Notes about Cutting-Edge Technologies and Everything

Datanami  is pleased to announce the results of its fourth annual Readers’ and Editors’ Choice Awards, which recognizes the companies, products, and projects that have made a difference in the big data community this year. These awards, which are nominated and voted on by Datanami readers, give us insight into the state of the community. We’d like to thank our dedicated readers for weighing in on their top picks for the best in big data. It’s been a privilege for us to present these awards, and we extend our congratulations to this year’s winners. Best Big Data Product or Technology: Machine Learning Readers’ Choice: Elastic Editor’s Choice: SAS Visual Data Mining & Machine Learning Best Big Data Product or Technology: Internet of Things Readers’ Choice: SAS Analytics for IoT Editor’s Choice:  The Striim Platform Best Big Data Product or Technology: Big Data Security Readers’ Choice: Cloudera Enterprise Editor’s Choice: Elastic Stack Best Big ...

With the challenge of the vast amount of data generated by devices at the edge of networks, new architecture needs a well-established data service model that accounts for privacy concerns. This paper presents an architecture of data transmission and a data portfolio with privacy for fog-to-cloud (DPPforF2C). We would like to propose a practical data model with privacy from a digitalized information perspective at fog nodes. In addition, we also propose an architecture for implicating the privacy of DPPforF2C used in fog computing. Technically, we design a data portfolio based on the Message Queuing Telemetry Transport (MQTT) and the Advanced Message Queuing Protocol (AMQP). We aim to propose sample data models with privacy architecture because there are some differences in the data obtained from 10T devices and sensors. Thus, we propose an architecture with the privacy of DPPforF2C for publishing data from edge devices to fog and to cloud servers that could be applied to fog architectu...

The AGILE-IoT project ( www.agile-iot.eu ), co-funded by the Horizon 2020 programme of the European Union, aims to address this concern, by providing a solution based on four main pillars: Agnosticity : depending on the technical background of the platform user, the background of his organisation or the software components he has to (re-) use, we cannot predict what would be the programming language of the solution. It might be built with a combination of languages, some of them being compiled (e.g., C, C++), others translated into intermediate languages (e.g., Java, Python) and, finally, some others interpreted (e.g., JavaScript). If the user chooses one platform because of the programming language(s) it supports, he may limit his options for developing his solution. AGILE-IoT, by leveraging a micro-service-based architecture, supports all the programming languages a platform user might require to implement their solution. Openness : Lots of platforms are provided by ...

By 2020, the Internet of Things (IoT) is predicted to generate an additional $344B in revenues, as well as to drive $177B in cost reductions. IoT and smart devices are already increasing performance metrics of major US-based factories. They are in the hands of employees, covering routine management issues and boosting their productivity by 40-60% [1]. The following list of predictions (Figure 1) explores the state of IoT in 2019 and covering IoT impact on many aspects business and technology including Digital Transformation, Blockchain, AI, and 5G. Read full article >>>

Because data is the most valuable resource in the digital business era, collecting it using only a centralized management approach is no longer viable. Data and analytics leaders need to take an aggressive approach that creates an appropriate balance between data collection and data connection. Key Challenges Data is distributed between cloud and premises, and hybrid deployments are becoming the default approach. The scale and pace of creation of data, as well as the need to harness it in real time, make it impossible to always collect data and then process it for a single value proposition or use case. As organizations prioritize operational efficiency and analytics, these two forces are making organizations rethink their data management strategies and investments. Data governance and regulatory requirements need to span all use cases and data distribution is further challenging centralized data governance approaches. Deploying different data management ...