NBQSA 2nd round Presentation
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Transcript of 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