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Phoenix Data Conference - Big Data Analytics for IoT 11/4/17


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“Big Data for IoT: Analytics from Descriptive to Predictive to Prescriptive” was presented to the Phoenix Data Conference on 11/4/17 at Grand Canyon University.

As the Internet of Things (IoT) floods data lakes and fills data oceans with sensor and real-world data, analytic tools and real-time responsiveness will require improved platforms and applications to deal with the data flow and move from descriptive to predictive to prescriptive analysis and outcomes.

Published in: Technology

Phoenix Data Conference - Big Data Analytics for IoT 11/4/17

  1. 1. Big Data for IoT: Analytics from Descriptive to Predictive to Prescriptive Saturday, November 4, 2017 at GCU Mark Goldstein, International Research Center PO Box 825, Tempe, AZ 85280-0825, Phone: 602-470-0389,, URL: Presentation Available at © 2017 - International Research Center Arizona Chapter
  2. 2. IoT Overview and Ecosystems
  3. 3. See also: Internet of Things Innovations & Megatrends Presentation to the IEEE Computer Society Phoenix On December 14, 2016 at IoT Overview and Ecosystems IoT Computing Platforms and Sensors IoT Gateway and Network Connections IoT Application Arenas • Consumer and Home Automation • Wearables • Healthcare and Life Science • Retail and Logistics • Industrial • Smart Buildings • Smart Cities and Environment • Transportation IoT Security and Privacy IoT Standards and Organizations IoT Data Applications and Business Models Next mega IoT update scheduled for 12/13/17 at DeVry University Phoenix for the IEEE Computer Society Phoenix (
  4. 4. Source: Teradata Corporation Internet of Things Basics
  5. 5. Source: Postscapes (
  6. 6. Sensor Cluster Trends for Mobile Phones (Inertial Measurement Units) AMS AV-MLV-P2 is a volatile organic compounds (VOC) gas sensor which can detect alcohols, aldehydes, ketones, organic acids, amines, aliphatic and aromatic hydrocarbons.
  7. 7. Source: Postscapes (
  8. 8. IoT Technology Data Rate and Range Needs Source: Rohde & Schwartz
  9. 9. Wi-Fi Ecosystem is Undergoing Change
  10. 10. Why the Present 802.11 Technology is Inadequate: • Absence of power-saving mechanisms: The energy constraints of sensor networks are not considered in the current IEEE 802.11 standard. • Unsuitable bands: Due to their short wireless range and high obstruction losses, existing Wi-Fi bands require the use of intermediate nodes, adding complexity to the network. IEEE 802.11ah Requirements to Support M2M Communications: • Up to 8,191 devices associated with an access point (AP) through a hierarchical identifier structure • Carrier frequencies of approximately 900 MHz (license-exempt) that are less congested and guarantee a long range • Transmission range up to 1 km in outdoor areas • Data rates of at least 100 kbps • One-hop network topologies • Short and infrequent data transmissions (data packet size approximately 100 bytes and packet inter-arrival time greater than 30 s) • Very low energy consumption by adopting power saving strategies • Cost-effective solution for network device manufacturers IEEE 802.11ah Wi-Fi Approach for M2M Communications
  11. 11. Potential 5G Services Bandwidth & Latency Requirements Source: GMSA, Heavy Reading
  12. 12. Source: Postscapes (
  13. 13.
  14. 14. Source: IDC & Peplink 2015 IoT Vision
  15. 15. Source: TE Connectivity IoT Adoption Landscape
  16. 16.
  17. 17. Source: IEEE Spectrum 10/16
  18. 18. IoT Solutions Architecture Source: TechBeacon (
  19. 19. Internet of Things Solutions Framework
  20. 20. Future X Network Enabling a New Digital Era Source: Bell Labs Consulting
  21. 21. Big Data Overview
  22. 22. Source: HP
  23. 23. Why the Internet of Things Matters
  24. 24. The Data-Value Pyramid Source: Russell Jurney/Data Syndrome 2017
  25. 25. Source: LNS Research
  26. 26. Source: HP
  27. 27. Internet of Things Data Value Chain Source: Navigant Research
  28. 28. Elements of a Cognitive/AI Software Platform
  29. 29. Source: LNS Research
  30. 30. Adoption Across the Analytics Spectrum
  31. 31. IoT Data Platforms, Tools, and Big Data Analytics
  32. 32. Predix delivers the industrial intelligence you need to transform your operations and generate new revenues. Combining sophisticated asset modeling, big data processing, analytics, and applications, Predix provides the IT foundation for tomorrow’s industrial operations. Predix lets you deploy processing and analytics power to control edge assets in real time or analyze big data in the cloud using the secure Predix connectivity and execution environment.
  33. 33.
  34. 34. Microsoft Azure IoT Suite Overview Source: VDC Research
  35. 35. CNTK can be included as a library in your Python, C#, or C++ programs, or used as a standalone machine learning tool through its own model description language (BrainScript). In addition you can use the CNTK model evaluation functionality from your Java program. CNTK supports 64-bit Linux or 64-bit Windows operating systems. To install you can either choose pre-compiled binary packages, or compile the toolkit from the source provided in GitHub.
  36. 36. AWS IoT Architecture Source: VDC Research
  37. 37. AT&T Dedicated IoT Starter Kit for Amazon Web Services
  38. 38. and-microsoft-announce-gluon-making- deep-learning-accessible-to-all-developers/
  39. 39. IBM Watson’s System Architecture
  40. 40. data-platform-help-unleash-ai-professionals-scn/
  41. 41. IBM Watson Analytics Editions and Pricing
  42. 42. The Analytics Solutions Stack Source: Intel Corporation 2017
  43. 43. IoT Challenges and Cisco Jasper/Tele2 Solutions
  44. 44. Google’s Serverless Cloud IoT platform Google Cloud IoT is a comprehensive set of fully managed and integrated services that allow you to easily and securely connect, manage, and ingest IoT data from globally dispersed devices at a large scale, process and analyze/visualize that data in real time, and implement operational changes and take actions as needed. Device data captured by Cloud IoT Core gets published to Cloud Pub/Sub for downstream analytics. You can do ad hoc analysis using Google BigQuery, easily run advanced analytics and apply machine learning with Cloud Machine Learning Engine, or visualize IoT data results with rich reports and dashboards in Google Data Studio.
  45. 45. From Data Warehouses to Data Lakes: Mission-critical information is quickly moving from databases to data lakes, from structured to unstructured data and from millions of transactions to billions of interactions. Qubole can help you transition from a legacy, on-premises data warehouse to an elastic, open source data lake in the cloud.
  46. 46.
  47. 47. Tealium’s Universal Data Hub Source: Tealium 2017 (
  48. 48. TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow™ was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. TensorFlow™ has APIs available in several languages both for constructing and executing a TensorFlow graph. The Python API is at present the most complete and the easiest to use, but other language APIs may be easier to integrate into projects and may offer some performance advantages in graph execution.
  49. 49. Big Data Infrastructure Priorities
  50. 50. Data Analytics Market Challenges and Innovations
  51. 51. Data Analytics Key Vendors and Products
  52. 52. IDC’s Cognitive Systems Ecosystem
  53. 53. IoT Big Data Wrapup
  54. 54. Source: MIT Sloan
  55. 55. Source: CompTIA
  56. 56. Modern BI and Analytics Platforms
  57. 57. Internet of Things (IoT) Maturity Mode Source: TDWI
  58. 58. The Five Levels of Analytics Maturity Source: Logi Analytics
  59. 59. Advanced Analytics Maturity Path Source: Intel Corporation 2017
  60. 60. Big Data and Analytics MaturityScape Source: IDC 2015
  61. 61. Source: Gartner (July 2017) Emerging Technologies Hype Cycle
  62. 62. Big Data for IoT: Analytics from Descriptive to Predictive to Prescriptive Saturday, November 4, 2017 at GCU Mark Goldstein, International Research Center PO Box 825, Tempe, AZ 85280-0825, Phone: 602-470-0389,, URL: Presentation Available at © 2017 - International Research Center Arizona Chapter