NBQSA 2nd round Presentation

Few Statistics…Time and Savings Deposits held by the Public

2010 1,405,808

2011 1,753,896

2012 2,143,136

Crime Rate in Sri Lanka (Source: http://www.police.lk/index.php/crime-trends)

Health Expenditure in Sri Lanka(Source: http://www.who.int/gho/countries/lka.pdf)

“Lot of data waiting to be mined…”

(Source: http://www.cbsl.gov.lk/pics_n_docs/10_pub/_docs/statistics)

Introduction What is Weave-D?

Inspired by human brain Data Accumulating, Learning and Fusing

System

Video

Supports Multi-modal data

Incremental learning

Inspiration source

Why Weave-D?

Growth of information

Handle data

Prevent catastrophic

forgetting

Visualizing information Conceptualization

??

??Intuitive

Simple

Come as chunks

Heterogeneous Incremental learning

Generalization of acquired knowledge

Apply previous knowledge to acquire

new knowledge

Business Value Medical

What we can mine? New patient has a cancer or not? Effective medicine for certain diseases Diseases distribution in the country

E.g. Anuradhapura – more kidney diseases

Business Value Finance

Predict customers’ transactional behaviors, so banks can plan their strategies ahead

Forensics or Police Predict criminal behavior Identify crimes with similar evidence

And many more…

Similar Products IBM Watson

Developed by IBM to compete in Jeopardy A Question answering system Consumes “millions” of Wikipedia pages

and try to find answers from the knowledge acquired

Finance and health care domains

Uniqueness

RapidMiner IBM Watson Weave-D

Support heterogeneous data

x x Learn without forgetting past data

x x Support analyzing at different granularities

x x Visualization Fast response x x

What does Weave-D do?

Business LogicPersistenceHandlers PresentationPersistence

Weave-D architecture

Config filesXML

Raw Data Learning Component

Link Generators

XML Parsers

XML WritersUser

Interfaces

Feature Extractor Facade

Weave-D Facade

Data Models

XML Outputs

3D Visualization

Interface

Perception Model

Configuration Loaders

Logger

Feature Extractors

Knowledge Representation

Day 1 2 3

Input

Layer 1 (Day 1) Layer 2 (Day 2) Layer 3 (Day 3)

(None)

Dataset 1 Dataset 2 Dataset 3

City (Day view) City (Night view)

Forest (Autumn) Forest (Spring)Forest (Winter)

Child (4-8 years old)

Child (1-4 years old)

Child (8-12 years old)

Sunset view Sunset view

C1

C2

C3

C4

C5

Demonstration - Scenario Description

Sam is a sports enthusiast. He has a set of images belonging to following sports; Croquet, Polo, Rock-climbing, Sailing, Rowing, Badminton. Also he has a small description of the sport for each image. He needs to cluster these images and text by the sports category.

Constraints All the photos are not available to him at once. He

gets sets of images each day. (Incremental learning)

User’s Point of View Input

Query image

Expected outcomes Set of related images and documents explaining the sport

Tasks Setting up Weave-D Training Weave-D Querying from Weave-D

Sam doesn’t know what sport this is (Query image) Meaningless file names! Get documents explaining the sport denoted by image

What happens inside?

Query Image

Day 1 Day 2 Day 3

Imag

esTe

xt

Result Images

Result Text

Associative Links

Time Series Links

Bigger Picture!!! Medical domain Forensic domain

Methodology Standards Agile development – Scrum Documentation

Architecture documents Class diagrams

Git version controlling Tests

Class Diagrams

Github

Milestones

Architecture Document

Website

Implementation Standards Rich client platform Object Oriented Programming Design patterns

Factories Facades Command Objects

High decoupling XML Configuration

Monetization Plans? Promotions through Social Media

Facebook Google+

Advertising on Data Mining websites KDNuggets

Discussions ICTA Private Hospitals Private Investigation Agencies

Investments? Project group

Sri Lanka Police

National Hospital

Few years ahead in MoneyPath

January, 2015

January, 2016Today

1st Release 2nd Release

January, 2014

Part Time Full Time

Break evenAdvertising campaign

(Rs. 15,000)

Labor cost (4 members)

(Rs. 60,000)

Other (Rs. 25,000)

Initial Investment

(Rs.100,000)

Sell 5 units1 unit = 80K-100K

Sell 10 units 1 unit = 150K-200K

Profitable

Glimpse to the Future Support mining information at

different granularities Extend Weave-D Client-Server

architecture Support already existing standards

(e.g. PMML)

Further Resources Website:

http://weave-d.com/ Facebook Page: https://

www.facebook.com/treadlabz.weaved Google+ Page: https://

plus.google.com/102785205487583718859

Thank you