1
Decision Tables and Business Rules
April 24, 2008
Decision Tables and
Business Rules
Prof. dr. Jan Vanthienen
Katholieke Universiteit Leuven (Belgium)
Leuven Institute for Research on Information Systems
jan.vanthienen@econ.kuleuven.be
Who?
Issues
Objectives
Examples
Decision
tables
Concept
Jan Vanthienen
K.U.Leuven, Belgium
Faculty of Business and Economics
Business Information Systems Group
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Research and teaching:
• Business rules, processes and information systems
• Verification & Validation
European Conference on V&V of KBS (EuroVaV)
• Business intelligence & Knowledge discovery
• Decision tables
Publications, research, expertise: see Google, etc.
Email: jan.vanthienen@econ.kuleuven.be
Conclusions
Decision Tables and Business Rules
2008
2
2
Decision Tables and Business Rules
Issues
Objectives
Examples
• Building customer-focus
• Flexibility
• Personalization
Decision
tables
Concept
Applications
Representation
need business rules and the ability to create and
manage business rules in a well organized setting
Kinds of tables
Consistency
by
construction
Construction
V&V
• by bringing business rules out of obscurity.
• by bringing business rules out of implementation
• by bringing business rules out of code
• and into the business management side.
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
Introduction
• Objectives
• Decision Tables
• Message & Vision
• Examples
3
3
Decision Tables and Business Rules
Objectives
Issues
Objectives
Examples
Decision
tables
Concept
• The Vision: policies and rules of the business are an important source
of change,
– dealing with constraints, guidelines, events, actions, contracts, derivations, policies
• but these elements are often not explicited, hidden in systems,
inflexible and difficult to change by business users
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
• Attention Points
– Facilitate specification of business rules by business users
– building high quality systems (using verification and validation of business rules)
– building flexible systems (using powerful representations)
– Maintain business rules by non-technical participants
– Change business rules dynamically
– Update business rules without changing the system
– Straightforward implementation of Business Rules
– Design based on explicit executable business rules
– run-time application of business rules (end-users maintaining the system)
– business rule harvesting (e.g. in the areas of insurance fraud, credit scoring).
2008
Decision Tables and Business Rules
5
Issues
Issues
The important issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
• Where do the rules come from?
– Modeling, specification, harvesting
• How do we make sure we have the right rules?
– Quality, verification, representation
• How do we get new (and the right) rules quickly?
– Maintenance by business experts
• How do we get them to work
immediately?
– Declarative
Structures
EU-Rent
• Rule management
– Tools
Conclusions
Decision Tables and Business Rules
Business
2008
IT
7
4
Decision Tables and Business Rules
Example: UServ Product Derby Case Study
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Auto Discounts (p. 6)
• If the car only has Driver airbags then lower the premium by
12 %
• If the car has Driver and Passenger airbags then lower the
premium by 15 %
• If the car has Driver, Passenger and Side airbags then lower
the premium by 18 %
• If the car’s potential theft rating is high and the car is
equipped with an alarm system, then lower the premium by
10 %
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
8
2008
A table of rules
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Car has Driver airbags
Y
Y
Y
-
Car has Passenger airbags
N
Y
Y
-
Car has Side airbags
N
-
Y
-
Car's potential theft rating
-
-
-
high
Car is equipped with alarm system
-
-
-
Y
Lower the premium by 12%
x
.
.
.
Lower the premium by 15 %
.
x
-
-
Lower the premium by 18%
.
.
x
.
Lower the premium by 10%
.
.
.
x
1
2
3
4
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Default rule
Decision Tables and Business Rules
2008
Exception
9
5
Decision Tables and Business Rules
Completeness by construction
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
-
Table
Structures
x
Structures
added
deleted
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
10
UServ Product Derby Case Study
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Rewriting the rules
Auto Discounts (p. 6)
• If the car only has Driver airbags then lower the premium by
12 %
• If the car only has Driver and Passenger airbags
or only has Driver and Side airbags
then lower the premium by 15 %
• If the car has Driver, Passenger and Side airbags then lower
the premium by 18 %
• If the car’s potential theft rating is high and the car is
equipped with an alarm system, then lower the premium by
10 %
Decision Tables and Business Rules
2008
11
6
Decision Tables and Business Rules
The total picture
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
12
2008
Decision Tables and Business Rules
The final tables
Issues
Objectives
Examples
Decision
tables
Concept
Applications
-
Representation
Kinds of tables
x
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
13
7
Decision Tables and Business Rules
Decision table: Concept
and Application Areas
•
•
•
•
•
Concept
Advantages and best practices
Consistency by construction
Application areas
Examples
Concept
Issues
Objectives
Examples
Decision
tables
Concept
C1. Wholesaler
C2. Quantity Ordered (Q)
Y
Q<10
N
10<=Q<15
Q>=15
-
C3. Travel Distance (D)
-
D<50
50<=D<100
D>=100
-
-
A1. Discount (%)
0
10
5
2
10
0
A2. Railway Transport
-
-
-
-
x
x
A3. Road Transport
x
x
x
x
-
-
A4. Bill Type
A
A
A
A
B
A
Conditions
Actions
Applications
Representation
Subjects
Entries
Kinds of tables
Rule 2
Rule 1
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
A decision table is a
table representing the
complete set of
mutually exclusive
conditional expressions
in a predefined area
Rule 3
Rule 4
Rule 5
Rule 6
two-dimensional grouping
of interrelated rules
...
- Completeness criterion
- Exclusivity criterion
per condition subject
Conclusions
per action subject
Decision Tables and Business Rules
2008
15
8
Decision Tables and Business Rules
Decision table Orientation
Issues
Objectives
Examples
Decision
tables
Vertical
(rules in columns)
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Horizontal
(rules in rows)
Table
Structures
Structures
EU-Rent
Conclusions
16
2008
Decision Tables and Business Rules
Some EU-Rent discount rules
Issues
Objectives
Examples
Decision
tables
Name
Car Groups
Durations
Discount
Business Rules
3-day Advance
All
All
10%
All rentals booked at least 3 days in advance
qualify for a 10% discount.
Summer Week
Mid-sized, Full Sized,
Luxury, Sport Utility,
Minivan
Weekly
€50.00
Weekly renters of a qualifying car receive a €50
discount.
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
17
9
Decision Tables and Business Rules
Consistency by construction
Issues
• Exclusivity
Objectives
column 4 subsumes column 1
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
• Completeness
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
18
Why use decision tables?
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
–
Powerful visualisation
Compact and structured presentation
– Preventing errors is easier
Avoid incompleteness and inconsistency
– Modular knowledge organisation
Group rules into single table
– Performance
Fast decision tree execution
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
21
10
Decision Tables and Business Rules
Multiple languages
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
Airbags chauffeur
Airbags passagier
Zijdelingse airbags
Verminder de premie met 12%
-
Verminder de premie met 15%
x
Verminder de premie met 18%
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
22
Example: ORDER PROCESSING
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
1. Discount
Only wholesalers receive a discount. The discount rates are 10%, 5% and 2% : 10% for
wholesalers with an order quantity of at least 15 pieces or who are situated within a
range of less than 50 miles of the company and order at least 10 pieces; a discount of
5% is allowed to those wholesalers that order at least 10, but less than 15 pieces and
whose distance to the company is 50 miles or more; finally, the discount rate comes to
2% for customers that live at least 100 miles from the company, and order at least 10 but
less than 15 pieces.
2. Means of Transportation
We deliver by rail if the order does not come from a wholesaler or if a wholesaler has
ordered at least 15 pieces. In all other cases, road transport is used
V&V
Table
Structures
Structures
3. Bill Type
The normal bill type is A. Exceptionally, a type B bill has to be made up. This is the
case when a wholesaler’s order quantity is at least 15 pieces.
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
23
11
Decision Tables and Business Rules
Checking Completeness, Consistency and Correctness
Issues
1. Wholesaler
Y
2. Quantity Ordered (Q)
Examples
3. Travel Distance (D)
-
D<50
50<=D<100
D>=100
-
-
1. Discount 0%
-
-
-
-
-
x
2. Discount 2 %
-
-
-
x
-
-
3. Discount 5 %
-
-
x
x
-
-
Decision
tables
Concept
Applications
Q<10
N
Objectives
10<=Q<15
Q>=15
-
Representation
4. Discount 10 %
-
x
-
-
x
-
Kinds of tables
5. Railway Transport
-
-
-
-
x
x
Consistency
by
construction
Construction
6. Road Transport
x
x
x
x
-
-
7. Bill Type A
x
x
x
x
-
x
8. Bill Type B
V&V
Table
Structures
CS
Structures
EU-Rent
-
-
-
-
x
-
1
2
3
4
5
6
• (C11,C21)
• (C11,C22)
• (C12,C21)
• (C12,C22)
• ...
• (x,x)
• (x,-)
• (-,x)
• (-,-)
• ...
AS
Conclusions
Decision Tables and Business Rules
2008
24
Updated Example: ORDER PROCESSING
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
1. Discount
Only wholesalers receive a discount. The discount rates are 10%, 5% and 2% : 10% for
wholesalers with an order quantity of at least 15 pieces or who are situated within a range
of less than 50 miles of the company and order at least 10 pieces; a discount of 5% is
allowed to those wholesalers that order at least 10, but less than 15 pieces and whose
distance to the company is 50 miles or more, but less than 100 miles; finally, the discount
rate comes to 2% for customers that live at least 100 miles from the company, and order
at least 10 but less than 15 pieces. No discount is given if the order quantity is less than
10 pieces.
2. Means of Transportation
We deliver by rail if the order does not come from a wholesaler or if a wholesaler has
ordered at least 15 pieces. In all other cases, road transport is used
3. Bill Type
The normal bill type is A. Exceptionally, a type B bill has to be made up. This is the case
when a wholesaler’s order quantity is at least 15 pieces.
Conclusions
Decision Tables and Business Rules
2008
25
12
Decision Tables and Business Rules
Applying procedures
Issues
(CASE: wholesaler; quantity ordered = 12; distance = 120)
Objectives
Examples
Decision
tables
Concept
•
•
Applications
Representation
Text = action oriented (on average: 7,66 questions)
+ verbose
Decision table = condition oriented (average: 2,33 questions)
+ correct decision path
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
•
Decision making is faster and correct
- less questions (only relevant questons)
- no interpretation, ambiguity
(‘in other cases’, ‘normally’, ‘unless stated elsewhere’)
Structures
EU-Rent
SPEED
CORRECTNESS
Conclusions
26
2008
Decision Tables and Business Rules
Application areas
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
• Regulations, legislation, compliance …
• Business rules, corporate policy, ...
accept/refuse orders
discounts
…
• Business Process Management
• Knowledge and Expertise
• Classification knowledge
types of customers
risk categories
…
knowledge engineering
• Systems analysis
and implementation
conditional logic experiments
knowledge validation
transformations
automation of construction
Table
Structures
application field
enlargement
advanced preprocessors
Structures
algorithms
EU-Rent
initial preprocessors
initial developments
Conclusions
1960
Decision Tables and Business Rules
1970
1980
2008
1990
2000
27
13
Decision Tables and Business Rules
Kim Clijsters' Tennis Ranking
Issues
Objectives
“Clijsters becomes the world's number one if she reaches the final, OR If Davenport
doesn't reach the final, OR Mauresmo doesn't win the tournament.
Examples
Decision
tables
Lindsay Davenport stays number one if she wins the tournament AND Clijsters
doesn't reach the final, OR she looses the final (against another player than
Mauresmo) AND Clijsters looses in the semi-finals.
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Amélie Mauresmo becomes number one if she wins the tournament and Clijsters
looses in the quarter-finals.”
(translated from www.sporza.be, 2006 ...)
1. Clijsters
2. Davenport
3. Mauresmo
1. Cijsters number 1
2. Davenport number 1
3. Mauresmo number 1
1. Clijsters
2. Davenport
3. Mauresmo
1. Cijsters number 1
2. Davenport number 1
3. Mauresmo number 1
Decision Tables and Business Rules
goes out before semi-final
goes out before final
runner-up
does not win tourn.
wins tourn.
does not win tourn.
wins tourn.
x
.
.
1
goes out before final
x
.
.
6
x
.
x
2
x
.
.
3
looses semi-final
runner-up or wins tourn.
does not win tourn.
wins tourn.
x
x
.
7
wins tourn.
-
.
.
x
4
x
x
.
5
runner-up or wins
-
.
.
.
8
x
.
.
9
2008
28
Discovering credit scoring rules
Issues
Objectives
Examples
Decision
tables
Concept
Applications
EXAMPLE
Major Benelux financial institution:
• A real life credit-risk evaluation data set is analyzed using
neural network rule extraction techniques. The resulting
business rules are represented in a format that is easily
comprehensible and verifiable by the credit-risk manager.
Representation
Kinds of tables
Consistency
by
construction
• Many classification/data mining techniques have been
suggested to develop credit scoring systems
– Examples are: logistic regression, discriminant analysis, decision trees and rules, knearest neighbour, genetic algorithms, neural networks, support vector machines, …
Construction
V&V
Table
Structures
Structures
EU-Rent
• Most studies primarily focus at developing credit scoring
models with a high classification accuracy
– But: more focus should lie on developing explanatory systems instead of merely
adopting black blox, mathematically complex models!
– E.g., open the neural network black box using rule extraction or tree extraction
techniques
Conclusions
Decision Tables and Business Rules
2008
29
14
Decision Tables and Business Rules
Comprehensibile credit scoring systems
if Term >12 months and Purpose=cash provisioning and Savings account<=12.40
Euro and Years client<=3 then Applicant=bad
Issues
Term > 12 Months
-0.202
Objectives
Purpose=cash provisioning
-0.287
Examples
Purpose=second hand car
Savings account > 12.40 Euro
Decision
tables
Representation
0.278
-0.081
-0.162
Concept
Years client > 3 years
if Purpose=cash provisioning and Income>719 Euro and Owns property=No and
Savings account <= 12.40 Euro and Years client<=3 then Applicant=bad
-0.102
Income > 719 Euro
Property=No
Applications
if Term >12 months and Purpose=cash provisioning and Owns property=No and
Savings account <=12.40 Euro then Applicant=bad
Applicant=good
0.137
0.457
if Purpose=second hand car and Income>719 Euro and Owns property=No and
Savings account <= 12.40 Euro and Years client<=3 then Applicant=bad
-0.453
0.611
0.380
Applicant=bad
if Savings account <=12.40 Euro and Economical sector=Sector C then
Applicant=bad
-0.289
Default class: Applicant=good
Economical sector=sector C
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
BAESENS B., SETIONO R., MUES C., VANTHIENEN J., Using Neural Network Rule Extraction and Decision Tables
for Credit-Risk Evaluation, Management Science, Vol. 49, No. 3, March 2003, pp. 312-329.
http://pubsonline.informs.org/main/pdfstore/cecbaae209_abstract.pdf
Decision Tables and Business Rules
2008
Business Rules and
Smart Business Processes
• Adaptability
• Business Processes
• Busines Rules
''We are what we repeatedly do. Excellence,
then, is not an act, but a habit.'‘
-- Aristotle
31
15
Decision Tables and Business Rules
What can change?
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
•
•
•
•
•
•
•
•
•
•
The rules and policies governing business process
Decision details of business processes
Service contracts and protocols
Constraints on business processes
The business processes itself
Calculation procedures
Constraints and definitions
Decision paths and elements
Assumptions and preconditions
Presentation, implementation, …
So how do we obtain this flexibility in processes, services,
components, applications
- aligned with the business
- in a stable architecture
Conclusions
Decision Tables and Business Rules
33
2008
The Business Rules Approach
Issues
Objectives
Examples
Decision
tables
Concept
Applications
• Define business rules explicitly
– Rules as data
– Rules separated from processes
• Organised and stored separately from rest of application
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
• Make the business the owner
– Provide owner access for definition and modification
• Use the business rules directly:
– In the organisation,
as guidance for people
– In software products
that automate them
EU-Rent
Conclusions
Decision Tables and Business Rules
A business rule is:
–
–
–
–
–
–
2008
Declarative (i.e. non-procedural);
Atomic (indivisible yet inclusive);
Expressed in business language;
Distinct, independent constructs;
Business, not technology, oriented;
Business, not technology, owned;
34
16
Decision Tables and Business Rules
The Business Rules Manifesto
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Article 1.
Primary Requirements, Not Secondary
1.1. Rules are a first-class citizen of the requirements world.
1.2. Rules are essential for, and a discrete part of, business models and technology models.
Article 2.
Separate From Processes, Not Contained In Them
2.3. Rules apply across processes and procedures. There should be one cohesive body of rules,
enforced consistently across all relevant areas of business activity.
Article 3.
Deliberate Knowledge, Not A By-Product
3.1. Rules build on facts, and facts build on concepts as expressed by terms.
3.2. Terms express business concepts; facts make assertions about these concepts; rules
constrain and support these facts.
Article 4.
Declarative, Not Procedural
4.5. A rule is distinct from any enforcement defined for it. A rule and its enforcement are separate
concerns.
Article 5.
Article 6.
Article 7.
Article 8.
Article 9.
Well-Formed Expression, Not Ad Hoc
Rule-Based Architecture, Not Indirect Implementation
Rule-Guided Processes, Not Exception-Based Programming
For the Sake of the Business, Not Technology
Of, By, and For Business People, Not IT People
9.2. Business people should have tools available to help them formulate, validate, and manage
rules.
Article 10. Managing Business Logic, Not Hardware/Software Platforms
(Version 2.0, November 1, 2003. Business Rules Group)
Conclusions
Decision Tables and Business Rules
2008
35
2008
36
Where are the rules?
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
(BPMN 1.0: OMG Final Adopted Specification, 2006)
• Decision rules
• Calculation rules
V&V
Table
Structures
Structures
EU-Rent
Conclusions
but also:
• Timing rules
• Workflow rules
• Access rules
• Exception rules
• …
Decision Tables and Business Rules
17
Decision Tables and Business Rules
BPM and Rules
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Graphical maps can get
unwieldy
Consistency
by
construction
Scripts are too
programmer-ese
Construction
V&V
Optimal:
1. BPM controls process
2. Delegate complex
decisions to rules and
tables
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
37
Separating process logic and rule logic
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
38
18
Decision Tables and Business Rules
Business Rules in Enterprise Model
Issues
defines and
constrains
terms of
Objectives
Examples
Decision
tables
Concept
Business
Vocabulary
Model
Business
Rule
Model
guides,
constrains,
defines
transitions of
Business
Process
Model
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
• Rules guide a process (decision points, computations)
• Rules constrain processes (order, time, resources)
– Model the rules first, then derive the process
• Externalizing rules produces thin (agile) processes
• Rules ensure compliance
• Rules ensure alignment
Conclusions
Decision Tables and Business Rules
2008
39
Example: order-to-cash process
Issues
Objectives
What is wrong with this process model?
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
“Customers under the age of 18 cannot order medication X” (integrity constraint)
“Loyal customers receive a 10% discount” (derivation rule)
“When the customer does not pay within 30 days, a payment violation notice is
sent to the financial department” (reaction rule)
Decision Tables and Business Rules
2008
41
19
Decision Tables and Business Rules
Permissions and obligations
Issues
Objectives
Examples
E.g. “We can only ship after we have accepted an official order”
e.g. order-to-cash business process
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
The Permissions and
obligations of the actors in a
business process constrain and
determine the sequence flow.
Structures
EU-Rent
Conclusions
Goedertier, S., Vanthienen, J.: Rule-based business process
modeling and execution, in Proceedings of the IEEE EDOC
Workshop on Vocabularies, Ontologies and Rules for The
Enterprise (VORTE 2005). CTIT Workshop Proceeding
Series (ISSN 0929-0672), Enschede, 2005.
42
2008
Decision Tables and Business Rules
Process-level enforcement
Service-Oriented Architecture
Issues
Objectives
Examples
Decision
tables
Concept
Applications
business rule
and process
layer
rules, rules
Call For
Proposals
processes
rules
Representation
Kinds of tables
Consistency
by
construction
services and
components
layer
shipment
order
e-payment
Construction
V&V
Table
Structures
Structures
EU-Rent
application
layer
MCS
LDAP
DWH
ERP
Conclusions
Decision Tables and Business Rules
2008
43
20
Decision Tables and Business Rules
Two sides of the same coin
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
• Business Process Management
• Business Rules Management
– BP Modelling
– BR Modelling
Analysis
Quality/Maintenance
Improvement
Analysis
Quality/Maintenance
Improvement
– BP Enactment
– BAM - Business Activity
Monitoring
– Execution
– BP Maturity
Decision Tables and Business Rules
– BR Enactment
– RAM - Rule Activity
Monitoring
– Execution
– BR Maturity
2008
44
Which comes first?
Issues
Two architectural styles:
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
• Process first
–Execution scenarios are explicit,
design choices are implicit.
–Excellent for stable processes,
highly standardized
• Rules first
–Rules, choices and goals are explicit,
execution scenario is derived.
–Compliance by design.
–Excellent for volatile processes,
many exceptions, agility
EU-Rent
Conclusions
• And, of course, combinations of both
Decision Tables and Business Rules
2008
45
21
Decision Tables and Business Rules
Representing rule sets
• Alternative representations: trees,
rules, tables, graphs and text
• Different representations for
different purposes
• Kinds of decision tables
• Contraction, optimization
Decision tables and rules
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
if (credit limit = 'Ok') and (customer = 'Good') and (Stock Sufficient)
Sufficient) then Execute Order
if (customer = 'Not Good') and (credit limit = 'Not Ok') then Refuse
Refuse Order
if (credit limit = 'Not Ok') and (customer = 'Good') and not(Stock Sufficient)
then Put On Waiting List
if (Customer = 'Good') and (Stock Sufficient) then Execute Order
if (Customer = 'Good') and not(Stock Sufficient) and (credit limit
limit = 'Ok') then Put On Waiting List
if (credit limit = 'Ok') and (Customer = 'Not Good') and not(Stock
not(Stock Sufficient) then Put On Waiting List
Conclusions
if (credit limit = 'Ok') and (Stock Sufficient) and (Customer = 'Not Good') then Execute Order
Decision Tables and Business Rules
2008
47
22
Decision Tables and Business Rules
50 ways to represent rule sets
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Assign 22 days if always
5 extra days if Age<18 or Age>=60
5 extra days if Service>=30
2 extra days if Age is between 45-<60 and Service <30
2 extra days if Age is between 18-<45 and Service is between 15-<30
3 extra days if Service>=30 and Age is between 18-<60
3 extra days if Age = >=60
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
(Clive Spenser)
48
2008
Decision Tables and Business Rules
Trees, Graphs, Rules, Tables
1. Customer is not a wholesaler
No discount is allowed, we deliver by rail
and the bill type is A.
2. Customer is a wholesaler
2.1. Quantity ordered is less than 10 pieces
No discount is allowed, road transport is used
and the bill type is A.
2.2. Quantity ordered is at least 10, but less than 15 pieces
2.2.1. Distance to the company is less than 50 miles
A discount of 10% is allowed, road transport is used
and the bill type is A.
2.2.2. Distance to the company is 50 miles or more, but less than 100 miles
A discount of 5% is allowed, road transport is used
and the bill type is A.
2.2.3. Distance to the company is more than 100 miles
A discount of 2% is allowed, road transport is used
and the bill type is A.
2.3. Quantity ordered is at least 15 pieces
A discount of 10% is allowed, we deliver by rail
and the bill type is B.
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
ANIMALS
Skin ?
Construction
V&V
feathers
hair
Birds
Table
Structures
Conclusions
(Firefly)
Decision Tables and Business Rules
Flies ?
yes
no
yes
Lays Eggs?
Mammals
Lays Eggs?
Structures
EU-Rent
...
Flies ?
no
Gives Milk?
yes
no
yes
no
Birds
Mammals
Birds
Gives Milk?
2008
yes
Mammals
no
/
49
23
Decision Tables and Business Rules
3 aspects of a decision situation
Issues
Objectives
Examples
Decision
tables
Specification
(description, modelling)
Business Rules
Tables
Trees
Concept
Applications
Representation
Verification
& Validation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Execution
(implementation)
Graphs
Rules
Conclusions
Decision Tables and Business Rules
2008
50
Example: premiums
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
Suppose we have to give a premium to an employee, depending
on age and performance evaluation. There are two kinds of
premium: the normal premium and the super premium.
Here are the specification rules:
– The Super Premium is only given if the evaluation is Good;
– The Normal Premium is given if (Age>=45 or Evaluation is Moderate) except in the
cases of rule 1;
– The Normal Premium is however given if (Age>=45 and Evaluation is Good).
Verification:
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
51
24
Decision Tables and Business Rules
Implementation
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
• <?xml version="1.0" ?>
• <Rulebase>
•
• <!-- rule 1 -->
• <Implies>
•
<head>
•
<Atom>
•
<Rel>equals</Rel>
•
<Var>SuperPremium</Var>
•
<Ind>execute</Ind>
•
</Atom>
•
</head>
•
<body>
•
<Atom>
•
<Rel>equals</Rel>
•
<Var>Person.Evaluation</Var>
•
<Ind>Good</Ind>
•
</Atom>
•
</body>
• </Implies>
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
• <!-- rule 2 -->
• <Implies>
•
<head>
•
<Atom>
•
<Rel>equals</Rel>
•
<Var>NormalPremium</Var>
•
<Ind>execute</Ind>
•
</Atom>
•
</head>
•
<body>
•
<Or>
•
<And>
•
<Atom>
•
<Rel>equals</Rel>
•
<Var>Person.Age&lt;45</Var>
•
<Ind>TRUE</Ind>
•
</Atom>
•
<Atom>
•
<Rel>equals</Rel>
•
<Var>Person.Evaluation</Var>
•
<Ind>Moderate</Ind>
•
</Atom>
•
</And>
•
<Atom>
•
<Rel>equals</Rel>
•
<Var>Person.Age&lt;45</Var>
•
<Ind>FALSE</Ind>
•
</Atom>
•
</Or>
•
</body>
• </Implies>
• </Rulebase>
2008
52
Text Rewriting
Issues
Objectives
Examples
Decision
tables
• Discount 0% IF Wholesaler = N or Quantity Ordered (Q) = Q<10
• Discount 2 % IF Wholesaler = Y and Quantity Ordered (Q) = 10<=Q<15
and Travel Distance (D) = D>=100
• Discount 5 % IF Wholesaler = Y and Quantity Ordered (Q) = 10<=Q<15
and Travel Distance (D) = 50<=D<100
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
• Discount 10 % IF Wholesaler = Y and ((Quantity Ordered (Q) = 10<=Q<15
and Travel Distance (D) = D<50)
or Quantity Ordered (Q) = Q>=15)
• Railway Transport IF Wholesaler = N or Quantity Ordered (Q) = Q>=15
• Road Transport IF Wholesaler = Y and (Quantity Ordered (Q) = Q<10
or Quantity Ordered (Q) = 10<=Q<15)
• Bill Type A IF Wholesaler = N or Quantity Ordered (Q) = Q<10
or Quantity Ordered (Q) = 10<=Q<15
• Bill Type B IF Wholesaler = Y and Quantity Ordered (Q) = Q>=15
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
53
25
Decision Tables and Business Rules
Formal definition: Conditions
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
e.g. C1: condition subject:
condition domain:
condition states:
Quantity (=x)
{0,1,2,...,n}
{x<10, 10<=x<20, 20<=x}
(Integer)
C2: condition subject :
condition domain :
condition states :
Color
{white, yellow, blue}
(Enumeration)
{color=white, color=yellow or blue}
C3: condition subject :
condition domain :
condition states :
Leap Year
{True, False}
{Yes, No}
(Boolean)
V&V
Table
Structures
The 'condition space' is the set of all possible condition combinations.
Structures
EU-Rent
Conclusions
2008
Decision Tables and Business Rules
54
Condition and Action Space
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
Condition list:
C=
{
(Quantity, {x<10, 10<= x<20, 20<= x}),
(Color, {white, (yellow or blue)}),
(Schrikkeljaar, {Yes, No})
}
Condition space:
CS= {
(x<10, white, Yes), (x<10, white, No),
(x<10, (yellow or blue),Yes), (x<10, (yellow or blue),No),
(10<= x<20, white, Yes), ... }
Action list:
A=
{
give allowance, road transport, … }
V&V
Table
Structures
Action space:
AS= {
(x, x), (x, -), (-, x), …
}
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
55
26
Decision Tables and Business Rules
The decision table as a relation
Issues
• The decision table relation DT
Objectives
Examples
Decision
tables
Concept
Applications
– is a function: every condition combination has only one image.
Multiple condition combinations may have the same image.
(consistency)
– every condition combination has at least one image
(completeness)
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
CS
AS
• (C11,C21)
• (C11,C22)
• (C12,C21)
• (C12,C22)
• ...
• (x,x)
• (x,-)
• (-,x)
• (-,-)
• ...
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
56
10 Commandments on decision table usage
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Content
• Multi-valued states (extended entry conditions)
• Exclusivity and completeness of the states (domain
partitioning)
• Exclusivity and completeness of the columns (single hit
tables)
• Predefined actions (refined action entries)
Form
• Optimization (group oriented contraction and row order
optimization)
• Tree structures (top-down readability)
• Concise representation (block-oriented notation)
• Indication of impossibilities (contracted impossibilities)
Purpose
• Selection structure (no initialization or repeat actions)
• Subtables (closed subtables)
Decision Tables and Business Rules
2008
57
27
Decision Tables and Business Rules
Kinds of tables
Issues
Objectives
Examples
Decision
tables
Concept
MH
Multiple Hit
MH/A
All Hit
table with non-exclusive columns
rule table or decision grid chart
Applications
Representation
Kinds of tables
Consistency
by
construction
MH/F
First Hit
SH
Single Hit
"classic" multiple hit table
decision table
Construction
V&V
Table
Structures
Structures
SH/X
Expanded
the table representation of all single
decision columns
SH/C
Contracted
the compact table representation of all
decision columns for a given condition
order
SH/O
Optimized
the compact table representation of all
decision columns for the optimal
condition order (cf. infra)
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
58
Example
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
• The number of holidays depends on age and years of service.
Every employee receives at least 22 days.
Additional days are provided according to the following criteria:
• Only employees younger than 18 or at least 60 years, or
employees with at least 30 years of service will receive 5 extra
days.
• If the employee has at least 15 but less than 30 years of service, 2
extra days are given. These 2 days are also provided for
employees of age 45 or more. The 2 extra days can not be
combined with the 5 extra days.
Construction
V&V
Table
Structures
• Employees with at least 30 years of service and also employees of
age 60 or more, receive 3 extra days, on top of possible additional
days already supplied.
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
59
28
Decision Tables and Business Rules
multiple hit versus single hit
Issues
Objectives
multiple hit, all hits
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
multiple hit, first hit
Consistency
by
construction
Construction
V&V
Table
Structures
single hit
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
Constructing decision
tables
• Construction steps
• Construction: example
• Completeness, consistency and
correctness
• Decision table layout
60
29
Decision Tables and Business Rules
Decision Table Construction
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
1. Define the conditions, the condition states and the actions.
2. Describe the problem using logical expressions, where
actions and (combinations of) condition states are related in
logical expressions.
3. Construct the empty table: list conditions and actions, fill out
the condition entries of the table (the lower conditions will
vary first).
4. Fill out the action entries (column by column or action by
action), based on the logical expressions.
5. Check table for completeness, correctness and consistency
6. Simplify the decision table
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
66
Example
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
• The number of holidays depends on age and years of service.
Every employee receives at least 22 days.
Additional days are provided according to the following criteria:
• Only employees younger than 18 or at least 60 years, or
employees with at least 30 years of service will receive 5 extra
days.
• If the employee has at least 15 but less than 30 years of service, 2
extra days are given. These 2 days are also provided for
employees of age 45 or more. The 2 extra days can not be
combined with the 5 extra days.
Construction
V&V
Table
Structures
• Employees with at least 30 years of service and also employees of
age 60 or more, receive 3 extra days, on top of possible additional
days already supplied.
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
67
30
Decision Tables and Business Rules
Condition states and actions
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Condition states
Actions
Age
Years of service
Every employee
age < 18
age >= 60 jaar
>= 30 years of service
15-<30 years of service
age >= 45 jaar
Number of holidays
At least 22 days
Extra days
5 extra days
2 extra days (can not be combined with 5 extra days)
3 extra days (on top)
Kinds of tables
Consistency
by
construction
Construction
V&V
Condition states
age < 18
age >= 60
>= 30 years of service
15-<30 years of service
age >= 45
Actions
Assign 22 days
5 extra days
2 extra days
3 extra days
Table
Structures
Structures
EU-Rent
Age
<18
18-<45
45-<60
Service
<15
15-<30
>=30
>=60
Conclusions
69
2008
Decision Tables and Business Rules
Decision Table Construction
Issues
Objectives
1.
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
2.
3.
Define the conditions, the condition states and the actions.
Age
<18
18-<45
45-<60
Service
<15
15-<30
>=30
>=60
Describe the problem using logical expressions, where actions and (combinations of) condition
states are related in logical expressions.
Construct the empty table: list conditions and actions, fill out the condition entries of the table (the
lower conditions will vary first).
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
4.
5.
6.
Fill out the action entries (column by column or action by action), based on the logical expressions.
Check table for completeness, correctness and consistency
Simplify the decision table
Conclusions
Decision Tables and Business Rules
2008
70
31
Decision Tables and Business Rules
Decision rules
Issues
Objectives
Examples
Decision
tables
Concept
Every employee receives at least 22 days.
Action 1 (Assign 22 days) must be executed for every possible combination of condition states.
Rule 1:
assign 22 days if always
Only employees younger than 18 or at least 60 years, or employees with at least 30
years of service will receive 5 extra days.
Rule 2:
5 extra days if (and only if) age < 18 or age >= 60 or service >= 30
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
If the employee has at least 15 but less than 30 years of service, 2 extra days are
given. These 2 days are also provided for employees of age 45 or more. The 2 extra
days can not be combined with the 5 extra days.
Rule 3:
Employees with at least 30 years of service and also employees of age 60 or more,
receive 3 extra days, on top of possible additional days already supplied.
Rule 4:
Table
Structures
Structures
EU-Rent
2 extra days if (45 <= age < 60 or age >= 60 or 15 <= service < 30) minus rule 2
3 extra days if age >= 60 or service >= 30
From common sense knowledge, it is clear that an employee younger than 18 years
can not have 15 or more years of service. The impossible condition combinations
can be discarded from the table by adding the rule:
Rule 5:
impossible if age < 18 and (15 <= service < 30 or service >= 30)
Conclusions
Decision Tables and Business Rules
2008
74
Fill out action entries
Issues
Expanded decision table
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Expanded decision table (without impossible combinations)
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
78
32
Decision Tables and Business Rules
4. Check for Completeness, Consistency & Correctness
Issues
1.
Examine the empty columns. These columns should be examined
one by one to verify if it really was the intention to have no actions.
2.
Examine the unreferenced actions (or conditions).
3.
Examine the completeness of actions and columns. Some actions
or groups of actions should have at least one occurrence. This is
usually the case if the actions are the representation of an
"extended-entry" action (exhaustivity requirement).
4.
Examine the table for contradictions between actions.
Some actions or action groups may exclude each other and
therefore cause contradictions if they occur in the same column
(exclusivity requirement).
5.
Examine the table for correctness. Here we should not only check
if the different columns correspond with the described
specifications, but also if this specification corresponds with the
desired reality.
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
79
Simplify the decision table
Issues
Objectives
Examples
Decision
tables
1. Contraction of the table. Adjacent columns with the same action-configuration are
contracted into combined columns.
2. An optimal condition order might be computed to minimize the number of columns.
3. Decide upon a suitable layout. To achieve this, several actions can be combined
into extended entry actions, as long as this does not cause any contradictions.
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
2008
82
33
Decision Tables and Business Rules
Optimization Features
Issues
Expanded table
Objectives
Examples
Decision
tables
Concept
• Table contraction
– merge adjacent column groups with identical action parts
Applications
Representation
Kinds of tables
Contracted table
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
• Row order optimization
– less columns by changing order of condition rows
For a table with n conditions,
this implies a choice between
n! alternative condition
orders, some of which might
be infeasible because of
precedence constraints
Reordered table
2008
Decision Tables and Business Rules
83
Optimization features
Issues
Objectives
Examples
Decision
tables
Concept
• Export to minimal rules
improved rule notation
– Discount 2 % IF Wholesaler = Y and Quantity Ordered (Q) = 10<=Q<15
and Travel Distance (D) = D>=100
– Railway Transport IF Wholesaler = N or Quantity Ordered (Q) = Q>=15
– Bill Type B IF Wholesaler = Y and Quantity Ordered (Q) = Q>=15
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
• Optimal code generation
Execution time optimization: this determines the optimal test sequences
(execution tree)
– In the resulting execution tree, conditions are not always tested in the same order anymore.
– taking into account condition test times and column frequencies (if available).
– If condition test times or column frequencies are not supplied, they are assumed equal for all
conditions or columns respectively and the average number of tests is minimized.
This implies choosing between f(n) decision trees:
f(n)
Decision Tables and Business Rules
=
n.[f(n-1)]2, with f(1) = 1
2008
84
34
Decision Tables and Business Rules
From the Business Rules Manifesto
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
2008
Decision Tables and Business Rules
Verification & Validation
•
•
•
•
Redundancy
Ambivalence (inconsistency)
Deficiency and completeness
Validation of correctness
85
35
Decision Tables and Business Rules
A few words from V&V Research
Issues
Objectives
Examples
Anomalies
Special Cases
Decision
tables
Unfirable rule
Unsatisfiable condition
Subsumed rule
Duplicate rules
Concept
Applications
Representation
Kinds of tables
Redundancy
Consistency
by
construction
Ambivalence
Construction
V&V
Circularity
Table
Structures
Deficiency
Unusable consequent
Contradictory rules
Unused input
Structures
EU-Rent
Preece’s anomaly classification
Conclusions
Decision Tables and Business Rules
2008
90
V&V experiences with decision tables
Issues
Objectives
Examples
Decision
tables
Concept
Applications
The cleanroom approach
• Redundancy
Avoided by table mechanism
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
• Inconsistency or contradictions
Avoided by modeling tables
• Incompleteness
Avoided by automatic generation of combinations
• Correctness
Easy to visualize the decision logic
Conclusions
Decision Tables and Business Rules
2008
91
36
Decision Tables and Business Rules
How decision tables avoid redundancy
Issues
Objectives
Examples
Decision
tables
The main problem with redundancy is maintenance and the risk of creating
inconsistencies when changing the specifications.
Some common forms of redundancy:
• Subsumption: rules with the same conclusions but with one of them containing
additional premises (and therefore being less general)..
– A credit check must be performed for a customer if the order total is more than $1000
– A credit check must be performed for a customer if the order total is more than $1000
and a waiver has not been authorized
Subsumption cannot occur in the decision table, because columns do not overlap
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
• Redundant premises: (partly) complementary rules with equal conclusions,
which can be combined.
– A credit check must be performed for a customer if the order total is more than $1000
and a waiver has not been authorized
– A credit check must be performed for a customer if the order total is more than $1000
and a waiver has been authorized
In a contracted decision table, complementary rules are combined, leading to the
detection of irrelevant or partly irrelevant conditions. The number of distinct columns is
thereby minimized.
Construction
V&V
Table
Structures
Structures
EU-Rent
• Redundant rules: rules with the same premises and (partly) equal conclusions.
Because in the decision table every possible case is included in only one column
(exclusivity), redundant rules will not occur.
Conclusions
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2008
Redundancy
Issues
• Subsumed Column
(special case: duplicate column)
Objectives
exclusivity criterion
Examples
Decision
tables
Prevented at tableconstruction time !
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
!
- incremental verification
- integration into modelling environment
Remark: traditional rule subsumption checking requires
pairwise rule comparison
subsumption
if (credit limit = 'Ok') and (customer = 'Good') and (Stock Sufficient) then Execute Order
Table
Structures
if (customer = 'Not Good') and (credit limit = 'Not Ok') then Refuse Order
if not(Stock Sufficient) and (credit limit = 'Not Ok') and (customer = 'Good') then Put On Waiting List
Structures
EU-Rent
if (Customer = 'Good') and (Stock Sufficient) then Execute Order
if (Customer = 'Good') and not(Stock Sufficient) and (credit limit = 'Ok') then Put On Waiting List
Conclusions
etc..
if (credit limit = 'Ok') and (Customer = 'Not Good') and not(Stock Sufficient) then Put On Waiting List
if (credit limit = 'Ok') and (Stock Sufficient) and (Customer = 'Not Good') then Execute Order
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Decision Tables and Business Rules
How decision tables avoid inconsistency
Issues
Objectives
Examples
Decision
tables
Concept
Inconsistency occurs when rules with the same premises, but with different
conclusions, exist. In a single-hit decision table (the only type we will use),
columns are non-overlapping and therefore conflicting columns will not occur.
This is one of the major advantages of decision tables: each condition
combination occurs only once and refers to exactly one configuration of
conclusions.
Some common forms of inconsistency:
Applications
Representation
Kinds of tables
Consistency
by
construction
• Conflict: rules with the same premises (or containing overlapping combinations),
but leading to contradictory conclusions.
– A credit check must be performed for a customer if the order total is more than $1000
– A credit check must not be performed for a customer if the order total is more than
$1000 and a waiver has been authorized
The single-hit table format avoids such conflicts.
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
• Invalid attribute values: a rule containing a nonexistent value of an attribute (e.g.
because of a typing error).
– Once the domain of conditions is well defined, invalid values will not occur, as table
entries can be generated automatically.
• Cyclical rules: a set of rules where a conclusion occurs somewhere as one of the
premises.
– In a decision table context, conclusions occurring as premises lead to separate tables.
The forward character of the decision table structure eliminates the problem of cyclical
references.
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How decision tables avoid incompleteness
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
No current system is able to incorporate all possible knowledge, but within the
specific problem area, the following omissions often occur:
• Unused attribute values or combinations: when attribute values (or
combinations) never occur as premises, a number of rules may be missing.
Detecting the completeness of all combinations of attribute values is not always
simple.
– A credit check must be performed for a customer if the order total is more than $1000
– A credit check must not be performed for a customer if the order total is less than $500
What if the order total is $800?
The nature of the decision table easily allows to check for completeness: the number of
simple columns should equal the product of the number of states for every condition.
This guaranty of completeness of condition combinations is one of the main
advantages of decision tables.
• Unreachable conclusions: conclusions which are never deduced and cannot be
asked.
The format of the decision table easily shows unreachable conclusions.
• Missing knowledge: the absence of some essential elements from the problem
situation. Inconsistency often is a symptom of incompleteness because of
missing premises.
Conclusions
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38
Decision Tables and Business Rules
Structures of decision
tables
•
•
•
•
Modeling structures
Modularization
Table structures
Verification and validation of table
hierarchies
• Table structures: example
Modeling Structures
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
•Structures of tables
–Condition subtables :
subtables that determine the state
of a condition (e.g. when do you
consider someone a good
customer?)
–Action subtables :
subtables that further elaborate on
what additional knowledge holds for
certain cases (e.g. what discount to
give if an order is accepted)
EU-Rent
Conclusions
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39
Decision Tables and Business Rules
Table Structures: Example
Issues
"Customer" subtable
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
subtable further specifies what is understood
by the notion of a "good" customer
main table
Consistency
by
construction
Construction
V&V
"Execute Order" subtable is only applicable
in cases where Credit Limit = Ok
and/or Customer = Good
Table
Structures
"Execute Order" subtable
Structures
EU-Rent
Conclusions
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2008
Verification and Validation (V&V)
Issues
Objectives
Examples
• Intra-tabular verification : verify each table
– single-hit decision tables
Decision
tables
intraintra-tabular
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
• Inter-tabular verification :
verify different (sub)tables
with respect to each other
interinter-tabular
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
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40
Decision Tables and Business Rules
Unfirable Column: Example
Issues
main table
Objectives
Examples
Decision
tables
Concept
Applications
"Execute Order" subtable
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
unfirable
Table
Structures
Structures
EU-Rent
Conclusions
“Credit Limit” and “Customer” are referred to in
condition stub of main table, as well as subtable
Decision Tables and Business Rules
2008
Example: EU-Rent
discount rules
• Full table
• Modularization
• Analysis
107
41
Decision Tables and Business Rules
Example: EU-Rent
Issues
Objectives
EU-Rent's current discounted promotion programmes are
Examples
Decision
tables
Concept
Car Groups
Durations
Discount
Business Rules
3-day Advance
All
All
10%
All rentals booked at least 3 days in
advance qualify for a 10% discount.
Summer Week
Mid-sized, Full
Sized, Luxury,
Sport Utility,
Minivan
Weekly
€50.00
Weekly renters of a qualifying car
receive a €50 discount.
New Loyalty
Member
Compact, Midsized, Full Sized
Daily, Weekly,
Monthly
2 car group
upgrades
New loyalty club members are eligible
for a 2 level upgrade, subject to
availability on their first rental after
joining the programme.
Name
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
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EU-Rent: Contracted decision table
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
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42
Decision Tables and Business Rules
Dependency Graph
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
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2008
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2008
114
EU-Rent decomposed
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
Decision Tables and Business Rules
43
Decision Tables and Business Rules
Experiences
•
•
•
•
•
Pension Fund
Study allowances
Hiring programs
Medical procedures
…
PROcedural LOGic Analyzer
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
A tool environment that uses decision tables for
knowledge modelling, validation, optimization and
implementation
Integrated V&V Features
Refined specification language
Actions [generally] if condition combinations
(Not) action definitely if condition combinations
Action only possible if condition combinations
Action definitely if and only if condition combination
Knowledge Optimization
Consultation Engine
Import/Export Facilities
Table
Structures
Structures
EU-Rent
Conclusions
www.econ.kuleuven.ac.be/prologa
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44
Decision Tables and Business Rules
Conclusions
Issues
Objectives
Examples
Decision
tables
The important issues
• Where do the rules come from?
– Modeling, specification, harvesting
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
• How do we make sure we have the right rules?
– Quality, verification, representation
• How do we get new (and the right) rules quickly?
– Maintenance by business experts
V&V
Table
Structures
• How do we get them to work immediately?
– Declarative
Structures
EU-Rent
• Rule management
– Tools
Conclusions
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129
Some References
Issues
Objectives
Examples
Decision
tables
Concept
Applications
Representation
Kinds of tables
Consistency
by
construction
Construction
V&V
Table
Structures
Structures
EU-Rent
Conclusions
• Goedertier, S. and Vanthienen, J. (2007). Declarative Process Modeling with Business Vocabulary and
Business Rules. In Halpin, T., Nijssen, S., and Meersman, R., editors, Proceedings of Object-Role Modeling
(ORM’07), Lecture Notes in Computer Science (Springer), volume 4805, pp. 603-612.
• Vanthienen, J. (2007). How Business Rules (Re)define Business Processes: A Service Oriented View, 10th
International Business Rules Forum, Orlando, FL (USA), Oct. 21-25.
• Goedertier, S., Mues, C., and Vanthienen, J. (2007), Specifying Process-Aware Access Control Rules in
SBVR, in Paschke, A. and Biletskiy, Y., editors, Advances in Rule Interchange and Applications,
Proceedings of The International RuleML Symposium (RuleML 2007), Lecture Notes in Computer Science
(Springer), volume 4824, pp. 39-52. (Best Paper Award).
• Goedertier, S., Haesen, R., and Vanthienen, J. (2007). EM-BrA²CEv0.1: A vocabulary and execution model
for declarative business process modeling. FETEW Research Report KBI 0728, K.U.Leuven.
http://www.econ.kuleuven.ac.be/public/ndbaf38/EM-BrAACE
• Vanthienen, J. (2006). Consistency by construction: Decision table experiences in business rules and
processes, 9th International Business Rules Forum, Washington, DC (USA), Nov. 5-9.
• Goedertier, S. and Vanthienen, J. (2006). Designing compliant business processes with obligations and
permissions. In Eder and Dustdar, editors, Proceedings of BPM 2006 International Workshops, volume
4103 of Lecture Notes in Computer Science. Springer. pages 5–14.
• Vanthienen, J. (2006). 50 Ways to represent your rule sets, Business Rules Journal, vol. 7, no. 1 (Jan.),
pages 1-7.
• Vanthienen, J. (2004), Quality by design: using decision tables in business rules, Business Rules Journal,
vol. 5, no. 2 (Feb), pp. 7.
• Vanthienen, J., Mues C., Aerts A. (1998), An illustration of verification and validation in the modelling phase
of KBS development, Data & Knowledge Engineering, vol. 27, no. 3 (Oct.), pp. 337 - 352.
• Vanthienen, J., Mues, C., Wets, G., Delaere, K. (1998), A tool-supported approach to inter-tabular
verification, Expert Systems with Applications, vol. 15, no. 3-4 (Oct.-Nov.), pp. 277 - 285.
• Vanthienen, J., Wets, G. (1995), Integration of the decision table formalism with a relational database
environment, Information Systems, vol. 20, no. 7 (Nov.), pp. 595 - 616.
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