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Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16

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Das Gesetz der großen Zahlen gilt immer: Die statistische Sicherheit nimmt mit der Anzahl der Datenpunkte immer zu, sofern die Datennahme fair erfolgt. Leider kostet das Sammeln der Daten oftmals Geld, und so ist man vor allem im Bereich der Sensorik (Stichwort: Internet der Dinge) gezwungen, sinnvolle Kompromisse einzugehen. In diesem Vortrag fasse ich die Erkenntnisse eines Projekts zusammen, in dem die Datenanalytik zeigte, dass man zukünftig nur 60% der ausgebrachten Sensoren wirklich braucht. Auch muss es nicht immer Echtzeit-Analyse sein: Mit einer auf den Business-Case abgestimmten Datenstrategie lassen sich unnötige Ausgaben vermeiden.

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Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16

  1. 1. Mehr und schneller ist nicht automatisch besser Dr. Boris Adryan @BorisAdryan
  2. 2. Dr. Boris Adryan • with Zühlke Engineering since September 2016 • longstanding IoT enthusiast • Founder of thingslearn Ltd. • Board Member & Strategic Advisor for Pycom (microcontrollers), BioSelf (biosensors) and OpenSensors (IoT platform) • before: research group leader for data analytics and machine learning at University of Cambridge, England @BorisAdryan
  3. 3. Vision 2020 Planung, Umsetzung, Betrieb Ihr Partner für Business Innovation Vernetzte System, neue Geschäftsmodelle
  4. 4. 39% of survey participants are worried about the cost of an industrial IoT solution. “Why aren’t you doing IoT?”
  5. 5. IoT cost expectations many sensors + complicated analytics + expensive infrastructure —————————————— IoT has little benefit “…because my data scientist said the more the better ”
  6. 6. peanuts: “a spoon full” How many peanuts are that on average? 0 50 100 “on average” 3 samples
  7. 7. Do I get more peanuts at Maxie Eisen or at Logenhaus? 0 50 100 “on average” Maxie Eisen 3 samples “on average” Logenhaus
  8. 8. 0 50 100 4 samples Do I get more peanuts at Maxie Eisen or at Logenhaus? “on average” Maxie Eisen “on average” Logenhaus
  9. 9. 0 50 100 n samples statistical power through large numbers of samples deviation Do I get more peanuts at Maxie Eisen or at Logenhaus? “on average” Maxie Eisen “on average” Logenhaus
  10. 10. Statisticians and data scientists LOVE larger sample sizes! …but if sampling costs time and resources, we need a compromise.
  11. 11. Zühlke Data Analytics Framework
  12. 12. precision and accuracy that can be achieved theoretically Sampling strategy precision and accuracy that is needed to get a job done accurate and precise not accurate, but precise accurate, not precise not what you want
  13. 13. • how to cut down on hardware costs • how to cut down on software costs Sweetening IoT for your customer A few recommendations from the trenches: many sensors + complicated analytics + expensive infrastructure —————————————— IoT has little benefit less reasonable
  14. 14. Westminster Parking Trial https://www.westminster.gov.uk/new-trial-improve-conditions-disabled-drivers IoT solution Service company ~750 independent parking lots with a total of >3,500 individual spaces access to
  15. 15. Humans don’t scale that well… labour: expensive sensor: cheap While the cost of the sensors is falling (and follows Moore’s Law), digging them in and out for deployment and maintenance is a significant cost factor.
  16. 16. Can we learn an optimal deployment and sampling pattern? •sampling rate of 5-10 min •data over 2 weeks in May 2015 •overall 2.6 million data points Can we make customers’ budget go further by • reducing the number of sensors in a geographic area? • lowering the sampling rate for better battery life?
  17. 17. A quick glimpse into the raw data
  18. 18. Correlation and clustering 0 5 10 15 20 0 3 6 9 12 “correlated” 0 5 10 15 20 0 3 6 9 12 “anti-correlated” 0 5 10 15 20 0 3 6 9 12 “independent” lorry coach car bike skateboard hierarchical clustering on the basis of a feature matrix
  19. 19. Good news: temporal occupancy pattern roughly predicts neighbours lots in Southampton lots around the corner of each other 750 parking lots
  20. 20. A caveat: Is a high-degree of correlation a function of parking lot size? finding two lots of 20 spaces that correlate finding two lots of 3 spaces that correlate 0:00 12:00 23:59 0:00 12:00 23:59 “more likely” “less likely”
  21. 21. Bootstrapping in DBSCAN clusters Simulation: Swap the occupancy vectors between parking lots of similar size and test per grid cell if these lots still correlate
  22. 22. What makes a good spatial cluster?
  23. 23. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) https://en.wikipedia.org/ wiki/DBSCAN#/media/ File:DBSCAN-Illustration.svg 2 parameters: epsilon (distance) minPoints (in cluster) A - core points B, C - corner points N - noise point
  24. 24. Stratification strategy 3 lots with cc > 0.5 2 spaces 4 spaces 4 spaces Test: 1. Take occupancy profile of ONE random 2-space parking lot and TWO random 4-space parking lots. 2. Determine cc. 3. Repeat n times and get a cc distribution for that parking lot combination.
  25. 25. Combining stats with street knowledge
  26. 26. Verdict: In some grid cells the level of the occupancy of one parking lot predicts the occupancy of most parking spaces. x x x x x x x x x x x x x x x x Better for navigation We suggested that about 60% of the sensors may be sufficient. Better predictive power
  27. 27. Suggested technology for trials A temporary survey would have allowed us to make the same recommendation, including the insight that the provided 5’ resolution is probably not required.
  28. 28. • how to cut down on hardware costs • how to cut down on software costs Sweetening IoT for your customer A few recommendations from the trenches: many sensors + complicated analytics + expensive infrastructure —————————————— IoT has little benefit less reasonable
  29. 29. My current pet hate: Deep Learning Deep learning has delivered impressive results mimicking human reasoning, strategic thinking and creativity. At the same time, big players have released libraries such that even ‘script kiddies’ can apply deep learning. It’s already leading to unreflected use of deep learning when other methods would be more appropriate.
  30. 30. “I need to do real-time analytics!” microseconds to seconds seconds to minutes minutes to hours hours to weeks on device on stream in batch am I falling? counteract battery level should I land? how many times did I stall? what’s the best weather for flying? in process in database operational insight performance insight strategic insight e.g. Kalman filter e.g. with machine learning e.g. rules engine e.g. summary stats
  31. 31. Can IoT ever be real-time? zone 1: real-time [us] zone 2: real-time [ms] zone 3: real-time [s]
  32. 32. Edge, fog and cloud computing Edge Pro: - immediate compression from raw data to actionable information - cuts down traffic - fast response Con: - loses potentially valuable raw data - developing analytics on embedded systems requires specialists - compute costs valuable battery life Cloud Pro: - compute power - scalability - familiarity for developers - integration centre across all data sources - cheapest ‘real-time’ option Con: - traffic Fog Pro: - same as Edge - closer to ‘normal’ development work - gateways often mains-powered Con: - loses potentially valuable raw data
  33. 33. Some of our examples for real-time analytics Choosing the appropriate method and toolset on every level.
  34. 34. Options for real-time in cloud some features can cost a bit, especially when you don’t really know what you’re doing and want to ‘try it out’. a badly configured SMACK stack on your own commodity hardware can be slow and unreliable your pre-trained classifier
  35. 35. Dr. Boris Adryan @BorisAdryan ‣ Preliminary surveys and data analysis can help to minimise the number of sensors and develop an optimal deployment strategy and sampling schedule. ‣ Super-fast analytics and state-of-the-art methods are not automatically the most useful solution. ‣ A good understanding on the type of insight that is required by the business model is essential. Zühlke can advise on options around IoT and data analytics, and provide complete solutions where needed. Summary

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