The document discusses using formal concept analysis (FCA) and business rules to develop a customer relationship simulation model for a telecommunications company. It provides background on business rule management systems (BRMS) and reviews some common BRMS vendors. The document then explains how FCA can be used to analyze customer attribute data, extract customer groups and rules about what marketing actions would be effective for different customer profiles. It provides examples of rules extracted from a formal customer context and discusses criteria for evaluating rule quality like confidence, conviction and lift.

1. Russian Plekhanov University of Economics Customer-telecommunications company’s relationship simulation model (RSM), based on non-monotonic business rules approach and formal concept analysis method. Victor Romanov Roman Veynberg AlinaPoluektova

2. Contents The problem actuality 1 BRMS review 2 EDM-conception and business rules application technology for decision making 3 4 Business rules theory 5 FCA for rules mining Business rules application at telecommunication sector 6

3. Why business rules? Dynamic competition economy In big and medium business a lot of documents contain business rules. EDM new conception propose extract business rule as different component, This makes possible more easy update them It is difficult to find and change them

4. Business static void processLoanRequest(Borrower borrower, Loan loan) { System.out.println("Processing request from " + borrower.getName()); // Approve or reject the loan checkLoanConditions(borrower, loan); // Display the verdict if (loan.isApproved()) { System.out.println("==> Loan is approved :-)"); } else { System.out.println("==> Loan is rejected :-("); for (Object msg : loan.getMessages()) { System.out.println("==> Because " + msg); } } } /** * Check conditions on the borrower and the loan using hard-coded policies */ static void checkLoanConditions(Borrower borrower, Loan loan) { // Check maximum amount if (loan.getAmount() > 1000000) { loan.addToMessages("The loan cannot exceed 1,000,000"); loan.reject(); } // Check repayment and score if (borrower.getYearlyIncome() > 0){ int val = loan.getYearlyRepayment() * 100 / borrower.getYearlyIncome(); if ((val>=0) && (val<30) && (borrower.getCreditScore()>=0) && (borrower.getCreditScore()<200)) { loan.addToMessages("debt-to-income too high compared to credit score"); loan.reject(); } if ((val>=30) && (val<45) && (borrower.getCreditScore()>=0) && (borrower.getCreditScore()<400)) { loan.addToMessages("debt-to-income too high compared to credit score"); loan.reject(); } if ((val>=45) && (val<50) && (borrower.getCreditScore()>=0) && (borrower.getCreditScore()<600)) { loan.addToMessages("debt-to-income too high compared to credit score"); loan.reject(); } if ((val>=50) && (borrower.getCreditScore()>=0) && (loan.getAmount() > Business Logic Applications codes IT What business rule is Business rule is the assertion at the natural or formal language,which for each state of business system defines permissible decisions on business control

5. The main BRMS vendors : IBM ILogJrules FICO Blaze Advisor Corticon BRMS Innovations Software Technology Visual Rules The Forrester Wave™ за второй квартал 2008 г.

6. Business rules management system The sources where rules originated from Documents Applications The rules are stored and updated The rules are extracted and executed The rules are inserted Personell Processes Business Rule Management System User Applications Rules + Metadata Rules repository Rules Server

7. BRMSApplications Domains

8. Corticon Technologies:Corticon BRMS Rules Modeling Server Rules Execution Server Data Base Connector Software Environment

9. IBM’s ILogJRules Business rule development Business Rule management Rule project Object Model Web application Rule parameters Vocabulary Synchronize business rule, decision tables Rule repository Synchronize Flow rule Deploy Deploy Deploy Decision Validation Services Decision Validation Services Application repository Application repository Application repository ArchitectureILogJRules

10. Component of ILogJRules Rule Studio Rule Team Server Rule Execution Server Rule Solutions for Office

11. Innovation Technologies: Visual Rules Modeling Analysis Monitoring Documentation Execution Test and Simulation Administration Deployment

12. FICO: Blaze Advisor Production Rule Repository Deployment Manager Customers Application Testing Rule Repository Application Server Rule Development Repository Business Rule Authoring Rule Development Architecture of Blaze Advisor

13. Business rules application for business system decision making1 1 Business rule based data analysis for decision support and automationhttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.928&rep=rep1&type=pdf

14. The business rules formal definition At the theory level of first level logic (FOL) business rules have statement view IF-THEN and expresses logical consequence or implication. IF p, THEN q,where q – assertion named as consequent, describing decision which are offering in this conditions. p is a assertion, named as antecedent, which is describing state of business conditions IF(conditions), then(the list of actions), else(alternate list of actons).

15. Business rules development and management

16. Formal Concept Analysis Formal context K:=(G,M,I) consists of sets G,M and a binary relation I ⊆ G ×M. M –attribute set, G –objects sets (g,m) ∈ I - object g has attribute m Let us define the mapping: ϕ: -> и ψ: -> ϕ(A)=def {m ∈ M | gIm ∀g ∈ A}, ψ(B)= def {g ∈ G | gIm ∀m ∈ B}, A ⊆ G, B ⊆ M. If A ⊆ G, B ⊆ M, then (A,B)- formal concept of context K, if ϕ(A) = B, ψ(B) = A

17. Formal Concept Analysis (FCA) Subconcept - superconcept relationship: A1,A2 ⊆ G, B1,B2 ⊆ M: 1. (A1,B1) (A2,B2) (A1 ⊆ A2) (B2 ⊆ B1) 2. (A1,B1) – subconcept, 3. (A2,B2) –superconcept, A1,A2 – intent B1,B2 - extent The relationships above define concepts lattice

18. FormalConceptAnalysis FCA may be used for visualization telecommunication company’s customer groups, that make possible for management assign to these groups corresponding set of discount options. Besides selecting the group of customers FCA method provide possibility by mean data mining approach extract new rules from customer database. The clients may be considered as an objects and their personal data, realty employment positions may be regarded as attributes. According to these data subsets of groups and their attributes may be selected as a concepts with common features.

19. CUSTOMER’S ATRIBUTES

20. Formal context “customers” Context part 1 Context part 2 (continuation)

21. Concept lattice “customers”

22. The rules discovered The rules discovered by FCA look like this: different kind of if customers satisfy different conditions and for them different marketing actions are effective: < 1 > age_25 gender_male head sms ms Loc_callGprsCons_mid ==> Inc_m Act1_eff; IF age <= 25 AND gender_male = true AND head = true AND smsms = true AND Loc_call = true AND Gprs= true AND Cons_mid= true THEN Act1_eff; < 2 > age_25 single Loc_callCons_mid ==> Act2_eff; IF age<=25 AND single = true AND Loc_call = true AND Cons_mid = true THEN Act2_eff; < 3 > Cons_high ==> sitizenInc_hInt_callGprs Act3_eff; IF Cons_high = true AND sitizen = true AND Inc_h = true AND Int_call = true AND Gprs THEN Act3_eff;

23. The rules with confidence <100 % 63 < 5 > Cons_mid =[80%]=> < 4 > Act2_eff; 66 < 5 > single Loc_call =[80%]=> < 4 > Act2_eff;

24. Rules quality criteria Let M – attribute set and G objects set. The rules are defined as the implication X⇒Y, whereX,Y ⊆ M, X Y =. The implication means that all objects of context which contain attributes X also contain attribute Y. That is in the situation X manager ought make decision Y. 3 conviction confidence lift support Is defined as supp(X Y)/ supp(Y) supp(X) Is defined as conf(X⇒Y)= supp(XY)/ supp(X) Conviction conv(X⇒Y)=1-supp(Y)/1- conf(X⇒Y) Supp=card(ψ(X)/card(G)) - is a rate of contextobjects K := (G,M, I), which contain attributes X