The document provides an overview of Weave-D, which is described as a data accumulating, learning and fusing system inspired by the human brain. It supports incremental learning from heterogeneous and multimodal data like video. Weave-D is designed to handle data in chunks, apply previous knowledge to acquire new knowledge, generalize acquired knowledge, and prevent catastrophic forgetting. Potential applications are discussed in domains like medicine, finance, and forensics. The architecture of Weave-D and how it represents knowledge over time are briefly outlined.

2. Few Statistics…
(Source:
http://www.cbsl.gov.lk/pics_n_docs/10_pub/_doc
s/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)

3. Introduction
 What is Weave-D?
 Inspired by human brain
 Data Accumulating, Learning and Fusing
System
Supports Multimodal data
 Video
Incremental
learning
Inspiration
source

4. Why Weave-D?
Apply previous
knowledge to acquire
new knowledge
Heterogeneous
?
Handle
data
Come as chunks
Prevent
catastrophic
forgetting
Incremental
learning
?
Growth of information
Intuitive
Visualizing
information
Simple
Generalization of
acquired knowledge
?
Conceptualization
?

5. 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

6. 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…

7. 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

8. 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

9. What does Weave-D do?

10. Weave-D architecture
Raw Data
Learning
Component
Link
Generators
Perception
Model
Logger
XML Writers
XML Outputs
Persistence
Persistence
Handlers
Feature
Extractor
Facade
Configuration
Loaders
Business Logic
Feature Extractors
Weave-D Facade
XML Parsers
Data Models
Config files
XML
User
Interfaces
3D
Visualization
Interface
Presentation

11. Knowledge Representation
Layer 1 (Day 1)
Day
Input
1
Layer 2 (Day 2)
Layer 3 (Day 3)
2
3

12. C3
C1
Child (4-8 years old)
Child (8-12 years old)
Child (1-4 years old)
C4
Forest (Autumn)
Forest (Spring)
Forest (Winter)
C2
City (Day view)
C5 City (Night view)
(None)
Sunset view
Dataset 1
Dataset 2
Sunset view
Dataset 3

13. 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)

14. 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

15. Images
What happens inside?
Query
Image
Result Images
Text
Day 1
Day 2
Day 3
Result Text
Time Series Links
Associative Links

16. Bigger Picture!!!
 Medical domain
 Forensic domain

17. Methodology Standards
 Agile development – Scrum
 Documentation
 Architecture documents
 Class diagrams
 Git version controlling
 Tests

18. Class Diagrams
Milestones
Github
Website
Architecture
Document

19. Implementation Standards
 Rich client platform
 Object Oriented Programming
 Design patterns
 Factories
 Facades
 Command Objects
 High decoupling
 XML Configuration

20. Monetization Plans?

Promotions through Social Media
 Facebook
 Google+

Advertising on Data Mining websites
 KDNuggets

Discussions
 ICTA
 Private Hospitals
 Private Investigation Agencies

National
Hospital
Investments?
 Project group
Sri Lanka
Police

21. Few years ahead in Money
Path
Sell 5 units
1 unit = 80K-100K
Part Time
Today
Initial
Investment
(Rs.100,000)
Full Time
January,
2014
1st Release
Advertising
campaign
(Rs. 15,000)
Sell 10 units
1 unit = 150K-200K
January,
2015
January,
2016
2nd Release
Labor cost (4
members)
(Rs. 60,000)
Break even
Other
(Rs. 25,000)
Profitable

22. Glimpse to the Future
 Support mining information at different
granularities
 Extend Weave-D Client-Server architecture
 Support already existing standards (e.g.
PMML)

23. Further Resources
 Website:
http://weave-d.com/
 Facebook Page:
https://www.facebook.com/treadlabz.weave
d
 Google+ Page:
https://plus.google.com/10278520548758371885
9

24. Thank you