Mapping out the growth of location services on the cloud

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Jonathan Bnayahu
Editor’s note: This article is by Jonathan Bnayahu, Manager of Smart Decision Solutions at IBM Research – Haifa.
Location-aware technologies and their sensors in our mobile devices, connected cars, and environment, have become ubiquitous – and big business. Analyst firm Gartner expects Software as a Service (SaaS) and cloud-based business application services to reach $32.2B by 2016, and that revenue generated by consumer location-based services will reach $13.5B by next year. To take advantage of this growth, today’s smarter systems need to be able to sense, analyze, monitor, predict, and respond to space- and time-based situations.
To meet this challenge head-on, my team at IBM Research – Haifa is developing location services that can be incorporated into mobile apps or cloud services. These solutions embrace a comprehensive set of technologies that make it easier to build smart systems than can identify, predict, and take proactive actions in space-time situations.
Just a few examples of location queries these services can provide, includes: 
  • Get a list of the five technicians closest to a specific location
  • Get the last few minutes traveled by an entity
  • Alert when a customer is near the shop, alert when a visitor has left a group of people doing a tour of the premises
  • Get notification when all staff have arrived at the facility
  • Alert when the speed of a vehicle goes above a certain threshold
  • Alert when a vehicle, vessel or person deviates from the planned route
  • And more…
When a user who visited the website last week enters the area, he gets a tailored message based on his loyalty card

The visualization and mapping technologies we create as part of this solution are especially helpful in identifying spatio-temporal patterns and trends. One example is the visualization of maritime traffic on a map of the port. In a pilot with a port authority, we were able to help identify smuggling boats that were evading customs agents by discovering areas where singular vessels slowed their speed significantly in places that were not part of the typical routes taken.

In another pilot, we were able to use these technologies to help public transportation officials check the efficiency of their bus schedules. By mapping unscheduled stops buses make throughout the day and identifying encounters between buses serving different lines we provided city officials with data on specific problematic time slots and frequencies.
By delivering new cloud services that streamline development for mobile applications, our research is helping create more efficient enterprise apps and improve interaction with customers.
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