Product forecastingwebinar 20130417

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Kognitio Webinar: Showcasing the Data Scientist Lab functionality with External Scripting and how it can be used to run ‘R’ in an MPP environment

April 18, 8:00am pst, 11:00am est, 4pm bst, 5pm cest
Duration: 45mins plus Q&A
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Dr. Sharon Kirkham, Principal, Kognitio Analytics Center of Excellence, showcases the power of external scripting with a demonstration of the ‘R’ statistical language, running in the massively parallel Kognitio Analytical Platform environment.

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Product forecastingwebinar 20130417

  1. 1. Showcasing Data ScienceLab functionalityWelcome from Kognitiowww.kognitio.com
  2. 2. Today’s Web Seminar -Presenters HostMichael HiskeyVice PresidentMarketing & Business DevelopmentFormat &AgendaKeynote PresentersDr. Sharon KirkhamData ScientistKognitio Analytics Center of Excellence• Big Data and Complexity– the need for Data Scientists Question Break #1• Data Manipulation – functional demonstrationQuestion Break #2• Product forecasting with parallel R  ‐ practical demonstration Question Break # 3
  3. 3. KognitioKognitio is focused on providing the premier high‐performance analytical platform to power business insight around the world• Kognitio invented the in‐memory analytical platform, first taking it to market in 1989• Privately held• Labs in the UK ‐ HQ in New York, NY 
  4. 4. The Data Science LabDataScientists &StaffMathematicAlgorithmsMPPComputingBIG DATA11
  5. 5. What do business users want to do?Find patternsTrack lifetimejourneysPredictbehaviorForecastscenariosAllocatescarceresourcesModelvalueCharacterizegroupsVisualizediscoveryRespond,trigger,manage,promote
  6. 6. I’m a data scientist! Are you?Entry level skills and development - aspirationMachineLearningGraduates
  7. 7. I’m a data scientist! Are you?BusinessExpertiseMachineLearningInterpretationskills= InsightGraduatesNeedguidanceDataScientist
  8. 8. Supporting the data scientistTypical process – traditionally…Database
  9. 9. Supporting the data scientistTypical process – direct data preparationDatabaseSQL processing
  10. 10. Supporting the data scientistTypical process – produces analytical data setDatabaseSQL processingData Set
  11. 11. Supporting the data scientistTypical process – run analytics from serverDatabaseSQL processingData Set???
  12. 12. Supporting the data scientistTypical process – data samples often usedDatabaseSQL processingData Set???Data SamplesProcess runiteratively= slow
  13. 13. Supporting the data scientistTypical process – modelling process is honedDatabaseSQL processingData Set???Data SamplesProcess runiteratively= slow
  14. 14. Supporting the data scientistTypical process – model is completeDatabaseData Set???
  15. 15. Supporting the data scientistTypical process – score full data (Ouch!)DatabaseData Set???Full datato score
  16. 16. Supporting the data scientistPush processes to DB – still produce analytical data setAnalytical PlatformSQL processingData Set
  17. 17. Supporting the data scientistPush processes to DB – translate specific processesAnalytical PlatformSQL processingData Set???Translation
  18. 18. Supporting the data scientistPush processes to DB – results passed backAnalytical PlatformSQL processingData Set???TranslationResult Data Set
  19. 19. Supporting the data scientistPush processes to DB– modelling process is honedAnalytical PlatformSQL processingData Set???TranslationResult Data Set
  20. 20. Supporting the data scientistPush processes to DB– model scoring done in DBAnalytical PlatformSQL processingData Set???Result Data Set
  21. 21. Supporting the data scientistBut we always want more! Complex data structureAnalytical PlatformData Set???Result Data SetSQL cannot handleData complexity.How do I integrateinto my model?
  22. 22. Supporting the data scientistBut we always want more! non-standard processesDatabaseSQL processingData Set???Data Samples Back wherewe started
  23. 23. Supporting the data scientistBring Analytics to data – still produce analytical data setSQL processingSQL processing
  24. 24. Supporting the data scientistBring Analytics to data – can use other code for data prepSQL processingKognitio scriptingCode executedUsing MPPData held inMemory. Fastaccess to CPUs
  25. 25. Supporting the data scientistBring Analytics to data – run analytics natively in KognitioSQL processingKognitio scriptingCode executedUsing MPPData held inMemory. Fastaccess to CPUsOne platform flexible workingfrom data prep through analyticalprocess
  26. 26. New! Kognitio version 8:Enabling and extending the Analytical PlatformExternal TablesExternal FunctionsNot Only SQLHadoop Connector Other ConnectorsKognitio Storageas an External tableGeneral Availability:June 2013
  27. 27. External Scripting – Data TransformationConverting structured data intoXML format, i.e. furnishingpersonalised contentAssemblyConverting XML into structureddataDisassemblyExtracting complex informationfrom URLsPulling words from large text fields,i.e. sentiment analysisParsingConverting row based informationinto columns for data mining,i.e. supporting classification orsegmentationTranspositione.g. using perlExamples where SQL is typically complex and extensive
  28. 28. Data ManipulationSmall Demo
  29. 29. Product Forecasting – with parallel RForecastingRequirementsForecastInputs
  30. 30. R running in an MPP environmentPersistenceLayerAnalyticalPlatformLayer
  31. 31. R running in an MPP environmentPersistenceLayerAnalyticalPlatformLayerKognitioplatformspecification16 servers462GBKognitioRAM128 CoresThis is old kit2.9 billionrows ofepos184 day time seriesfor 12K products
  32. 32. R running in an MPP environmentPersistenceLayerAnalyticalPlatformLayer
  33. 33. R running in an MPP environmentPersistenceLayerAnalyticalPlatformLayer1 output tablein RAM128 parallelinstances of R
  34. 34. R running in an MPP environmentPersistenceLayerAnalyticalPlatformLayerApplication &Client LayerExcelAll BI Tools
  35. 35. R running in an MPP environmentPersistenceLayerAnalyticalPlatformLayerApplication &Client LayerExcelAll BI Tools13 views ofdifferent analyticaloutput
  36. 36. R running in an MPP environmentPersistenceLayerAnalyticalPlatformLayerApplication &Client LayerExcelAll BI ToolsResult setcontained# rows12K forecasts andstats calculatedin # seconds2.9B EPOS itemscollated intotime seriesin # seconds
  37. 37. Product Forecastingusing parallel R Demo
  38. 38. Thank you for your participation today• More information on today’s topic can be found at: • kognitio.com/mpp_r• kognitio.com/product‐forecasting• FREE TO USE – perpetual license– www.kognitio.com/free– Contact us for the pre‐release version 8• Analyst White Papers– EMA Comparative Analysis – In‐memory database platforms– www.kognitio.com/emacompinmem• Today’s slides (and more): www.slideshare.net/Kognitio
  39. 39. connectwww.kognitio.comtwitter.com/kognitiolinkedin.com/companies/kognitiotinyurl.com/kognitio youtube.com/kognitioNA: +1 855  KOGNITIOEMEA: +44 1344 300 770

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