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Strategic Decision Support Systems
Design: Integration Approach
Between Expert Knowledge and
Historical Data
Abdessamed Réda GHOMARI
LMCS « Laboratoire Méthodes de Conception de Systèmes»
INational Institute of Computer Science
BP 68M, Oued Smar, Algiers, Algeria.
Email: a_ghomari@ini.dz
ICTTA'04 April 19-23, 2004 2
Content
Research direction
DSS
Knowledge acquisition
Combined Approach
Conclusion
ICTTA'04 April 19-23, 2004 3
Research Direction
The research work focuses on
Strategic Decision support systems
design
Decision = Knowledge
Information systems: source for DSS
Experience:
CMEP Project (collaboration I.N.I-University
Toulouse1 UFR computer science)
ICTTA'04 April 19-23, 2004 4
DSS: Definitions
Turban defines DSS as
“an interactive, flexible, and adaptable
computer-based information system,
especially developed for supporting the
solution of a non-structured management
problem for improved decision making. It
utilizes data, provides an easy-to-use
interface, and allows for the decision-
makers own insights.”
ICTTA'04 April 19-23, 2004 5
DSS: Definitions
DSSs belong to an environment with
multidisciplinary foundations, including
(but not exclusive)
database research,
artificial intelligence,
simulation methods,
human-computer interaction,
software engineereing and telecommunications
Central Issue in DSS
support and improvement of decision
making
ICTTA'04 April 19-23, 2004 6
DSS: Taxonomy
There is no all-inclusive taxonomy
of DSSs.
Different authors propose different
classifications.
ICTTA'04 April 19-23, 2004 7
DSS: Taxonomy
At the conceptual level, Power 1997
Communication-Driven DSSs,
Data-Driven DSSs,
Document-Driven DSSs,
Knowledge-Driven DSSs
and Model-Driven DSSs.
At the technical level, Power 2000
Entreprise-wide DSS: linked to large data warehouses
and serve many managers in a company.
Desktop single-user DS: small systems that reside on
a individual manager’s PC.
At user level, hattenschwiler 1999
Passive DSS
Active DSS
Cooperative DSS
ICTTA'04 April 19-23, 2004 8
DSS: Other taxonomy
Institutional DSS:
decisions of a recurring nature
Ad Hoc DSS:
specific problems that are usually neither
anticipated nor recurring
Personal, group, and organizational
support
Individual versus group support systems
(GSS)
ICTTA'04 April 19-23, 2004 9
DSS: Components
1. Data Management Subsystem (DMS)
2. Model Management Subsystem (MMS)
3. Knowledge-based (Management)
Subsystem (KMS)
4. User Interface Subsystem (UIS)
5. The User
ICTTA'04 April 19-23, 2004 10
Strategic decision making:
Generic Structure
Dichotomy between
Internal Information
External information
ICTTA'04 April 19-23, 2004 11
SDSS: Architecture
SCM
External know ledge
K now ledge
M odels
Internal
K now ledge
D ecision-m aking
support
K now ledge
M odels
SC M
SCM: Strategic Corporate Memory or Business Memory
ICTTA'04 April 19-23, 2004 12
Corporate Memory
CM content covers various fields.
In the Literature, CM content are:
product requirements,
project tasks and planning,
human expertise involved,
resources used,
project cost elements and structure,
monitoring and control supports,
electronic documents and reports,
design rationales,
lessons learned…
ICTTA'04 April 19-23, 2004 13
Knowledge acquisition: step
of Knowledge management
A company produces goods or services, and, in the
process, also produces knowledge.
Knowledge management(KM): great importance for
companies.
KM objectives: to promote knowledge growth,
communication and preservation in an organization
and from a business point of view, to produce
better business, competitive gain and greater
profits.
ICTTA'04 April 19-23, 2004 14
Knowledge acquisition:
multi-sources
Documented (books, manuals, etc.),
Undocumented (in people's minds),
from Databases,
via the Internet.
ICTTA'04 April 19-23, 2004 15
Knowledge acquisition:
Methods
Three categories of K.A methods [16]
Manual:
Interviewing (Structured, Semistructured,
Unstructured)
Tracking the Reasoning Process
Observing
Semiautomatic:
Support Experts Directly
Automatic (Computer Aided)
Expert’s and/or the knowledge engineer’s roles are
minimized (or eliminated)
Induction Method
ICTTA'04 April 19-23, 2004 16
Automatic method: KDD
ICTTA'04 April 19-23, 2004 17
KDD: « data-pushed approach»
Knowledge management is often
investigated through knowledge discovery
in data (KDD), using raw data mining and
algorithms tools [7].
This approach operate on an a-posteriori
paradigm where data are already stored
and easily available.
ICTTA'04 April 19-23, 2004 18
Combined Approach:
characteristics
Generic Approach with 3 points:
Strategic decisional Process
Decision Support System
Information Systems support
ICTTA'04 April 19-23, 2004 19
Aggregated K: an expertise
Relative importance of the 2 classes
Repetitive Environment
Experts Knowledge: low
Historical Knowledge : high
Non repetitive Environment (case:
Strategic DSS)
Experts Knowledge: high
Historical Knowledge : low
ICTTA'04 April 19-23, 2004 20
Combined approach
Knowledge
Data
base
KDD
process
ExpertsCorporate
Knowledge
Memory
New items New items
DW
process
1
2
2
Decision Makers
Ad hoc
Requests
Data
base
Decision making
support
Models
3 4
1
ICTTA'04 April 19-23, 2004 21
Conclusion
Combined Approach Advantages
Enhanced use or Knowledge reuse pull
approach
Company referential building
Contribution to Improve strategic decision
making
Application
New CNEPRU projet 2004-2008 at LMCS INI
algiers “Platform for Environmental risks
management in industrial projects”
method
Strqtegic DSS

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Ictta04 paper

  • 1. Strategic Decision Support Systems Design: Integration Approach Between Expert Knowledge and Historical Data Abdessamed Réda GHOMARI LMCS « Laboratoire Méthodes de Conception de Systèmes» INational Institute of Computer Science BP 68M, Oued Smar, Algiers, Algeria. Email: a_ghomari@ini.dz
  • 2. ICTTA'04 April 19-23, 2004 2 Content Research direction DSS Knowledge acquisition Combined Approach Conclusion
  • 3. ICTTA'04 April 19-23, 2004 3 Research Direction The research work focuses on Strategic Decision support systems design Decision = Knowledge Information systems: source for DSS Experience: CMEP Project (collaboration I.N.I-University Toulouse1 UFR computer science)
  • 4. ICTTA'04 April 19-23, 2004 4 DSS: Definitions Turban defines DSS as “an interactive, flexible, and adaptable computer-based information system, especially developed for supporting the solution of a non-structured management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows for the decision- makers own insights.”
  • 5. ICTTA'04 April 19-23, 2004 5 DSS: Definitions DSSs belong to an environment with multidisciplinary foundations, including (but not exclusive) database research, artificial intelligence, simulation methods, human-computer interaction, software engineereing and telecommunications Central Issue in DSS support and improvement of decision making
  • 6. ICTTA'04 April 19-23, 2004 6 DSS: Taxonomy There is no all-inclusive taxonomy of DSSs. Different authors propose different classifications.
  • 7. ICTTA'04 April 19-23, 2004 7 DSS: Taxonomy At the conceptual level, Power 1997 Communication-Driven DSSs, Data-Driven DSSs, Document-Driven DSSs, Knowledge-Driven DSSs and Model-Driven DSSs. At the technical level, Power 2000 Entreprise-wide DSS: linked to large data warehouses and serve many managers in a company. Desktop single-user DS: small systems that reside on a individual manager’s PC. At user level, hattenschwiler 1999 Passive DSS Active DSS Cooperative DSS
  • 8. ICTTA'04 April 19-23, 2004 8 DSS: Other taxonomy Institutional DSS: decisions of a recurring nature Ad Hoc DSS: specific problems that are usually neither anticipated nor recurring Personal, group, and organizational support Individual versus group support systems (GSS)
  • 9. ICTTA'04 April 19-23, 2004 9 DSS: Components 1. Data Management Subsystem (DMS) 2. Model Management Subsystem (MMS) 3. Knowledge-based (Management) Subsystem (KMS) 4. User Interface Subsystem (UIS) 5. The User
  • 10. ICTTA'04 April 19-23, 2004 10 Strategic decision making: Generic Structure Dichotomy between Internal Information External information
  • 11. ICTTA'04 April 19-23, 2004 11 SDSS: Architecture SCM External know ledge K now ledge M odels Internal K now ledge D ecision-m aking support K now ledge M odels SC M SCM: Strategic Corporate Memory or Business Memory
  • 12. ICTTA'04 April 19-23, 2004 12 Corporate Memory CM content covers various fields. In the Literature, CM content are: product requirements, project tasks and planning, human expertise involved, resources used, project cost elements and structure, monitoring and control supports, electronic documents and reports, design rationales, lessons learned…
  • 13. ICTTA'04 April 19-23, 2004 13 Knowledge acquisition: step of Knowledge management A company produces goods or services, and, in the process, also produces knowledge. Knowledge management(KM): great importance for companies. KM objectives: to promote knowledge growth, communication and preservation in an organization and from a business point of view, to produce better business, competitive gain and greater profits.
  • 14. ICTTA'04 April 19-23, 2004 14 Knowledge acquisition: multi-sources Documented (books, manuals, etc.), Undocumented (in people's minds), from Databases, via the Internet.
  • 15. ICTTA'04 April 19-23, 2004 15 Knowledge acquisition: Methods Three categories of K.A methods [16] Manual: Interviewing (Structured, Semistructured, Unstructured) Tracking the Reasoning Process Observing Semiautomatic: Support Experts Directly Automatic (Computer Aided) Expert’s and/or the knowledge engineer’s roles are minimized (or eliminated) Induction Method
  • 16. ICTTA'04 April 19-23, 2004 16 Automatic method: KDD
  • 17. ICTTA'04 April 19-23, 2004 17 KDD: « data-pushed approach» Knowledge management is often investigated through knowledge discovery in data (KDD), using raw data mining and algorithms tools [7]. This approach operate on an a-posteriori paradigm where data are already stored and easily available.
  • 18. ICTTA'04 April 19-23, 2004 18 Combined Approach: characteristics Generic Approach with 3 points: Strategic decisional Process Decision Support System Information Systems support
  • 19. ICTTA'04 April 19-23, 2004 19 Aggregated K: an expertise Relative importance of the 2 classes Repetitive Environment Experts Knowledge: low Historical Knowledge : high Non repetitive Environment (case: Strategic DSS) Experts Knowledge: high Historical Knowledge : low
  • 20. ICTTA'04 April 19-23, 2004 20 Combined approach Knowledge Data base KDD process ExpertsCorporate Knowledge Memory New items New items DW process 1 2 2 Decision Makers Ad hoc Requests Data base Decision making support Models 3 4 1
  • 21. ICTTA'04 April 19-23, 2004 21 Conclusion Combined Approach Advantages Enhanced use or Knowledge reuse pull approach Company referential building Contribution to Improve strategic decision making Application New CNEPRU projet 2004-2008 at LMCS INI algiers “Platform for Environmental risks management in industrial projects” method Strqtegic DSS