1. Marine Institute of Ireland: Streams architecture and deployment deep dive Krishna Mamidipaka, krishnag@us.ibm.com Roger Rea, rrea@us.ibm.com

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4. Agenda Solution overview Why InfoSphere Streams? Solution details

5. Analytics & Sensors Advanced Acoustical Analytics InfoSphere Streams  Filter wind & wave noise  Model Marine Mammal environment  Correlate to Galway Bay ecosystem Real Time Marine Mammal Position ++ =

6. IBM InfoSphere Streams v1.2 Development Environment Runtime Environment Toolkits & Adapters Front Office 3.0 RHEL v5.3 or v5.4 x86 multicore hardware InfiniBand support Up to 125 servers Eclipse IDE StreamSight Stream Debugger Connectors to data sources Operator Library Financial Toolkit Mining Toolkit

7. Streams Programming Model Streams Processing Language Input OutputProcess Platform optimized compilation

8. X86 Box X86 Blade Cell Blade X86 Blade FPGA Blade X86 Processor X86 Processor X86 Processor X86 Processor X86 Processor Streams Runtime Illustrated Transport Streams Data Fabric Processing Element Container Optimizing scheduler assigns operators to processing nodes, and continually manages resource allocation Runs on commodity hardware – from single node to blade centers to high performance multi-rack clusters

9. X86 Processor X86 Processor X86 Processor X86 Processor X86 Processor Transport Streams Data Fabric Processing Element Container Can adapt to changes in resources, workload, data rates Streams Runtime Illustrated

10. SmartBay Overview  Multi year joint development between the Marine Institute, IBM and others  Project 1:Next generation integrated cyberphysical environment for sensors in environmental monitoring and management  Project 2 : Integrated data and information environment with innovative human interface and advanced visualization capabilities supporting multidisciplinary users in environmental monitoring management and sustainable energy  Project 3 : Advanced device monitoring and management for remote sensors and data collection/aggregation platforms  Project 4: Real-time distributed stream analytical fabric for environmental monitoring and management

12. Project 4 – Cetacean Streaming Analytics Goals  Real-time acoustic analysis of hydrophone data  Initial goals to use echo location clicks to identify  Cetacean Species (and sub-species)  Count  Distance  Extended Goal  Individual animal detection & recognition

13. Hydrophone Audio High frequency Requirement to sample up to 300 KHz Bottlenose dolphins produce directional, broadband clicks in sequence. Each click lasts about 50 to 128 microseconds. Peak frequencies of echolocation clicks are about 40 to 130 kHz. Medium resolution (16bit mono) but can be higher Contains environmental (natural and artificial) noise sound of weather on ocean surface, sea bed activity artificial noise from marine traffic (propellers) Seismic surveys Not normalised (significant drift)

14. Just over.5 second of data Click periodicity varies based on activity e.g. navigation, hunting

15. Species Identification  In simpler terms “Click Detection” and “Click Profiling”  Three primary stages in operation  Pre-click detection  establishing a click “hint”  Clean Up / Dynamic Filtering  isolating key frequencies  Frequency and Time Based Click Profiling / Detection  Arriving at a decision over species based on matching to known characteristics

16. Dynamic Pre-Click Detection – a click “hint”  An algorithm to detect “potential” clicks in the acoustic data  By calculating a rolling average of the incoming sound pressure / intensity level we can then dynamically change the max / min thresholds used to for checking for potential clicks, holding above a given low threshold for a certain duration Max Min

17. Pre-Click Detection Red – unfiltered, Green – Filtered, Blue – Click “hints”

18. Clean Up / Dynamic Filtering  Number of factors effecting the intensity / sound pressure of the received acoustic signal  Water temperature  can change significantly above / below any thermocline  monitored via the deployment buoy  Salinity  maps are available  can also be monitored but does not shift significantly  Species Most significantly the Distance of source from the hydrophone  Necessary to have understanding of the sound pressure level and “species hint” (based on mean frequency) to apply appropriate filter  Specific filter applied based on industry recognised sound pressure lookup adjusted for distance

19. Click Detection & Profiling  Moving from a species “hint” to a more robust approach requires a series of checks to confirm a click and try to identify the species*  Comparison of the Relative energy in two different frequency bands  Peak spectral frequency in the click  The width of the main frequency peak (based on 80% of the energy)  The duration of the click * Credit Marjolaine Caillat, SMRU, 2005

20. FFT  Most of these techniques require that we provide both time domain and frequency domain for the input signal  FFT normally computed across a “window” of samples returning the frequency distribution of the time based signal  Window size is governed by the number of samples in a click rounded up to the nearest factor of 2  There is a computational overhead in running FFT on each click so minimising the window size is of benefit

21. Peak spectral frequency in the click

22. Pre-click Detector Click Detection Click Profiling and Detection – 4 CriteriaDynamic Filtering WAV Decoder High Pass Filter (remove < 10 kHz) FFT (4096 Window) Calc. mean frequency “Species Hint” and split Porpoise Fo 131 kHz Calc Sound Pressure Level (inv. sq.) Common Dolphin Fo = 80 kHz Calc Sound Pressure Level (inv. sq.) 175 dB Low order BP Filter 161 dB Medium order BP Filter 151 dB High order BP Filter 230 dB Low order BP Filter 216 dB Low order BP Filter 210 dB Low order BP Filter SPL in dB is calculated using the inverse square Law High = 1 meter Medium= 5 meter Low = 20 meters FFT (512 Window) Mean Frequency Band Energy Peak Position and Width Click Length Click Counter Dynamically Change -Click Detector. Parameters Depending on average SPL then Assign Click # Low Pass Filter (remove > 10 kHz) *To Do Low Frequency Species Click Detection

23. Determining Counts  Animal counts are based on building click trains through correlation  Correlate from click to click based on relative energy and peak frequency  Additional rules need applying to account for “re-visits” Credit - Josefin Starkhammara, Johan Nilssona, Mats Amundinb, Tomas Janssona, Monica Almqvista, Hans W. Perssona

24. Visualisation - Spectrogram  There are a number of metrics planned for the acoustic work through the SmartBay portal  Species breakdown  Species count  Distance from hydrophone  A “basic” spectrogram (sound intensity by frequency and time) is shown below

25. Performance  Our initial approach was to treat each audio sample (int) as a single tuple (300k per second)  Allowed us to implement all of the Streams operators quickly to validate the end to end processing  Recently moved to  integer list of 128 samples per tuple up to the pre-click detector  Followed by integer list of the total click per tuple  And Integer list of the frequency spectrum for a complete click per tuple  Moving to multiple samples per tuple has resulted in approx. 20x performance improvement (faster than real time)  Currently testing on cluster of 4 x xSeries 3950 (8GB Ram, 2xQuad Core)

26. Next Steps  Implement development GUI to allow tuning of the detection values (by species)  Reference dB, salinity, temperature in addition to intensity to calculate distance  Additional challenges down to frequency of the echo location click as produced by each species – high frequencies don't tend to travel very far  Variations in the echo location click spread  Porpoise click is very narrow and directional from the melon and out in front of the animal  Add in the ability to check for low frequency species such as Fin Whales, Hump Back Whales etc.

27. Futures – Large Scale Ocean Energy Impact Analysis

28. IBM InfoSphere Streams directions WebSphere Business Events Existing business information Data in motion InfoSphere Warehouse IBM Mashup Hub 8BI Tools Streams Studio enhancements Video/audio analytics Text/unstructured analytics Streams Processing Language v2 Native XML support Runtime High Availability Security enhancements Unicode support Installation enhancements Adapters Cognos Now WebSphere MQ RSS feeds Mashup Hub WebSphere Business Events Oracle SQL Server Millions of events per second Millisecond Latency Cognos Front Office All statements regarding IBM's plans, directions, and intent are subject to change or withdrawal without notice. Any reliance on these statements are at the relying party's sole risk and will not create any liability or obligation for IBM.

29. InfoSphere Streams sessions TimeSessionTitleLocation Thursday May 20 10:45 AM - 11:35 AM 3666AInfoSphere Streams for Real Time Analytics in Financial Services Industry Marriott Park Hotel, Room 14 Friday May 21 09:00 AM – 09:50 AM 3661AInfoSphere Streams helps Stockholm build Ver 2.0 Traffic Control System Marriott Park Hotel, Room 13 Friday May 21 11:30 AM - 12:30 PM 3692AInfoSphere Streams at Marine Institute of Ireland: Deep Dive Marriott Park Hotel, IOD Mini Theatre 3 Wednesday 10AM - 6PM Thursday 10AM - 5PM Friday 9AM - 2PM Demo Room InfoSphere Streams DemonstrationsMarriott Park Hotel, IOD Demo Room Station 19 Wednesday 10:30 – 11:30 Thursday 12:30 – 13:00 Thursday 16:30 – 17:00 Mini Theater on Expo Floor InfoSphere Streams in Telco InfoSphere Streams Business Insight Leverage Warehouse, SPSS with Streams Marriott Park Hotel, InfoSphere Mini Theater Expo Floor