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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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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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