This repository contains the material for the SAP TechEd 2021 session DAT262 - Unlock Value and Gain Deep Insights from Your Data with SAP HANA Cloud.

The replay of the session held on Nov. 17th 2021 can be found on the TechEd 2021 website.

This session introduces attendees to the multi-model capabilities in SAP HANA Cloud. We will mainly work with data from the Automatic Identification System (AIS), which is basically observations in space and time. In specific, we will process AIS vessel data provided by https://marinecadastre.gov/ais/. Files covering two months of "Zone 16" (Chicago, Great Lakes) are imported into SAP HANA Cloud and Lon/Lat coordinates are converted to a "real" point geometry.
In exercises 2-4, the data is processed using standard SQL/spatial functions to

• check which vessels were crossing boundaries of a national park (2)
• derive motion statistics and trajectories of individual vessels (3)
• aggregate observations using spatial clustering techniques (4)

We will then calculate routes using graph capabilities (5). The exercise includes a simulated blockage of a ship canal and the identification of a suitable alternative route.
In exercise 6, we will show how to use an extension of spatial clustering to derive a "Space-Time Cube". The resulting dataset is then used to forecast traffic (7) on the lake using HANA's built-in "Predictive Analysis Library" (PAL).
Enterprise Search is the topic of exercise 8. We will create a search model, run some search queries, and see the search UI. Finally, Document Store is leveraged in exercise 9 to ingest data from GDELT's Global Entity Graph and subsequently create a graph workspace which we can use for visualization.

Most of the spatial and graph related exercises can be run on an SAP HANA Cloud trial instance, but for the PAL and JSON Document Store related capabilities you currently would need to work with a "full" SAP HANA Cloud system.

A copy of the demo data and all scripts are located in the data_and_script folder.

• Getting Started
  • Base Data & Demo Scenario
  • SAP HANA Cloud setup
  • DBeaver, QGIS, hana-ml, Cytoscape, and Enterprise Search
  • Background Material
• Exercise 1 - Prepare the Data
  • Exercise 1.1 - Import Data
  • Exercise 1.2 - Generate Geometries
  • Exercise 1.3 - Remove Duplicates
• Exercise 2 - Identify Vessels within National Park Boundaries
  • Exercise 2.1 - Use ST_Within(), ST_MakeLine(), and ST_CollectAggr()
• Exercise 3 - Understand Vessel Motion
  • Exercise 3.1 - Derive Speed, Acceleration, Total Distance, and Total Time
  • Exercise 3.2 - Vessel Trajectories
  • Exercise 3.3 - Dwell Locations and Trip Segments
• Exercise 4 - Spatial Clustering
• Exercise 5 - Vessel Routes
  • Exercise 5.1 - Generate a Network for Path Finding
  • Exercise 5.2 - Use Shortest Path with a Custom Cost Function
  • Exercise 5.3 - Simulate a Canal Blockage and Find Alternative Routes
• Exercise 6 - Spatio-Temporal Clustering
• Exercise 7 - Predict Traffic
  • Exercise 7.1 - Create Space-Time Cube
  • Exercise 7.2 - Timeseries Forecasting using Massive Auto Exponential Smoothing
• Exercise 8 - Enterprise Search
• Exercise 9 - Document Store and Graph
  • Exercise 9.1 - Load JSON data via Python Client for Machine Learning (hana-ml)
  • Exercise 9.2 - Generate a Graph and Visualize in Cytoscape

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Copyright (c) 2021 SAP SE or an SAP affiliate company. All rights reserved. This project is licensed under the Apache Software License, version 2.0 except as noted otherwise in the LICENSE file.