Between the Poles

Oracle Spatial and Graph to support NURBS

At the Oracle Spatial and Graph User Meeting, the folks from Oracle outlined some of the things to expect in the next version of Oracle 12c.  I was very impressed.  All of the below is my interpretation of what will be in Oracle 12c.

Parametric curve support

The really big news for the architecture, engineering and construction community (AEC) who use CAD and BIM applications is that Oracle 12c is expected to support NURBS (non-uniform rational basis spline) a type of parametric curve widely used in the design space.  According the Siva Ravada the support will be very general and will be able to handle different ways of representing NURBS. Up to now the only parametric curve supported by Oracle was circular arcs.

To me it looks like Oracle is taking a major step toward being able to store natively and manipulate (in some cases by stroking) the parametrized curves used by CAD and BIM applications.  There is a lot of CAD and BIM data out there and being able to store it and manipulate it in Oracle Spatial and Graph is going to be a game changer.

Orthorectification

Oracle is providing support for orthorectification embedded directly in the database.  This will have important implications for the industry.   Up to now, you had to pull the image out of where ever it was stored, transfer it over the network to someone's application often from an image processing company to orthorectify it, and then transfer it back to store the image or stream it to the end user.  With 12c you will be able to orthorectify images in the database.  This is in line with Oracle's objective of bringing processing to the data, rather than the data to processing.

Raster algebra

Oracle 12c will also have support for raster algebra, again in the database.  For example, you will be able to average in the database12 months of national temperature data stored in monthly raster files.

GDAL and PDAL support

Oracle supports the open source GDAL raster libraries, the industry standard developed by Frank Warmerdam (now with Google) and used by just about everyone for accessing raster images including ESRI.  PDAL is a similar type of open source library for point clouds developed by Howard Butler and Michael Gerlek.

3D

Oracle supports three types of 3D data.  3D vectors, point clouds and terrain (surfaces).  Oracle 12c will provide full support for 3D geodetic data, lon lat and elevation in meters or feet.

Performance

Oracle has developed a new engine for handling massive point clouds.  Dan Geringer showed impressive results for a 2.8 billion point point cloud (286 gigabyte file). 

Processing vector geospatial data is reported to be 50 to 100 times faster in 12c than in 11g. 

Raster operations have been parallelized which can dramatically reduce the time it takes for these operations.

Engineered Database

Since acquiring Sun, Oracle has become a hardware and software company and is offering integrated hardware and software solutions that offer extremely good performance characteristics.  For example, processing LiDAR data benefits from hardware acceleration on engineered machines such as Exadata boxes.

Bundled data

Oracle is planning to bundle vector data from Nokia (Navteq), TomTom and others.

Oracle Spatial and Graph

The name of the product is changing to Oracle Spatial and Graph partly because it has had graph capabilities (network and RDF semantic) already for several years.  But probably more importantly because the demand for graph capabilities is accelerating, especially RDF semantic graphs for linked data, text mining, and for social media analytics, according to Xavier Lopez.  Even Oracle's NOSQL database (aka BerkeleyDB) is apparently getting graph capabilities.

Location-enabled Society: Mapping tweets using real-time big data technology and privacy

In a very appropriately titled session Taking Society's Pulse in Real-time at the Creating the Policy and Legal Framework for a Location-enabled Society conference in Boston, one item really got people's attention focussed on the issues that arise from combining location with personal data. TweetMap is a technology that visualizes a large number of tweets on a map of the world. This is not the first time I have seen tweets mapped.  But what really got people's attention is that a speaker showed how to drill down to your neighbourhood and see what the people next door are tweeting about.  Of course you can do this without a map by simply following your neighbors on Twitter.  But somehow mapping it makes it seem more intrusive, even invasive to some people. In another demonstration, Tweetmap was used to show how a person sailing in the Caribbean could be tracked as they tweeted from their boat.

To be tracked geospatially on Twitter requires that you explicitly agree to geolocation tracking for Twitter on your iPhone or Android device.  For example, on an iPhone the geolocation option allows you to make your location known to others. Apps such as Facebook or Twitter for iPhone can report your location as well as the messages you post.  The iPhone requires that these applications obtain your permission before they can be enabled.  Apparently only about 2-4% of smart phone users, mostly younger folks, agree to this.  After seeing a Tweetmap drill down, I would expect that some people might have second thoughts about agreeing to the gelocation option. And in the not too distant future this will be possible in real-time on inexpensive hardware with open souce software.

TweetMap is an instance of MapD, a massively parallel database platform being developed through a collaboration between Todd Mostak, (a researcher at MIT), and the Harvard Center for Geographic Analysis (CGA).  Currently the tweet database starts on 12/10/2012 and ends on 12/31/2012 and contains 125 million tweets.  The database can be queried by time, space, and keyword.  The ultimate objective is to be able to query billions of tweets live in real-time.  In this case real-time means from tweet to tweet on a map in under a second.

MapD is a general purpose SQL database that can be used to provide real-time visualization and analysis of very large data sets.  MapD uses commodity Graphic Processing Units (GPUs) to parallelize compute intensive tasks such as querying and rendering very large data sets on-the-fly.  MapD runs on inexpensive hardware ( ~$1000) and is intended to be open source.

Utilities need to develop a social media strategy

I have blogged before about the use of social media by utilties to communicate with the public.  Colorado Springs Utilties used Facebook during the Colorado forest fires to keep people apprised of the status of the fires.  As another example, Southern California Edison (@socaledison) uses Twitter to report extreme weather alerts, outages, and restoration progress.

At the EDIST 2013 Conference in Toronto there were two presentation that focussed on social media, primarily for customer interaction management.  But utilities are just beginning scratch the surface in this area, so the talks were intended to make the audience aware of just how pervasive social media has become.

Facebook
FB has a billion users. If it were a country, FB would be the third largest. Half of the users use mobile devices.  The average user spends 5 hours a week on FB.  FB supports 70 languages.  FB users used to be primarily a younger crowd, but the older age groups are now growing faster.  There are about an equal number of male and female users.  For the Canadians out there, apparently Tim Hortons has 2 million "friends" on FB.

Twitter
140 million active users, 55% of them women. 41% of users use smartphones to tweet.

Youtube
Youtube has become the 2nd largest search engine, after Google.

Pinterest
25 million users, 72% of them women. 

These are very  impressive statistics, and the clear message is that in this time of the smart grid and in that context the much greater importance of the customer relationship utilities need to prioritize the development of a social media strategy.