The availability of ever-increasing amounts of structured and unstructured data, along with higher, cheaper and faster computing power, has been essential for the development of advanced analytics. The Internet of Things (IoT) has also boosted the development of the streaming analytics market through sensor-driven, real-time insights into consumer behavior.

It is more critical than ever to infuse innovation into every aspect of the supply chain, as edge technologies such as the Internet of Things (IoT) and artificial intelligence (AI) are essential to digitally transforming supply chains.

Users of the new platform upload data about customers or other individuals, such as records of mobile phone calls, credit card purchases, or web activity. They use Endor’s “query-builder” wizard to ask questions, such as “Where should we open our next store?” or “Who is likely to try product X?” Using the questions, the platform identifies patterns of previous behavior among the data and uses social physics models to predict future behavior. The platform can also analyze fully encrypted data-streams, allowing customers such as banks or credit card operators to maintain data privacy.

We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query.

In this paper, we define the concept of a Query Controller ( QC ) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering.

The new solution seamlessly integrates Huawei's Edge Computing IoT (EC-IoT) with GE's Industrial Internet cloud platform Predix to enable rapid end-to-end connectivity between industrial assets and cloud applications, allowing real-time machine health monitoring, data analysis and perception, and smart maintenance decision-making.

Plenarium Inc. announces the formation and launch of a new company delivering predictive talent and other advanced analytics solutions, resulting in measurable "return on talent." Plenarium has assembled a team of executives and scientists with extensive experience in behavioral science, technology, and business, as well as experts in entrepreneurial and operational management.

NEXTracker announced it has launched an innovative solar plus energy-storage solution for U.S. and international markets. NX Fusion Plus integrates the latest, best-in-class solar tracker, battery, inverter, and software to deliver better return on investment to owners of solar power plants. By incorporating battery storage technology into its product, NEXTracker is further increasing the energy output and duration of solar power plants, just as tracking technology did for fixed-tilt solar applications.

The Australian precision agriculture market accounted for approximately 7.2 percent of the global market share in 2015 and is forecast to increase its share over the next 5 years. Adoption of remote monitoring, Big Data, variable rate technology and farm management software will also drive the precision agriculture market.

The WWW is rich with data feeds - social media, internet of things (IoT), surveillance, primary surveys, biometrics, spatial imagery - however, we consume a very tiny fraction of this massive information mine to determine outcomes.

In Europe, IoT connections are set to exceed 9 billion by 2021. Dedicated venture funds for IoT development will help create an ecosystem that is conducive to the growth of startups in the region.

Find & Pay, uses big data, predictive analytics and machine learning to reduce the time it takes to find parking in congested cities by up to 50 per cent, reducing stress for motorists and the congestion and emissions associated with city parking today.

This paper aims at providing a proposed neural network solution that can be used to predict the future sales on the base of historical data using Back Propagation algorithm. Especially we aim at finding which automobile company will be on a high scale of sales as per the customer’s capacity and desire. This forecasting helps firms to take strategic decisions to achieve their goals.

Smart sensors are now evolving to be prognostic/ predictive. With up-gradation of contact, non-contact technologies and evolution of hybrid technologies, sensors are proactively enabling new applications. Sensors are at the forefront of digital transformation across diverse industrial markets.