CISCO CAD AUTOANSWER SOFTWARE
PTC has been expanding from its product design and CAD software background, adding product lifecycle management (PLM) tools and then buying the ThingWorx application platform at then end of 2013, Axeda’s device connectivity software in 2014, and Kepware’s industrial automation software in 2015. Rounding out the trio of announcements was PTC, which used its LiveWorx show as the platform to announce an expansion of its IoT portfolio, with the release of a pre-integrated stack for industrial edge computing. It also claims to be much more memory efficient than software using JavaScript engines based on node.js or PhantomJS (JSCore). The gateway agent also offers security and firmware OTA updates, but will also allow developers to use the software to process the data it collects at the edge, to reduce the network load of sending it back to a centralized cloud deployment for processing.
However, the agent is something that could be run on the smart TVs and set top boxes that Access already has a presence inside too.
CISCO CAD AUTOANSWER TV
Access has primarily designed the technology for consumer electronics, industrial, and business device manufacturers – and sees it being adopted in utility deployments, telecoms, cable and pay TV operators, systems integrators, and home construction.Ĭoming from its background in TV and automotive software, where Access’ NetFront browser is deployed in some 1.5bn devices, the IoT is a new opportunity for Access. The agent bridges sensors and other I/O options to the web, collecting the data they generate in deployments such as plants, offices, and hotels. This is a direction that Cisco has been making some serious moves in, promising an M&A directive that saw it acquire ParStream, a company that specialized in big data analytics on small computers at the network edge.Īway from the technology giants, software company Access has also announced an edge-computing product called Access NetFront Agent – aimed at IoT gateways, and claiming to be the smallest managed edge computing engine, for linking sensors using IoT communication protocols to the internet. Using a delta and deviation approach would be significantly cheaper.
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In another manner, if that monitoring system were to collect hourly or weekly snapshots, there will come a point where that archive begins costing more to store and process than the cursory value of knowing what RPM a turbine was spinning at on Wednesday, January the 6 th, 2016, at 16:49. In a monitoring solution, a critical error that requires a machine to shut down to prevent damage is useless if the monitoring system can only act on the warning after the point at which it should have shutdown.
The response time is key to the value of the data, of course. With historical data on hand, and the smaller amount of streamed data, the pair believe that the tech can provide sub-second response times. The pair point to factories as prime examples of the customers that would be interested in the new product, as well as remote oil rigs and mines. With IBM, Cisco is looking to leverage the analytics power of Watson to improve the performance of the devices it sells to applications at the network edge – which will be very different to the servers and switches it typically sells to datacenter applications. It’s hard to be as enthusiastic about the news as the presenting staffers were.Ĭisco’s term for this emerging form of edge-computing is Fog computing – etymologically linked to Cloud computing because the fog process is taking place much closer to the source (the ground) than the cloud.
The first announcement came from Cisco and IBM, and was somewhat painful to watch on the livestream – not for lack of trying from the production team.
Doing so would greatly reduce the volume of data that needs to be sent back to the cloud too – useful for applications in locations where a robust and fast enough internet connection is hard to come by. It’s a vibrant market, that is slowly realizing that the volume of data generated by IoT devices can’t be backhauled to the cloud for analysis every time.īy processing data at the edge, application latency could be vastly improved, as long as there is sufficient computational power to ingest the various data points and spit out the correct answer. This week has seen at least three announcements from software players looking to take control of IoT data and analytics at the network edge.
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