Few Statistics…

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

Time and Savings Deposits held by ...
Introduction
 What is Weave-D?
 Inspired by human brain
 Data Accumulating, Learning and Fusing
System

Supports Multim...
Why Weave-D?

Apply previous
knowledge to acquire
new knowledge

Heterogeneous

?

Handle
data
Come as chunks

Prevent
cat...
Business Value
 Medical
 What we can mine?
 New patient has a cancer or not?
 Effective medicine for certain diseases
...
Business Value
 Finance
 Predict customers’ transactional behaviors, so
banks can plan their strategies ahead

 Forensi...
Similar Products
 IBM Watson
 Developed by IBM to compete in Jeopardy
 A Question answering system
 Consumes “millions...
Uniqueness
RapidMiner

IBM Watson

Weave-D

Support heterogeneous
data

x

x



Learn without forgetting
past data

x

x
...
What does Weave-D do?
Weave-D architecture
Raw Data

Learning
Component

Link
Generators

Perception
Model

Logger

XML Writers

XML Outputs

Pe...
Knowledge Representation
Layer 1 (Day 1)

Day
Input

1

Layer 2 (Day 2)

Layer 3 (Day 3)

2

3
C3

C1

Child (4-8 years old)

Child (8-12 years old)

Child (1-4 years old)

C4

Forest (Autumn)

Forest (Spring)

Forest...
Demonstration - Scenario
 Description
 Sam is a sports enthusiast. He has a set of
images belonging to following sports;...
User’s Point of View


Input
 Query image



Expected outcomes
 Set of related images and documents explaining the spo...
Images

What happens inside?
Query
Image

Result Images

Text

Day 1

Day 2

Day 3

Result Text

Time Series Links
Associa...
Bigger Picture!!!
 Medical domain
 Forensic domain
Methodology Standards
 Agile development – Scrum
 Documentation
 Architecture documents
 Class diagrams

 Git version...
Class Diagrams

Milestones

Github

Website

Architecture
Document
Implementation Standards
 Rich client platform
 Object Oriented Programming
 Design patterns
 Factories
 Facades
 Co...
Monetization Plans?


Promotions through Social Media
 Facebook
 Google+



Advertising on Data Mining websites
 KDNu...
Few years ahead in Money
Path
Sell 5 units
1 unit = 80K-100K

Part Time
Today

Initial
Investment
(Rs.100,000)

Full Time
...
Glimpse to the Future
 Support mining information at different
granularities
 Extend Weave-D Client-Server architecture
...
Further Resources
 Website:
http://weave-d.com/
 Facebook Page:
https://www.facebook.com/treadlabz.weave
d
 Google+ Pag...
Thank you
NBQSA 2nd round Presentation
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NBQSA 2nd round Presentation

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Presentation at NBQSA 2nd round evalutions held on 14th September 2013 at UCSC.

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  • Data accmulation and fusion system. Seems like an already achieved thing and straightforwardLet me tell you how this is special from other tools out thereAppear Heterogenous (describe)Appear Incremental learning (describe)
  • Data is no longer homogeneous (it is a combination of images, text, audio)Weave-D supports heterogeneous dataData is no longer available at once, data arrives as streams, at different timesWeave-D can learn incrementallyNot all information is important, user should be able to select which features are importantWeave-D allows user to select important features of data
  • Would this be better if we present as a 2d flow chart?
  • Show config filesShow componentsShow as a diagram
  • Few points about what is the experiment what we’re trying to achieveExploratory mining techniqueVery difficult to measure the qualityBy InspectionCluster PurityShow horizontal not vertical
  • Sam has an image and he doesn’t know what sport this is. And the images and text files does not have very meaningful filenames. (otherwise he could have guessed the name and found the sport). What Sam can do is, he can query this image from Weave-D and find related images and text both. Then by reading the returned documents, he can figure out the sport.Rename data to have meaningless names!
  • Few examples explaning the same task Sam querying image and getting text in other domainsEx. Radiologist input and image of a cancer and get the full detailed reports relatedEx. Forensic investigators input an audio clip of a criminal and getting picture of a person as the resultFlexible architectureAllow user to form the architecture!Intuitive UI (Drag & Drop)
  • Potential Customers
  • NBQSA 2nd round Presentation

    1. 1. 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)
    2. 2. Introduction  What is Weave-D?  Inspired by human brain  Data Accumulating, Learning and Fusing System Supports Multimodal data  Video Incremental learning Inspiration source
    3. 3. 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 ?
    4. 4. 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
    5. 5. 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…
    6. 6. 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
    7. 7. 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
    8. 8. What does Weave-D do?
    9. 9. 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
    10. 10. Knowledge Representation Layer 1 (Day 1) Day Input 1 Layer 2 (Day 2) Layer 3 (Day 3) 2 3
    11. 11. 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
    12. 12. 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)
    13. 13. 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
    14. 14. Images What happens inside? Query Image Result Images Text Day 1 Day 2 Day 3 Result Text Time Series Links Associative Links
    15. 15. Bigger Picture!!!  Medical domain  Forensic domain
    16. 16. Methodology Standards  Agile development – Scrum  Documentation  Architecture documents  Class diagrams  Git version controlling  Tests
    17. 17. Class Diagrams Milestones Github Website Architecture Document
    18. 18. Implementation Standards  Rich client platform  Object Oriented Programming  Design patterns  Factories  Facades  Command Objects  High decoupling  XML Configuration
    19. 19. 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
    20. 20. 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
    21. 21. Glimpse to the Future  Support mining information at different granularities  Extend Weave-D Client-Server architecture  Support already existing standards (e.g. PMML)
    22. 22. Further Resources  Website: http://weave-d.com/  Facebook Page: https://www.facebook.com/treadlabz.weave d  Google+ Page: https://plus.google.com/10278520548758371885 9
    23. 23. Thank you

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