Connected Industry and Enterprise Role of AI, IoT and Geospatial Technology. Vijay Kumar, CTO ESRI India
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1 Connected Industry and Enterprise Role of AI, IoT and Geospatial Technology Vijay Kumar, CTO ESRI India
2 Agenda: Understanding IoT IoT component and deployment patterns ArcGIS Geospatial Platform with IOT, Big Data and Machine Learning Way Forward
3 IoT overview What is the Internet of Things (IoT)? The network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment. Source: Gartner February 2016
4 IOT bringing geospatial insights to your Environment noise co2 nitrates gases temperature humidity Weather atmospheric pressure radiation warnings earthquakes pesticides electromagnetic feedback precipitation icy conditions rain gauges water level gauges water quality air quality Airports Public Safety flight status queues police fire plane location runway status surveillance Connected Cars autonomous driving traffic conditions holes parking meters road conditions slippery areas network improvements Internet of your Things Buildings lighting hvac occupancy counts Web Device Desktop ArcGIS Enterprise with real-time & big data capabilities Transit buses taxis rail trains crowds People health monitoring social activity Public Health hospitals ambulances Energy Usage electricity gas smart meters City Workers sanitation snow plows Telecommunications cell phone signals dropped calls
5 Blueprint for IoT solutions The Internet of Things (IoT) is an integrated solution that senses & collects data from devices at the edge, analyzes that data and takes action to accomplish the business goals of an enterprise. An IoT solution consists of the following layers: - Edge: Embedded technology at the edge that senses, acquires & disseminates data. - IoT platform: Accepts, ingests, stores, analyzes and shares intelligence extracted from the data. - Enterprise: Applications & processes that act upon the intelligence as a result of analytic results. Edge IoT Platform Enterprise
6 Environment Complementing an IoT platform with ArcGIS enabling geospatial insights with your IoT solution The Edge of an IoT broadcasts into an IoT platform such as: Azure IoT, Amazon IoT, Cisco IoT, IBM Bluemix, Hitachi,. The IoT platform integrates with ArcGIS to expand it s capabilities with spatiotemporal analytics, visualization & dashboards. Edge IoT Platform Enterprise Sensors Devices (or Things) Gateways ingestion Device Management Actuators management console Policy & Orchestration ingestion actions Ingestion Streaming Analytics GeoEvent Server analytics ArcGIS Enterprise policies & orchestration spatiotemporal big data store Data Store data store Actions & visualization Intelligence Batch Analytics GeoAnalytics Server analytics dashboards Operations Dashboard for ArcGIS Insights for ArcGIS Esri Story Maps ArcGIS Earth ArcGIS Online ArcGIS Pro Collector for ArcGIS Web AppBuilder for ArcGIS AppStudio for ArcGIS
7 ArcGIS as an IoT Platform ArcGIS Enterprise with real-time & big data capabilities Web Device Desktop Ingest high velocity real-time IoT data into ArcGIS. live features live & historic aggregates & features Perform continuous analytics on IoT events as they are received. Store IoT observations in a spatiotemporal big data store. stream service map & feature service Visualize high velocity & volume IoT data: - as an aggregation - or as discrete features. ingestion actuation visualization ArcGIS Enterprise Notify about IoT patterns of interest. Adjust behavior of things in our environment through actuation. GeoEvent Server analytics spatiotemporal big data store storage GeoAnalytics Server analytics
8 Machine Learning in ArcGIS Prediction Using known to estimate unknown Classification Object assignment Clustering Grouping of observations on similarities Empirical Bayesian Kriging, Areal Interpolation, EBK Regression Prediction, Ordinary Least Squares Regression Exploratory Regression, Geographically Weighted Regression Maximum Likelihood Classification, Random Trees, Support Vector Machine Spatially Constrained Multivariate Clustering, Multivariate Clustering, Density-based Clustering, Image Segmentation, Hot Spot Analysis, Cluster and Outlier Analysis, Space Time Pattern Mining
9 Key Trends and Learning Convergence of Geospatial, IOT, AI and Realtime Simple Map Visualization 3D and Spatio-Temporal Analytics Technology Maturity is helping in easy integration Business Problems driven use cases and solutions Emergence of Fully Managed Integrated IOT Platform and Services
10 Thanks
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