The Fifth Elephant - 2013 Talk - "Smart Analytics in Smartphones"

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I gave a talk at the Fifth Elephant -2013 in Bangalore. Here is the ppt of my talk "Smart Analytics in Smartphones".

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The Fifth Elephant - 2013 Talk - "Smart Analytics in Smartphones"

  1. 1. - 1 / 26 - 방갈로르연구소(SISO)11 Smart Analytics in Smartphones Satnam Singh, PhD Samsung Research India -Bangalore Fifth Elephant Conference, Bangalore July 13, 2012 Disclaimer: Talk is based on my personal views and knowledge gathered from open sources
  2. 2. - 2 / 26 - 방갈로르연구소(SISO)22 • What is Smart Analytics? • Trends in Smart Analytics • Why to do Analytics in Device? • Case Study: Sensory Data Analytics Outline
  3. 3. - 3 / 26 - 방갈로르연구소(SISO)33 Smart Analytics - Analytics keeping end-user in mind - Enable use cases to bring new experience, ease and benefits to end-user Buying habits Location and time Activity Entertainment User Presence Sensor Data User Data Social Data SNS Data, RSS feeds Images, Videos, Music, Call logs, SMS data Browser data
  4. 4. - 4 / 26 - 방갈로르연구소(SISO)44 Smart Analytics in Smartphones Sensor Data - Enhance User Experience - Recommendations - Personalization Social data User data… Analytics (Text Mining, Machine Learning, Signal Processing) Sensor Data User Data Social Data 3rd Party Applications, Native Applications
  5. 5. - 5 / 26 - 방갈로르연구소(SISO)55 User Data Analytics- Trends Breadcrumbs • A Simple Timeline of your Day • Everything happening at your places • Offers and Deals for your favorite places Radii • Connecting Personality to Places • Match the place's personality with users personality to give the best recommendations • Deliver movie-like game experiences, videos, images and wallpapers • Bring users into the film's story and world Paramount Pictures - Star Trek Into Darkness Qualcomm’s Gimbal Platform Applications
  6. 6. - 6 / 26 - 방갈로르연구소(SISO)66 Sensory Data Analytics - Trends Galaxy S4 Sensors  Multiple sensors, Environment sensing Activity Recognition [Sensor Platforms, Alohar mobile, ActiServ]
  7. 7. - 7 / 26 - 방갈로르연구소(SISO)77 Analytics in Server vs. Device Device-based Analytics - Privacy concerns are taken care of.. • It works even if no network !! • Need predictive models to run close to real-time and automatically deploy them • Power and battery consumption should be kept under control Server-based Analytics is needed if the application is too compute intensive for a smart phone • Latency and data transfer cost • Data must be communicated securely • Authentication before any data transfer
  8. 8. - 8 / 26 - 방갈로르연구소(SISO)88 Case Study: Sensory Data Analytics Activity Recognition: Detect walking, driving, biking, climbing stairs, standing, etc. Activity Recognition Running Biking Climbing stairs Walking Sitting 1. If phone call comes then Send an automated SMS to call later 3. Do not refresh location  Save battery power 2. If phone call then increase ring tone
  9. 9. - 9 / 26 - 방갈로르연구소(SISO)9 Data Visualization – Raw Data & Activity (Class Variable) [Ref] Rattle R Data Mining Tool Bar Plot Example of Accelerometer data
  10. 10. - 10 / 26 - 방갈로르연구소(SISO)1010 Activity Recognition - Steps Feature Extraction Time Series Data 43 Features Mean for each acc. Axis (3) Std. dev. for each acc. Axis (3) 200 samples (10 sec) Avg. Abs. diff. from Mean for each acc. Axis (3) Avg. Resultant Acc. (1) Histogram (30) Classifier CART: Decision Tree Classify the Activity [Ref] Gary M. Weiss and Jeffrey W. Lockhart, Fordham University, Bronx, NY [Ref] Jordan Frank, McGill University [Ref] Commercial API Providers: Sensor Platoforms, Movea, Alohar
  11. 11. - 11 / 26 - 방갈로르연구소(SISO)11 [Ref] Rattle R Data Mining Tool Decision Tree -Accuracy for general model~75%, >95% personalized model using 10 seconds training for each activity -Accelerometer sensor is low power consuming sensor - Use other sensors to figure out where is smartphone  Enhance accuracy by 5-6%
  12. 12. - 12 / 26 - 방갈로르연구소(SISO)12 Activity Recognition: Engg. Challenges “Design Considerations for the WISDM Smart Phone-based Sensor Mining Architecture,” SensorKDD ’11, Fordham University • Supervised models- problems in collecting user data • Data sampling rate for each activity: o High sampling rate than needed  waste CPU cycles, o While low sampling rate degrade the performance • App should work even if device is in hibernation mode • Control SQLite database overheads • Power consumption and real-time computations • Benchmarking and user testing is a key challenge • Global user – support multiple languages for any text mining application
  13. 13. - 13 / 26 - 방갈로르연구소(SISO)13 • Fusion of data science and domain knowledge can bring new experiences for end-users • Getting data analytics-based feature in product needs intense team effort between various stakeholders Summary Thanks!!
  14. 14. - 14 / 26 - 방갈로르연구소(SISO)1414 Backup Slides
  15. 15. - 15 / 26 - 방갈로르연구소(SISO)15 [Ref] Rattle R Data Mining Tool … Σ Random Forest Tree1 Tree2 Treen Random Forest: An Ensemble of Trees
  16. 16. - 16 / 26 - 방갈로르연구소(SISO)16 Another Approach: Activity Recognition Feature Extraction using PCA Classification using SVM 9 PCs Classify the activity “Activity and Gait Recognition with Time-Delay Embeddings” Jordan Frank, AAAI Conference on Artificial Intelligence -2010 McGill University

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