Everyday we see more and more mobile apps that leverage location services to provide context-aware functionalities. For example, location-based taxi apps, such as Uber, find you a ride with the touch of a bottom while you’re comfortably waiting inside a building. Gone is the time when you had to flag down a taxi on the curb of a busy street. Increasingly, location information is also used not only to detect where a person is, but also predict where that person might go at a given time of the day. Applications can actively track location data and compares the real-time data with the historic one in order to predict the potential destination, which can be used in countless business scenarios, ranging from staffing cashiers at toll plazas to marketing more relevant offers to customers. Moreover, Apple’s iBeacon location sensing technology can help applications to figure out the phone’s accurate location inside a building. Thus, museums, hospital, or campuses can use new geo-location apps to deliver better customized services. Additionally, location-based applications can allow business decision makers to take analytics out of the office, delivering location-based summaries with a geographic component within a BI dashboard.
I believe there is unlimited potential associated with location data. Sophisticated applications leveraging location data can change our life and transform businesses. Many of these location-based applications will increasingly leverage the advanced geo-spatial analysis capabilities. While a simple query on location data might look like: find McDonalds within 1km range from where I am, real business questions can be much more complex. Let’s look at CenterPoint Energy (CNP), a SAP innovation Award participant in 2014. CNP is a utility company serving five million residential and business customers. Analyzing customer utility usage allows them to understand the changes in usage and factor it in together with weather impacts, timing and location to optimize energy load and distribution. Due to technology limitation, in the past, this study was based on stratified random samples of residential, small commercial and all industrial customers, and only simple questions could be answered. With SAP HANA, they have built a new Forecasting Model Engine that works on much larger data sets and it, “correlate big data sets like 15 minutes interval data from smart meters (~5 billion records) with weather (1 year history), customer information (~2.3 million customers), and geographical location information to perform advanced analytics and predictive models more efficiently and accurately”. CNP also implemented a Weather Response Function (WRF) wizard that can validate estimates of weather impacts predicted against observed changes in hourly loads with geo-spatial data at real-time. For the complete story about Center Point Entergy’s innovations, you can find Innovation Award submission here
, blog here
, and video here
Leveraging geospatial data to provide real-time analytics is just a starting point. We could expect even more creative applications to use location data. SAP HANA is the best platform to build these applications. Some of the reasons come from SAP HANA unique architecture and the in-memory technology to accelerate your application fundamentally that allows fast location data processing. Huge amount of location data can be stored and processed in-memory and right inside the database (no need to move the data to an application server). Additionally SAP HANA’s columnar store and specialized processing engine eliminate the need for spatial indexes or tessellation technology. Your application presentation layer just needs to retrieve the processed results from the database and visualize them. But there is more that HANA can do for application that leverage geospatial data:
1. SAP HANA geospatial analytic capabilities are very broad and can be easily leveraged through standard SQL extensions. These extension implement ISO/IEC 13249-3 standard and open geospatial consortium SQL standard. Specifically, SAP HANA supports spatial vector data to describe measurement (distance, surface, area, perimeter, volume), relationship (intersects, contains, within, adjacent, touches), operators (buffer, transform), or attributes (geometry data type, such as Line, Polygon, or Point). It also supports GeoJson and technologies to extract geometrical data structure like Well Known Text (WKT) and Well Known Binary (WKB). SAP HANA also supports ESRI shape file format. Those common features are necessary to support advanced geospatial analysis.
2. SAP HANA spatial engine can integrate with other HANA engines like Predictive, Planning, or Text Search etc. to apply complex algorithms on combined data set, deriving deeper insights. This means that SAP HANA allows spatial & non-spatial data to be stored and processed within the same database, so that you can develop sophisticated applications that enrich location-based data and correlate it with other historic and transactional data.
3. With SAP HANA, you can combine the power of ESRI ArcGIS suite with the native SAP HANA spatial in-memory processing to achieve high-performing location analytic. SAP HANA simplifies the integration by connecting directly to the process layers from ESri ArcGIS tools.
4. SAP HANA embed geo-content, services maps, and geospatial services for seamless application development and deployment. You can build new geospatial apps easily using SAP HANA XS engine calling Nokia mapping services through the Nokia API.
Additionally, please have a look at this blog
and this demo
with Nokia & ESRI maps for more information on how to develop application that leverage SAP HANA geospatial capabilities.
Blog 4: Develop SAP HANA Application to integrate IT and OT Data
Blog 3: SAP HANA Helps Build Modern Applications that Combine Core Business Data with External Data
Blog 2: Business Drivers and Requirements for Custom Application Development on SAP HANA Platform
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