1
COMP504 p5 1
COMP697 m
COMP607
p15 1 1 1
p16
COMP660
p24
p29 1 m
COMP610 m p2
COMP503 p6 1
COMP689
p25 1
COMP698 1 m 1 1
p30 p3
COMP501 p7 1 1
p18
COMP641
p26 m
COMP699 m p4
COMP601 p8
COMP604
p17 1
p19
COMP674
p27
p31
Foundation
Core
Career Track
Electives
Graduation
Project/Thesis

47. Program Maintenance

48. PNML-represented Curriculum Model
<?xml version="1.0" encoding="ISO "?>
<pnml>
<net id="n1" type="BlackTokenNet">
<name>unnamed</name>
<transition id="COMP501">
<name> <value>1</value> </name>
</transition>
<place id="READYCOMP501">
<marking> <value> 1 </value> </marking>
<name> <value>READYCOMP501</value> </name>
<initialMarking> <value> 1 </value> </initialMarking> …
<arc id="a1" source="READYCOMP501" target="COMP501">
<inscription> <value>MANDATORY</value> </inscription>
</arc>
</net> </pnml>
READYCOMP501
COMP501

49. Steps to Plan-Generation
Read planning parameters (job objectives with their priorities, expected numbers of courses for the following four semesters and the expected graduation year): Ji, Pi, i=1,.., l; ci, i =1, …, 4, g;
get KB from the Ontology WS; Map the Job Objectives to the courses: C[zik]mxn = A[xij]mxk x B[yjj]kxn (m, n, k>0);
Read student’s PNML file from Student Model WS; Initialize a Petri Net, pn; Travel through pn to find the “ready-to-take” courses, Cr;
Get “available courses” next semester from Course Schedule WS, Ca;
Determine the preferred courses with Ji and Pi, and preferred assessment style from Student Model WS;
If the number of Ca < the number of C1, and current semester = 1, end;
Calculate all the combinations of selecting expected courses from available courses;
For each combination
simulate the plan and calculate the utility of the plan by
considering the temporal constraints and credits constraints.

50. An example of plan generation
Student Bill Woods already finished COM501, COMP503, COMP504, COMP601. And he asked eAdvisor to create study plan for him start from Spring 2005 to Fall 2006 (graduation)
From Bill’s Petri Net file “read-to-take” courses are COMP602, COMP603, COMP604, COMP607, COMP610, COMP667, COMP648
Bill’s job objective is Electronic Commerce specialist(JO14). Comp603 exempted. So, eAdvisor finds that the courses matching his job objectives are COMP501, COMP503, COMP504, COMP601, COMP695, COMP604, COMP607, COMP636, COMP648, COMP660, COMP667, COMP689

51. eAdviosr selects the preferred courses based on his job objective from the prerequisite-ready courses, and get the following courses Bill would prefer to take in Spring They are COMP604, COMP607, COMP667, COMP648
eAdvisor calls the course schedule Web service and get to know that only COMP604 and COMP667 are available in Spring So eAdvisor draws the conclusion that the only good choice for Bill in Spring 2005 are
Spring 2005: COMP604 and COMP667.
Fall : COMP607 and COMP648
Winter 2005: COMP695 and COMP697
Spring 2006: COMP698
Fall : COMP699

52. Scenario-based Planning
For Students
What if I want to graduate in two years?
How many classmates will I get if I take course Y?
Any good study patterns to follow?
What if I change my Job Objectives?
For administrators
How many students may take course X next Spring?
How many students will/may graduate next year?
Is there any conflict in course selection and program schedule?
What if our program collaborates with University ABC’s similar program?

53. Conclusions Web Services are currently contributing to shape
the processes of business modeling,
solution creation,
service delivery, and
software architecture design, development and deployment.
Agents and Web Services are complementary
Knowledge modeling is still a bottleneck of the development of such systems

54. Future Work
Factors affecting the performance of the agent in course choosing and scheduling
Software testing of Web Services
Distributed Adaptive Learning Environments: Collaborative Universities
Privacy and Trust Issues

55. COMP501
READY
COMP501

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