Intelligent Fril/SQL Interrogator Dong (Walter) Xie Supervisor : J. F. Baldwin 2003 - 2004

Intelligent Fril/SQL Interrogator

Dong (Walter) Xie

Supervisor : J. F. Baldwin

2003 - 2004

Why We need Computers in Business

To Store, Retrieve, Analyse and Integrate Information Database system, Information system Data mining, Machine learning, Fuzzy logic system Procedural programming language, Logic programming language

Never Tired, Never Complain, Less Mistake, Less Paid To Communicate in The World

Email, Internet phone, Internet fax

Some of Popular Systems in Business BI (Business Intelligence) ERP (Enterprise Resource Planning) CRM (Customer Relationship Management)

SAP, Oracle, Sagent, Informatica, SAS, SPSS, Business Objects, Cognos, IBM … …

Intelligent Fril/SQL Interrogator

Powerful Query System Standard SQL query Fril query — querying the facts and rules to give the answer with support pair Natural language query

Fril Support Logic Programming + Knowledge Bases + Fuzzy Logic System (Graphic Fuzzy Sets Interface)

Fuzzy Logic Data Mining & Machine Learning Toolkit Object Oriented

Reusable, structural, adaptable, information hiding, reliable

Fril (Fuzzy Relation Inference Language) & its Resources

Fril is a support logic programming language which includes Prolog as a subset of the language, but which also allows probabilistic uncertainties and fuzzy sets to be included. Fril and its applications are described in detail in “Fril – Fuzzy and Evidential Reasoning in Artificial Intelligence”, J. F. Baldwin, T. P. Martin and B. W. Pilsworth. Trevor Martin’s homepage http://www.enm.bris.ac.uk/ai/martin/downloads/FrilResources.html

http://www.enm.bris.ac.uk/ai/fril.html

Intelligent Fril/SQL Interrogator Flow Graph

… ….

Database tables & Output tables

ODBC

JDBC

Database drivers

User

IntelligentFril/SQL Interrogat

or

CFril

JFril

Knowledge Base

Fuzzy Logic Data Mining Toolkit

Fril

Fril interface

Natural Languageinterface

Structure of Objects

1 Database tables2 Output tables from other objects3 Knowledge bases from other objects

Slot 1 SQL query using tables in the inputs list

Slot 3 Fril wh query given by rule with result as head

Inputs List

Processed a temporary database TD

Fril wh query to provide smart answers

Slot 2 or 3 can be empty. Temporary database TD will be passed down from slot 1 to the last slot in sequence, and training set of slot 2 automatically formed from TD Output of the object comes out from the last slot.

Output of one object can link to other objects as input

Slot 2 Machine learning toolkit to generate Fril knowledge base, or Fril method written by user

Output List

either 1 Output tableor 2 Knowledge base

Provided Fril method or Fril knowledge base for Fril queries

Fuzzy Partition List

FP1FP2

Object Slots

Object Inputs

Output Table

1 SQL Slot

3 Fril Query Slot

2 Knowledge Base Slot

Machine Learning Toolkit

Customized by User

Knowledge Base

2 & 3 empty

2 empty

If 3 empty, the object outputs knowledge base; if 3 not empty, it outputs output table.

Fril Knowledge Base in Slot 2

Generated automatically by machine learning toolkit IF… THEN… fuzzy logic rules, fuzzy ID3 decision tree, Bayesian network, fuzzy association rules or prototypes The training set is automatically provided by the result set of SQL query in slot 2 The knowledge base can be edited by user

Customized by user Fril method written by user Some theorem in Fril format

Fuzzy Logic Machine Learning Toolkit

Original Database

Effective Reduced Database

Fuzzy ID3 Decision Tree

Fuzzy ID3 Decision Tree

Fril general rule

Fril general rule

Fril general rule

Fuzzy clusters

Fuzzy Logic rules

Bayesian Net

prototypes

Evidential logic rules

Fuzzy clusters

Fuzzy Logic rules

Fuzzy Association rules

Temporary Fril Knowledge BaseBefore using Fril query, we first extract data from temporary database from SQL query to form a temporary Fril knowledge base of clauses.

For example:temp =

name wt height

John 144 20

Jill 156 22

Pat 120 18

Bill 153 21

((temp John 144 20))((temp Jill 156 22))((temp Pat 120 18))((temp Bill 153 21))

We obtain Fril knowledge base

The Fril query then operatesusing this temporary knowledge baseResult of query put into output table

Fuzzy Partitions in the Object

Simple Fuzzy Partition :

A simple fuzzy partition {fi} is a set of triangular or

trapezoidal fuzzy sets such that

for any data point x X where X is the universal set.

1)x( i

fi

E q ua l s p ac e fuzzy s e ts

...

n 2 1

a b

... 3 F u zz y s e t

1-n

a-ba

1-n

a)-2)(b-(na

E q u a l d a ta p o in ts f u z z y s e ts

...

n 2 1

a b

... 3 F u zz y s e t

)1-n

2)m-(n( a val)

1-n

m( a val

m / (n - 1 ) m / (n - 1 ) m / (n - 1 ) m / (n - 1 ) N u m b e r o f i n s ta n c e s

Fuzzy partitions listed in the object are used to explain fuzzy sets in Fril method or answer Fril query

Fril Query in Slot 3 & its Result

Fuzzy Set(short [0:1, 2:0.7, 3:0])

Temporary Knowledge Base((case 1 a 2))((case 2 b 1))((case 3 c 3))

Fril Query in Slot 3(X (findall (T SUPPORT) ((supp_query ((case R T short)) (SUPPORT P)) (less 0 SUPPORT)) X))

Solution in Fril format((a 0.7) (b 0.85))

Transferred to output table as

T SUPPORT

ab

0.70.85

Object’s Output Table

Note: Fril query in slot 3 is equivalent to a wh query, but word wh has been omitted

Linking Objects

Output table 1Database 1

SQL 1

Fril Query 1

Object 1

Output table 2

SQL 2

Fril Query 2

Object 2

Output table 4

SQL 4

Fril Query 4

Object 4

Knowledge base 3

SQL 3

Object 3

Machine learning toolkit

Database 2

Applications in Business and Commerce

It can be either integrated with other system AI part of these systems, such as BI, ERP, CRM, or E-Commerce … …

Or can be used alone Supermarket Basket Analysis Risk Analysis (Foreign Trade)

Fraud Detection (APACS London reported £373.7 million losses through credit card fraud in the 12 months ending August 2001)

Advertisement Analysis Commodities, Currencies, Stocks & Futures Market Analysis

Other Applications

Engineering Intelligent CAD and CAM Engineering design … …

Science Intelligent personal identification library (face, fingerprint, DNA) Flood or earthquake prediction Virtual chemical combination

Medicine, Biology and Genetics Intelligent gene library Diagnostic expert system

Supermarket Basket Example Database

Customer table contains the personal details of customers

Transaction table contains supermarket basket scanner panel data

Product table contains the commercial and nutritional information published in web

Product Table & Fuzzy Partition of Sugar Content

UPC (universal product code)

ProductName

Fuzzy set defined by manager or user

Price Vegetarian Energy Protein Sugar …

Product Table (nutrition)

Crispy Chilli Beef

£ 2.96(unit)

2014730 No(Yes / No)

23.8 (g per 100g)

11.9 (g per 100g)

318(kcal per

100g)

Sainsbury's online shop : http://www.sainsburystoyou.com/arriving/login.jsp

29.2g (per 100g)0

1low

Fuzzy Sets of Sugar Content

medium high

Example of Linking Objects

Output table 2Transaction

SQL 2

Object 2


SQL 3

Object 3

Output table 1Product

SQL 1

Fril query 1

Object 1

FP1

Object 1

Object 1

Customized rules

Knowledge base 5

Customer

SQL 5

Object 5


SQL 4

Object 4

Output table 7Customer

SQL 7

Fril query 7

Object 7

Output table 6

SQL 6

Fril query 6

Object 6

Object 4

Machine learning toolkit

Object 4

CustomerObject 4Object 5

Customized knowledge

Object1 — Support of Low Sugar Content

List the items whose membership of that sugar content being low

UPC, ProductName, Price, Sugar, Support

Product

SELECT UPC, ProductName, Price, Sugar FROM Product ORDER BY UPC

gives

UPC

in Output Table(X (findall (UPC PRODUCTNAME PRICE SUGAR SUPPORT) ((supp_query ((case R UPC PRODUCTNAME PRICE object1_sugar_low)(case R UPC PRODUCTNAME PRICE SUGAR)) (SUPPORT P)) (less 0 SUPPORT)) X))

…object1_sugar

Object2 — Total Price of Low Sugar Content Food

Items, SaleTotalPrice, LabelTotalPrice, Support

SELECT Sum(Transaction.ItemQuantity) AS NumOfItems,

Sum(Transaction.TotalPriceOfTheItem) AS SaleTotalPrice,

Sum(Transaction.ItemQuantity*FocusOutputTable_1.Price) AS LabelTotalPrice,

FocusOutputTable_1.Support FROM Transaction INNER JOIN

FocusOutputTable_1 ON Transaction.UPC=FocusOutputTable_1.UPC WHERE

Transaction.Date<#10/10/2004# And Transaction.Date>#1/10/2004# GROUP BY

FocusOutputTable_1.Support ORDER BY FocusOutputTable_1.Support

Object2 sums up the number of items sold from 01/10/2004 to 10/10/2004, their total price and the total price originally labelled according to the distribution of support of Object1, where the support is the membership of that sugar content being low


Product


(X (findall (UPC PRODUCTNAME PRICE SUGAR SUPPORT) ((supp_query ((case R UPC PRODUCTNAME PRICE object1_sugar_low)(case R UPC PRODUCTNAME PRICE SUGAR)) (SUPPORT P)) (less 0 SUPPORT)) X))

Object1 Object2

Transaction, Object1

Chart of Object2 Output Table (1)

NumOfItems

SaleTotalPrice LabelTotalPriceSuppor

t

1 2.37 2.96 0.1849

11 16.39 16.39 0.7979

2 3.78 5.78 0.8493

1 0.17 0.27 0.9931

11 62.25 70.71 0.9966

1 1.09 1.68 1

Object2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.17 1.09 2.37 3.78 16.4 62.3

Total Price Sold (£)

Supp

ort

Support – Total Price Bar Chart & Fuzzy Set Tendency Line

Based on Low Sugar Content Fuzzy Set


NumOfItems

SaleTotalPrice

LabelTotalPrice

Support

1 2.37 2.96 0.1849

11 16.39 16.39 0.7979

2 3.78 5.78 0.8493

1 0.17 0.27 0.9931

11 62.25 70.71 0.9966

1 1.09 1.68 1

Object2 Total Price – Support Bar Chart & their Tendency Lines


1 0 0

1115

0

10

20

30

40

50

60

70

80

0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1

Support

Tot

al P

rice

(£)

Number of Items

Total Price Sold

Total Price Labelled

Poly. (Total PriceSold)Poly. (Total PriceLabelled)Poly. (Number ofItems)

Object3 — Marketing Segmentation or Customer Personalization

Customer Informaton, Items, SaleTotalPrice, LabelTotalPrice, Support

SELECT Customer.CardNumber, Customer.FirstName, Customer.Surname, Sum(Transaction.ItemQuantity) AS NumOfItems, Sum(Transaction.TotalPriceOfTheItem) AS SaleTotalPrice, Sum(Transaction.ItemQuantity*FocusOutputTable_1.Price) AS LabelTotalPrice, FocusOutputTable_1.Support FROM Customer INNER JOIN (Transaction INNER JOIN FocusOutputTable_1 ON Transaction.UPC = FocusOutputTable_1.UPC) ON Customer.CardNumber = Transaction.CardNumber GROUP BY FocusOutputTable_1.Support, Customer.CardNumber, Customer.FirstName, Customer.Surname ORDER BY Customer.FirstName, Customer.Surname

If we constrain SQL in Object3 to search an individual or a group of customers with personal information, such as name, age, post code, etc., we are able to find customers’ favourites by individual or groups.


Product


(X (findall (UPC PRODUCTNAME PRICE SUGAR SUPPORT) ((supp_query ((case R UPC PRODUCTNAME PRICE object1_sugar_low)(case R UPC PRODUCTNAME PRICE SUGAR)) (SUPPORT P)) (less 0 SUPPORT)) X))

Object1 Object3

Transaction, Object1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.23 2.52 2.81 3.78

Total Price Sold (£)

Supp

ort


NumOfItems

SaleTotalPrice

LabelTotalPrice

Support

1 1.23 2.72 0.0411

1 2.81 2.96 0.1849

2 3.78 5.78 0.8493

2 2.52 3.7 1

Object3


Customer name = “Dong Xie” Support – Total Price Bar Chart

& Fuzzy Set Tendency Line

2

0 0 0

4

0

1

2

3

4

5

6

7

8

9

10

0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1

Support

Tot

al P

rice

(£)

Number of Items

Total Price Sold

Total Price Labelled

Poly. (Total PriceSold)Poly. (Total PriceLabelled)Poly. (Number ofItems)


NumOfItems

SaleTotalPrice

LabelTotalPrice

Support

1 1.23 2.72 0.0411

1 2.81 2.96 0.1849

2 3.78 5.78 0.8493

2 2.52 3.7 1

Object3


Customer name = “Dong Xie” Total Price – Support Bar Chart

& their Tendency Lines

Marketing Segmentation or Customer Personalization Report

To analyse customer behaviour and favourite, we plot ordering support graphs in number of items, total price, …, with respect to the energy, sugar content, fat content, …, and price, discount, …, etc.

Fat PriceEnergy Protein Sugar …

low

medium

high

0

2

4

6

8

10

12

0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1

0

2

4

6

8

10

12

0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1

…

… … ……

Fuzzy Set

Report an individual customer OR a certain group of customers

2

0 0 0

4

0

2

4

6

8

10

12

0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1

Object5 — Customers’ Shopping Frequency & Average Spending

Transaction ID Date Card Number UPC Item Quantity Total Price

SELECT DISTINCT DateOfTrans, CardNumber, SUM(TotalPriceOfTheItem) AS TotalPrice FROM Transaction GROUP BY DateOfTrans, CardNumber ORDER BY DateOfTrans

Transaction Table (basket scanner data)

Name Shopping Frequency Average Spending Customer Rank

= Price Item

Object4

Object8 (= Object5 SQL)SELECT Count(FocusOutputTable_4.CardNumber) AS ShoppingFreq, Avg(FocusOutputTable_4.TotalPrice) AS AvgSpending, Customer.FirstName, Customer.Surname, Customer.Rank FROM Customer INNER JOIN FocusOutputTable_4 ON Customer.CardNumber=FocusOutputTable_4.CardNumber WHERE Customer.Rank > 0 GROUP BY Customer.FirstName, Customer.Surname, Customer.Rank

Customer rank can be adjusted by manager or analyser

Object5 — Knowledge Base Learned by Data Mining Toolkit

Fuzzy Logic Data Mining Toolkit

Training Set (SQL Result)

Fuzzy Sets

General Fril Rule

Simple Fuzzy Logic Rule

Fuzzy Decision Tree

IF … THEN … rule

The output of Object5 is the knowledge base, which can be linked into the input list of other objects

The Best Customers Definition from Knowledge Base

1 X is RarelyRarely (rank_fuzzy_set_1) Best Customer IF his/her Shopping Frequency is RareRare (shoppingfreq_fuzzy_set_1)

2 X is HighlyHighly (rank_fuzzy_set_3) Best Customer IF his/her Shopping Frequency is HighHigh (shoppingfreq_fuzzy_set_3)

3 X is FairlyFairly (rank_fuzzy_set_2) Best Customer IF his/her Shopping Frequency is FairFair (shoppingfreq_fuzzy_set_2)

Object5’s knowledge base (learned by fuzzy logic data mining toolkit)

Object7’s knowledge base (customized by user)

Object6 & Object7 — To Infer the Rank of Each New Customer

Form temporary knowledge basewhich is passed to Fril

Fril query to infer the rank of each new customer

Who are the best customers?

Object6 Load knowledge base in Fril, before to process slots[1] Customer Table

[2] Object4[3] Object5 Knowledge BaseSELECT Count(FocusOutputTable_4.CardNumber) AS ShoppingFreq, Avg(FocusOutputTable_4.TotalPrice) AS AvgSpending, Customer.FirstName, Customer.Surname, Customer.Rank FROM Customer INNER JOIN FocusOutputTable_4 ON Customer.CardNumber=FocusOutputTable_4.CardNumber WHERE Customer.Rank is null GROUP BY Customer.FirstName, Customer.Surname, Customer.Rank

(X (findall (FIRSTNAME SURNAME NEW_RANK) ((supp_query ((rank is rank_fuzzy_set_2 R)(case R SHF ASP FIRSTNAME SURNAME RANK)) (NEW_RANK P)) (less 0 NEW_RANK)) X))

FirstName, Surname, New_Rank

((term R SHF ASP)(case R SHF ASP FIRSTNAME SURNAME RANK))

Fril program to infer the rank of each new customer

Object7 — Knowledge Base Customized by User

[1] Customer Table[2] Object4

SELECT Count(FocusOutputTable_4.CardNumber) AS ShoppingFreq, Avg(FocusOutputTable_4.TotalPrice) AS AvgSpending, Customer.FirstName, Customer.Surname, Customer.Rank FROM Customer INNER JOIN FocusOutputTable_4 ON Customer.CardNumber=FocusOutputTable_4.CardNumber WHERE Customer.Rank is null GROUP BY Customer.FirstName, Customer.Surname, Customer.Rank

Fril program written by user

Knowledge base customized by user provides flexibility of system, which can be rules in Fril format or theorem (Fril program).

(X (findall (FIRSTNAME SURNAME NEW_RANK) ((supp_query ((rank is rank_fuzzy_set_2 R)(case R SHF ASP FIRSTNAME SURNAME RANK)) (NEW_RANK P)) (less 0 NEW_RANK)) X))

FirstName, Surname, New_Rank

Object7

Fuzzy logic rules defined by experienced expert

(avgspending_fuzzy_set_2 [ 47.108 : 0 99.497 : 1 ] )

((rank is rank_fuzzy_set_2 R)(term R X1 avgspending_fuzzy_set_2))

((term R SHF ASP)(case R SHF ASP FIRSTNAME SURNAME RANK))

A Simple Flight Routes Example

343 km 874 km

Edinburgh

Paris

Hongkong

Beijing

London Frankfurt Amsterdam

535 km

8238 km 7811 km 8161 km

9740 km

2018 km

660 km

7844 km

1024 km

In the example of fight routes query system, we design a logic object shown in the next slide to list all possible routes from Edinburgh to Beijing, their total distances and total number of stops associated.

Object 1 — Flight Routes Query System

Object1

[1] Flight

SELECT DISTINCT DepartureAirport, Destination, Distance FROM Flight

(X (findall (ROUTE_LIST TOTAL_DISTANCE TOTAL_STOP) ((travel Edinburgh Beijing 0 TOTAL_DISTANCE 0 TOTAL_STOP ROUTE_LIST)) X))

Route_List, Total_Distance, Total_Stop

((travel DEPAR DEST S1 TOTAL_DIS TOTAL_STOP TOTAL_STOP (DEPAR | (DEST | ( )))) (case INDEX DEPAR DEST DIST)(sum S1 DIST TOTAL_DIS))

((travel DEPAR DEST S1 TOTAL_DIS S2 TOTAL_STOP (DEPAR | ROUTE_LIST)) (case INDEX DEPAR Z DIST)(sum S1 DIST SD)(sum S2 1 ST)(travel Z DEST SD TOTAL_DIS ST TOTAL_STOP ROUTE_LIST))

traveltravel is a recursive definition in Fril containing 7 parameters.

The first traveltravel is the terminating condition of the recursion.

Object 1 Output Table

It is obviously that using conventional programming languages, such as C++, Java, Fortran, etc, has to cost more code than the two-line simple code in last slide. Nor SQL can implement this example just using two-line simple code. This is one of the big advantages to use Fril compared with other programming languages.

Product Recommendation System Example

Energy Production Vitamin Fat Salt Sugar Fibre

Bas

kets

Find overlapping clusters and name each cluster e.g. healthy eater,junk eater, etc. A point will have membership in each cluster

Cost

Each cluster can be represented by a fuzzy prototype.

… …

The Hybrid Fuzzy Expert System

New Basket

FilterSelect useful

pattern

Delete incompatible

pattern

Healthy Eater

Greedy Buyer

Vegetarian

… …

0.5

0.2

0.8

Classify new customer

Association rules

associated with

each fuzzy cluster

… …

List of recommendations

with rating

.

.

.

Semantic Distance

The products already in the basket will not appear in the recommendation list

Fuzzy Prototypes

The Fuzzy Prototype Model

A fuzzy prototype is a fuzzy model of a cluster providing a description of the relevant properties of the data in the cluster. The fuzzy model can be a fuzzy decision tree, simple fuzzy logic rules, etc. Therefore, the fuzzy prototype model is the collection of fuzzy prototypes formed from the data set.

Semantic Coordinate and Semantic Distance

If the semantic distance between two semantic coordinates of objects is smaller than a threshold SD < ε, then we consider that these two objects have the semantic identity (SD = 0).

Linking Objects For The Fuzzy Prototype Model

Linking Objects For Semantic Distance

Papers Related with Machine Learning Toolkit

J. F. Baldwin, Dong (Walter) Xie, “Simple Fuzzy Logic Rules based on Fuzzy Decision Tree for Classification and Prediction Problem”, Intelligent Information Processing II, Published by Springer, ISBN 0-387-23151-X (HC), Page 175, October, 2004.

J. F. Baldwin, Dong (Walter) Xie, “Fuzzy Association Rules discovered on Effective Reduced Database Algorithm”, Fuzz-IEEE 2005, Reno.

D. Xie and J. F. Baldwin, “Fuzzy prototype model and semantic distance”, Intelligent Systems, Special Issue, 2006.

D. Xie and J. F. Baldwin, “Intelligent fril/sql interrogator”, International Journal of Intelligent Systems, in review.

Contact Detail

Dong Xie (Walter) 谢冬

http://eis.bris.ac.uk/~enxdx or google search “Dong Xie”

[email protected] (English)[email protected] (中文 )

Professor Jim F. Baldwinhttp://www.enm.bris.ac.uk/ai/enjfb.html

Documents

Intelligent Fril/SQL Interrogator Dong (Walter) Xie Supervisor : J. F. Baldwin 2003 - 2004