Fog Computing, IoT, and Big Data Analytics

Fog Computing is a distributed computing infrastructure in which some application services are controlled at the network edge in a smart device and some application services are controlled in a remote data center (e.g. a centralized Cloud).

Due to this new architecture approach, the need to handle data differently, and the sheer volume of unstructured data, there will be great opportunities for Big Data in IoT.   Big Data and Analytics software and applications will play a crucial role in making IoT a success. The data generated through sensors embedded in various things/objects will generate massive amounts of unstructured (big) data on real-time basis that holds the promise for intelligence and insights for dramatically improved decision processes.

However, Big Data in IoT is different than conventional data systems and thus will require more robust, agile and scalable platforms, analytics tools, and data storage systems than conventional Big Data infrastructure.


Real-time and distributed unstructured (Big) data processing will become increasingly important, especially with the anticipated accelerations of IoT networks and applications.  This is because there will be many distributed points of data access such as sensors, wearable technology, and various other consumer and industrial devices.

There is a close association here with a hybrid cloud network topology in which some data is passed to a more centralized storage and processing area while other data is processed locally in an edge computing manner (e.g. Fog Computing).

More than 50% of enterprise IT organizations are experimenting with Artificial Intelligence (AI) in various forms such as Machine Learning, Deep Learning, Computer Vision, Image Recognition, Voice Recognition, Artificial Neural Networks, and more. AI is not a single technology but a convergence of various technologies, statistical models, algorithms, and approaches. Machine Learning is a sub-field of computer science that evolved from the study of pattern recognition and computational learning theory in AI.

Artificial Intelligence and Machine Learning in Big Data and IoT: The Market for Data Capture, Analytics, and Decision Making 2016 – 2021 evaluates various AI technologies and their use relative to analytics solutions within the rapidly growing enterprise data arena. The report assesses emerging business models, leading companies, and solutions. The report also provides forecasting for unit growth and revenue from 2016 – 2021 associated with AI supported predictive analytics solutions.

About Mind Commerce

Analysis of telecom and ICT infrastructure, technologies, and applications.
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