Research in the Logic Group - Stanford Computer Science · 2009. 11. 9. · Research in the Logic Group Michael Genesereth ... Handled through magic of networking Format Heterogeneity

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Research in the Logic Group

Michael GeneserethLogic Group

Stanford University

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Computational Logic

above(yellow,blue)on(red,yellow)

∀x.∀y.(on(x,y) ⇒ above(x,y))

∃y.on(blue,y)

¬on(yellow,blue)

on(green,yellow)∨ on(green,blue)

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Automated Theorem Proving

Group Axioms�

Theorem

(x × y) × z = x × (y × z)x × e = xe × x = x

x × x −1 = e

x −1 × x = e

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Constraint Satisfaction Systems

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Database Tables

Queriesquery(X,Z) :- parent(X,Y) & parent(Y,Z)

Constraintsillegal :- parent(X,X)illegal :- parent(X,Y) & parent(Y,X)

Deductive Database Systems

parentart bobart beabea coe

parent(art, bob)parent(art, bea)parent(bob,coe)

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Sample Applications Logical Spreadsheets

Data Integration

Web of Data

Computational Law

General Game Playing

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Logical Spreadsheets

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Huge Success individual users companies conglomerates

Good Features Automatic computation of values Ease of specification using simple math formulas

Computerized Spreadsheets

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Functional formulas

Unidirectional Update

B1 B2 B3

Limitations of Traditional Spreadsheets

2 3

B3 = B1 + B2

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Logical Spreadsheets

Extension Relational constraints on the values of cells

Good Features Automatic computation of values Ease of set-up (using logical formulas)

Challenges Automatic update Temporary inconsistencies User feedback

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yescale3nobobe2noarte1

timeroomprojectorownerevent

eveningafternoonmorning

g300g200g100

classroomnog300

classroomnog200

theateryesg100

SeatingProjectorRoom

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Constraints

If a projector is needed, an event cannot bescheduled in a room without a projector.

illegal :- event(E,O,yes,R,T) & room(R,no,S)

Bob is not permitted to schedule events in g100.

illegal :- event(E,O,P,g100,T) & owner(E,bob)

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yescale3nobobe2

morningg100noarte1TimeRoomProjectorOwnerEvent

eveningafternoonmorning

g300g200g100

classroomnog300

classroomnog200

theateryesg100


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yescale3nobobe2


eveningafternoon

e1morningg300g200g100

classroomnog300

classroomnog200

theateryesg100


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yescale3nobobe2


eveninge2afternoon


classroomnog300

classroomnog200

theateryesg100


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yescale3afternoong200nobobe2morningg100noarte1

TimeRoomProjectorOwnerEvent

eveninge2afternoon


classroomnog300

classroomnog200

theateryesg100


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yescale3afternoong200nobobe2morningg100noarte1


eveninge2afternoon

e3e1morningg300g200g100

classroomnog300

classroomnog200

theateryesg100


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morningg300yescale3afternoong200nobobe2morningg100noarte1


eveninge2afternoon


classroomnog300

classroomnog200

theateryesg100


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morningg300yescale3afternoong200nobobe2morningg100noarte1


e4eveninge2afternoon


classroomnog300

classroomnog200

theateryesg100


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eveningg300e4morningg300yescale3afternoong200nobobe2morningg100noarte1


e4eveninge2afternoon


classroomnog300

classroomnog200

theateryesg100


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Research Topics

Paraconsistent Logic (Eric Kao) reasoning with inconsistent data and constraints

Differential Logic (Mike Kassoff) computing differences from differences analogy with differential calculus

Dynamic Logic (Ashwin Deshpande) constraints on transitions, not just state privacy, security, responsibility workflow management

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Collaborative Spreadsheets

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Data Integration

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Structured Data Sources

Public Sources Company Directories Weather Reports Product Catalogs Airline Schedules Product Reviews Drug Studies

Enterprise Sources Personnel Records Orders Equipment Databases Inventories Room Schedules

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Consumers of Structured Data

Different users Pilots and mechanics and airline marketers Passengers and travel agents FAA and NTSB

Different uses often require different datasets Airline personnel want to see their own flights Passengers want to see all flights for their trips

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Data Integration

Answer

Data Broker

Manufacturer 1Manufacturer 2

Marketplace Data

Query

Product analysis

SatisfactionRatings

Supplier 1Supplier 2


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Complications

Distribution Data at different sites Handled through magic of networking

Format Heterogeneity e.g. relational databases, XML, tab-delimited text Handled through hand-coded translators

*Conceptual Heterogeneity Differences in schema and vocabulary

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20ironsaucepanc04

carter

30ironskilletc0340aluminumsaucepanc0250aluminumskilletc01

pricematerialtypeid

Source 1 - Carter

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saucepanm04

kind

saucepanm03skilletm02skilletm01valueid

Source 2 - Mirkwood

20m04

price

40m0350m0260m01

valueidcoating

yesm02yesm01

valueid

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30msrps03tefloncoatings03

stainlessmaterials03skillettypes03

superchefmakers0350msrpr01

marvel

aluminummaterialr01skillettyper01

renfrewmakerr01valueattributeid

Source 3 - Marvel

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Source 4 - NHMA

nonstick

yesteflonnocoppernoceramic

valueid

americausa

country

europeukeuropefranceamericacanada

areaid

francesuperchef

company

canadarenfrewukmirkwoodusacarter

nationid

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Consumer 1 - Xanadu

50skilletrenfrewr0120saucepancarterc04

xanadu

30skilletcarterc0340saucepancarterc0250skilletcarterc01

pricetypemakerid

All cookware products manufactured in the UnitedStates.

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Consumer 2 - Yankee

40frypanrenfrewr0116potcarterc04

yankee

24frypancarterc0332potcarterc0240frypancarterc01

pricetypemakerid

All cookware products manufactured in the UnitedStates with European types and prices in euros.

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Consumer 3 - Zebulon

zebulon

30skilletsuperchefs0350skilletmirkwoodm0260skilletmirkwoodm01

pricetypemakerid

All skillets manufactured in Europe that are madefrom non-corrosible materials.

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Schemas

Consumer Relations: xanadu(X,Y,Z,W) yankee(X,Y,Z,W) values in European units alproduct(X) ceproduct(X) feproduct(X) ssproduct(X) ncproduct(X)

Source Relations: carter(X,Y,Z,W) kind(X,Y) nonstick(X,Y) marvel(X,Y,Z) coating(X,Y) company(X,Y)

price(X,Y) country(X,Y)

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Relational LogicSafe, Horn Rules grandparent(X,Z) :- parent(X,Y), parent(Y,Z)

Existential Rules parent(X,f(X,Z)) :- grandparent(X,Z)

Disjunctive Rules father(X,Y) | mother(X,Y) :- parent(X,Y)

Recursive Rules ancestor(X,Y) :- parent(X,Y) ancestor(X,Z) :- parent(X,Y), ancestor(Y,Z)

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Direct Mapping

Schema Schema Schema

Schema Schema Schema

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Sometimes called Global-As-View Integration

Source-Based Integration

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Relationships Among Sources

Replicated data Cached data Materialized views (as in data warehouses)

Heterogeneity (different schemas or vocabularies) values in euros versus values in dollars French instead of English different numbers of tables or attributes

Real World Constraints Physical laws Governmental laws Business rules

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Collaborative Data Management

When sources are independent, they can be updatedindependently.

In the face of interrelationships among sources,individuals performing updates must collaborate(explicitly or implicitly) to ensure correct updates.

Collaborative Data Management must replaceindependent data management.

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Data Manager

Data Manager

Database 2

Database 5

Database 1 Database 3

Database 4 Database 6

Metadata

Data

Constraints

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Update Integration

Data Broker

Manufacturer 1Manufacturer 2

Marketplace Data

Update

Product analysis

SatisfactionRatings



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Web of Data

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Future - Web of Data

World Wide Web Collaborative Document Management System Interlinked Documents Keyword Search, Lists of documents as answers Individual source update, source ownership

Web of Data Collaborative Data Management System Objects/relations and constraints Query not Search, Answers instead of Documents Single Entry Principle, data ownership

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Showcase - Digital DepartmentGoal - Enterprise Data Management for Stanford

Components Multiple Departments University Databases

Areas Room reservations Event Management (Equipment, Food, Mail lists) Office Assignments Curriculum (prerequisites, requirements, policies) Programs (Undergraduate, Masters, Doctoral)

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Showcase - Digital GovernmentGoal - Enterprise Data Management for governments

Components Federal Agencies States Local Governments

Output Government people and organizations Bills, votes, laws

Input Taxes, licenses, reports, court proceedings, results Online Turbotax for everything

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