1. ABOUT ME ADAPTIVE SOFTWARE | Samudra Kanankearachchi Senior Software Architect@99XTechnology Data Science Specialist NEXT GENERATION OF ADAPTIVE ENTERPRISE APPS

2. GOOGLE SEARCH ON

3. LET’S RE-THINK WHAT DARWIN SAID It is not the strongest species that survive, nor the most intelligent, but the ones most responsive to change

4. Evolution = Variation + Natural Selection EMPIRICAL STUDIES IN WILD

5. 1.Price Sensitive Variations. 2.Quality Sensitive Variations 3.Feature Sensitive Variations EXAMPLE FROM TELEPHONE MARKET

6. One variation Few variation Many variation Redesign REPRODUCTION BY ENGINEERS (LESS ADAPTIVE) Monolithic Architectures Micro Service Architectures SOA Architectures variations are made at application design time (A static approach)

7. Evolution = Variation + End User Selection HOW DO WE MAKE THE EVOLUTION PROCESS DYNAMIC

8. SAMPLE USE CASE ( TOURISM DOMAIN)

9. Feature set: 1. Search location 2. Book hotels 3. Search flights 4. Discounts 5. Popular places

10. USER NEEDS ARE DIFFERENT

11. A MONOLITHIC APPLICATION/ DIFFERENT USERS

12. Budgeter

13. Explorer

14. Traveler

15. STEP1 : DECOMPOSE THE APPLICATION INTO MICRO FEATURES

16. USER APP = F ( FEATURE, ACTIVITY, DEVICE TYPE, USER BEHAVIOR)

17. DEMO

18. Starter Layout

19. STEP 2 - DYNAMIC APP GENERATION

20. CONFIGURATION BASE DYNAMIC APP GENERATION Dynamic Application Generator Feature Configuration Activity Configuration Layout Configuration Application

21. STEP 3 SUPERVISED LEARNING PROCESS

22. Train budgeterTrain Explorer Train traveler AWS Integration Service(Node JS) ML API Adaptive Query API Analytics on user events Models for (B, T, E)

23. AWS Integration Service(Node JS) ML API Adaptive Query API Analytics on user events Question : What is my type Answer :Budgeter A regular user Models for (B, T, E)

24. THANK YOU