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Cloud-based service eases integration of geotag searches

Posted: 15 Oct 2015 ?? ?Print Version ?Bookmark and Share

Keywords:Geospock? geotag? cloud? database? big data?

Geospock, a Cambridge-based startup, has secured $4.02 million in Series A funding to bring its first product to market. According to the company, the cloud-based database service features simple-to-use API for developers to easily integrate real time geotag searches to their applications.

Nowadays, machines are generating data at a rate and scale beyond the volumes of human-generated data for which most data bases were originally architected, observes Steve Marsh, CEO and co-founder of GeoSpock.

"We are generating much more data than we are generating searches, and existing databases are starting to fail or lag behind in terms of real time accessibility," he said. "Eighty per cent of all data has a geotag attached to it, but often, the geolocation is rapidly changing and in order to be meaningful, it should be continuously updated and searchable in real time."

"Big data is slow data unless it is managed correctly. A new generation of applications use time and place to deliver a customer service. By combining this dynamic data with historical information in real time, companies are in a position to predict demand, manage services geographically and optimise their resources," noted Marsh, mentioning telematics or mapping services tied to social applications as obvious examples.

The startup has developed a very efficient multi-dimensional database that features simultaneous data read and write to increase throughput, and solves the 'heat map' problem of large amounts of transient, nearby data. It is specifically designed for the storage, search and retrieval of geospatial data in real-time no matter how big it gets or how often it changes.

"A single mobile tower can collect up to 100,000 geotags every second. If telecom companies could monitor every devices in real time, they could offer marketing insights to, say, a supermarket, tell them about which demographics entered the premises, where the customers came from and where they are heading to afterwards. Even as anonymised statistical data, this would be very valuable," Marsh said.

"Imagine tens of thousands of drones, all having to communicate their position and their sensor data and each having to search for the positions of the others to calculate their geospatial relation to each other for path finding," he added.

"A differentiator is that we can not only search for real time streaming data, but we can also correlate this to historical data to identify anomalous behaviours."

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