Intelligent Information Systems
10. Educational Challenges.
Projecting the Future.
Gio Wiederhold
EPFL,
April-June 2000, at 14:15 - 15:15, room INJ 218
Schedule
Presentations in English -- but I'll try to manage discussions in French and/or German.
• I plan to cover the material in an integrating fashion, drawing from concepts in
databases, artificial intelligence, software engineering, and business principles.
1. 13/4 Historical background, enabling technology:ARPA, Internet, DB, OO, AI., IR
2. 27/4 Search engines and methods (recall, precision, overload, semantic problems).
3. 4/5 Digital libraries, information resources. Value of services, copyright.
4. 11/5 E-commerce. Client-servers. Portals. Payment mechanisms, dynamic pricing.
5. 19/5 Mediated systems. Functions, interfaces, and standards. Intelligence in
processing. Role of humans and automation, maintenance.
6. 26/5 Software composition. Distribution of functions. Parallelism. [ww D.Beringer]
7. 31/5 Application to Bioinformatics.
8. 15/6 Semantic Interoperation
9. 22/6 Privacy protection and security. Security mediation.
10.29/6 Educational challenges. Expected changes in teaching and learning.
Summary and projection for the future.
• Feedback and comments are appreciated.
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Open question?
• Web enables remote education
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Stanford Model
• Based on TV courses offered to industry
• Part of normal curriculam
– TV operator in special classroom
shows notes (must be legible), blackboard, teacher
– tutor at remote site (has taken class earlier)
– voice link for questions (if live TV)
• Can be replayed on web in students rooms, …
– morning classes getting to be empty
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Threat to smaller schools
Alternatives
• Overloaded professor with older material
• Inaccessible professor with up-to-date material
– technology from the entertainment industry
• Education when and where wanted
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E-libraries
www.TeleRead.org
• Public libraries (funded in the U.S. by Dale Carnegie)
have been of major benefit to children and older
people who wanted to improve themselves
• Libraries and librarians are under budget pressures
– Older voters do not support education
• In an E-world at least augment paper libraries
• Proposal [Rothman@clark.net] free E-libraries using
– Internet and e-books
• Contents
– Books in public domain
– Popular books; suggestion that their copyrights be donated
by their authors after initial sales reduce -- ~1-2 years
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Added Value
• Virtually everything we have is in the public domain. Our
value added is not communications. It is not hardware. It is
not even the data. Our Value added is the categorization,
the storage, and the archiving of data, which gives the tools
to search the data, to project it, compare it, chart it, and so
on. Our strategy is to be a high-margin, low-volume
producer for a specialized market. [Michael R. Blomberg,
in Wired April 1998]
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Nothing is harder to predict than the future [Yogi Berra]
•
Heavier-than-air flying machines are impossible.
•
Radio has no future.
•
X-rays will prove to be hoax. [Willliam Thomson, Lord Kelvin, 18241907].
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Industry Needs Information
• Engineering and Manufacturing
 own capability
 demand


suppliers’ capabilities
global demand
• Distribution and Transportation
 costs for alternate means of shipping
• Finance
 project demand 3 project cost of funds
• Marketing and Service
 taste and style

demographics
more from remote sources
Data and Knowledge
Information is
created at the
Storage
confluence of
Education
data -- the state
Selection
Recording
&
knowledge -Integration
the ability to
select and
Abstraction
Experience
State changes
project the
Decision-making
state into
the future
Action
Knowledge Loop
Data Loop
Knowledge Manifestations
• Procedural
– system analysts
– programmers
• Declarative
– domain analysts
– knowledge engineers
– rule writers
• Creators
}-{
• faster
• Maintainers
• easier
Information Leverage
Tactical
• Customers
• Inventory
• Suppliers
Strategic
• Planning
• Capabilities
• Opportunities
Projects at Stanford DB group
Data Mining.
Mediator & Wrapper
Generation.
Warehousing.
Security Mediators.
Megaprogramming.
Simulation Access.
Changes, Consistency,
and Configurations.
MIDAS
WHIPS
TSIMMIS
TIHI
C3 CHAIMS
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SimQL
13
XML
Machine Processable !
• return to origin?
–
–
–
–
–
–
–
–
ARPAnet -- share heterogeneous machines
Email -- people-to-people
Digital Library -- people-to-machines
E-commerce (E2B)-- people-to-machines
• client-server
Mediated -- people-to-services-to-machines
Business (B2B)-- machine-to-machine(s)
Business services -- machine-to-services-to-machines
Ubiquitous -- gadget-to-gadget
• (embedded)
Future
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XML: the end of Databases?
<panel chair_name=Daniela Florescu loc=INRIA>
<name> Catriel Beeri<at>HUIJ</at></name>
<name> Adam Bosworth<at>Succendo</at></name>
<name> Guy Ferran<at>INRIA</at></name>
<name> Michael Ries<at> Microsoft</at></name>
<name> Martin Schulze<at> Excelon</at></name>
<name>Gio Wiederhold<at>Stanford</at></name>
</panel>
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DBMSs will serve XML
• XML will help in use of DBs on the web.
• HTML is wild and wooly, oriented towards flexible
human processing
• For B2B applications interpretation will be by
processing programs.
• Programs cannot exploit flexibility.
• Data requirements remain regular
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Relevant Examples?
 Browsing for papers and authors
 human searching
 flexible contents
 recursive, cyclic, linked structures
 Supplier catalog
+ some number of required entries
+ some predefined optional entries
+ further, arbitrary entries are ignored.
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Needed features from DBMs
• Rapid retrieval  indexing
– indexes best derived from regular structures
• Optimization  quantity estimates
– best attached to schemas,
• Business integrity Tx integrity
– fine-grained, routinely provided by DBMSs.
• Access control  constraint rules
– attached to schema attributes and keys
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Will RDBMs adapt to XML?
Changes will be needed for XML correctness
– Ordering, - cross refs., - document snippets, ...






Pressure from customers
Experience from OO-developments
Inadequate rethinking
Rigid internal structure
Staff with performance-oriented experience
XML-specialists mis-focusing
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Future Information
Systems
Gio Wiederhold
Stanford University
Information Systems should also
Project into the Future
past
now
future
time
Databases,
accessed via SQL or
CORBA compliant
wrappers
Msg
systems,
sensors
Simulations,
accessed via SimQL and
compliant wrappers
Use of Simulation Results
Simulation results can be composed for
Alternative Courses-of-actions
Composition should be seamless, elegant, with
computation and recomputation of likelihoods
Results change as now moves forwards and eliminates
earlier alternatives.
DM support is disjoint
does not interoperate
Planning Science
Distribution
extensions to move
to networked support
are also disjoint
Current state of DM Support
past
now
time
organized support
Data integration
Databases
distributed, heterogeneous
future
disjointed support
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nsnd nn 23.5a
Intuition +
• Spreadsheets
• Planning of allocations
• Other simulations
various point assessments
Technology Transition
• Economic drivers have to be considered.
• Three party model
• Industry: need-based invention
• academia: formalization
• innovators: new technology
• New Service models provide new Opportunities
• supply innovative tools to industry
• supply specialized information to industry
I
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Understanding the other parties
Motivation is profit and loss avoidance of
• Industry: investment -– payoff to stockholders / retain value / stable
• Academia: prestige -- (leads to continuing funding)
– visibility, not stability or reliability
• Innovative businesses: leverage -- not sustainable
– low downside cost, high upside risk,
– change expected and needed
• Government research:
– technology dissemination & shelving service ?
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Notes from Stanford OTL
•
SoE inventions
–
–
–
•
31 % licensed / 29% waiting, 40%dropped
Large companies poor adopters, best are exclusive to startups
Center for Networking has an overll license, pay once + small annual
New Licensed Field of Use: EPIC program
–
–
–
–
–
–
–
–
–
–
–
excludes software - but same pricing scheme
excluded are already active inventions
inventions co-developed outside of SoE
inventions not pursued by Stanford
base cost for membership 400K or 100K for 5 years
100K non-exclusive license per inventions before patent is granted
200K non-exclusive license per invention after patent is granted
easy access for 6-months
SOE gets base, can distribute to depts. Inventor gets license fees.
objective is better relationships.to large companies
not suitable for startups, small companies
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Research economy transfer paths
Product suppliers (PS)
people
results
high
volume
Customers
Taxes
high-value
modest volume
Products
Tool suppliers (TS) versus
Research
Government
Teaching
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Gaps in MI Science/Systems
Application of CS, …, to Healthcare
• understand CS, don’t try forcefully contribute
• understand the differences in
– the research and validation proof vs. case
– the practice paradigms broad vs. focused
• supply CS with data? NASA earth sciences
• apply the best you can find
• compose it
•
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Moving to a Service Paradigm
• Server is an independent contractor, defines service
• Client selects service, and specifies parameters
• Server’s success depends on value provided
•
Some form of payment received for services
x,y
Databases are a current example.
Simulations have the same potential.
Operating Systems
• Microsoft Windows, personal computer and WS.
proprietary product, no obligations to hardware,
rapidly adapted to new requirements
• UNIX, an open systems, consensus and takes time.
• SUN servers
• LINUX clients and servers, free, low entry cost
• ….
• Mainframe operating systems, little growth expected
• VMS (COMPAQ) reliable 24 hour / 7 day
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1 Pre-competitive development.
2 Integration and Marketing
3. Problem: Asynchrony.
3.1 Industry-driven. research.
3.2 Curiosity-driven research.
3.3 Fundamental research
3.4 Transition windows
4 Transition agents.
4.1 Link academic researchers to industry
4.2 Link academic and industrial research.
4.3 Startup companies.
4.4 Incubator services.
4.5 Research stores.
Commercial Technology Transfer Company.
Governmental Technology Transfer Institute.
Other candidate organization models for research stores.
5 Research Venues and Technology Transfer.
6 Summary
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New Role for Consultants
Old
• Used at Design Time
and
• To Explain Failures
Future
• Available as a Service
• Responsible for Knowledge
Maintenance
Integration Science
Databases
access
storage
algebras
Systems
Engineering
analysis
documentation
costing
Artificial
Intelligence
knowledge mgmt
models
uncertainty
Integration
Science
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Fin
Comments?
• what was new / what was old or boring?
• future emphasis
– more technological detail?
– more situational detail?
– more extrapolation to the future
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