Computer Solutions


4 The ‘Internet of Things’ and ‘Big Data’ hold the promise of one big interconnected world that will allow people to harmonise with machines and gadgets. Boris Sedacca investigates whether it is all vapourware or whether there are real life examples in practice.


4 L’«Internet des choses» et les «grandes données» promettent un grand monde interconnecté, qui permettra aux gens de s’harmoniser avec les machines et les gadgets. Boris Sedacca enquête pour savoir s’il s’agit d’un effet d’annonce ou s’il existe des exemples concrets dans la vie réelle.


4 Das „Internet der Dinge” und „Big Data” versprechen eine große verbundene Welt, die es den Menschen ermöglicht, mit Maschinen und Geräten zu harmonisieren. Boris Sedacca prüft, ob dies alles nur heiße Luft ist oder ob es in der Praxis auch Beispiele aus dem echten Leben gibt.


When the Internet of Things meets Big Data


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anufacturing has been transformed by the size and complexity of machines on the factory floor today, swathed with sensors that


gather voluminous data to keep them ticking over. Aside from internal machine control purposes or legal requirements to gather data in industries like pharmaceuticals, captured data can also be analysed to predict failures for example. Big data needs huge storage and


supercomputers to be effective. Andrew Dean, pre-Sales manager at OCF, has been involved with data storage integration on the University of Edinburgh’s High End Computing Terascale Resources (HECToR) Cray XE6 supercomputer. This holds eight petabytes of data with another 20 petabytes of disk backup and 92 petabytes of tape for less frequently used files. HECToR is funded by the Engineering


and Physical Sciences Research Council (EPSRC), and the Natural Environment Research Council (NERC). Scientists currently store highly complex simulations on site at Edinburgh. OCF supplies data storage and processing to the automotive, aerospace, manufacturing, oil & gas and pharmaceutical industries.


“We use IBM’s super-fast General Parallel File System (GPFS software which allows us to combine multiple storage arrays into one file system, using four large DataDirect Networks (DDN) clusters,” reveals Dean. High performance computing is where


Big Data happened before it was called Big Data, according to Dr James Coomer, Senior Technical Advisor at DDN, where for years it has been quite normal to think in terms of petabytes of storage at speeds of hundreds of gigabytes per second. “In oil and gas industries, one of the


biggest issues is gathering seismic data during exploration in deserts and oceans, where hundreds or thousands of ultrasound beacons and sensors gather huge amounts of sub- surface data,” Coomer exemplifies.


Data de-convolution


“This data needs to be de-convoluted and that requires two features of Big Data - huge data ingest rates from the sensors and number crunching to map sections of land or oceans, in the latter case with sensors trailing like serpents from ships. Some sensor data may have originated in analogue or digital form, but by the time we see it, it is all digitised. “Also in aerospace engine testing or


Formula 1 car design crash testing, both of which are very expensive, running simulations in advance in the latter case reduces the expense of a large number of cars physically having to be crashed. Modelling accuracy is extremely high now in areas like fluid dynamics around car bodies and wheels. “In aero engine design, Big


Data provides complex modelling of combustion rates and engine noise. Hundreds of engineers and CAD designers can produce structural geometry data using software like LS-Dyna, a popular finite element analysis (FEA)


Fig. 1. The University of Edinburgh’s HECToR facility uses a Cray XE6 supercomputer.


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