ABOUT ME ADAPTIVE SOFTWARE | Samudra Kanankearachchi Senior Software Data Science Specialist NEXT GENERATION OF ADAPTIVE ENTERPRISE APPS

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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

Evolution = Variation + Natural Selection EMPIRICAL STUDIES IN WILD

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

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)

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

SAMPLE USE CASE ( TOURISM DOMAIN)

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

USER NEEDS ARE DIFFERENT

A MONOLITHIC APPLICATION/ DIFFERENT USERS

Budgeter

Explorer

Traveler

STEP1 : DECOMPOSE THE APPLICATION INTO MICRO FEATURES

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

DEMO

Starter Layout

STEP 2 - DYNAMIC APP GENERATION

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

STEP 3 SUPERVISED LEARNING PROCESS

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

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)

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