Smart Building Initiatives are the Building Blocks of a Smart City

GUEST WRITER | April 8, 2020

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To paraphrase a well-known saying, the journey to a complete smart city begins with a single building. No matter the size of the city, the extent of the technology or the most helpful use cases, a prospective smart city can integrate into — or branch off of — initiatives pushed forward by a smart building or campus. And when there is an increasing demand for these types of solutions, large corporations have the opportunity to improve corporate and social governance practices, as well as stand out in their community by championing more connected technologies.

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Qubole delivers a Self-Service Platform for Big Data Analytics built on Amazon Web Services, Microsoft and Google Clouds. We were started by the team that built and ran Facebook's Data Service when they founded and authored Apache Hive. With Qubole, a data scientist can now spin up hundreds of clusters on their public cloud of choice and begin creating ad hoc and/or batch queries in under five minutes and have the system autoscale to the optimal compute levels as needed. Please feel free to test Qubole Data Services for yourself by clicking "Free Trial"​ on the website.

OTHER ARTICLES

WISeKey Drives Innovations in IoT Security with 23 Strategic Patents in the US

Article | February 18, 2020

WISeKey International Holding Ltd., cybersecurity delivering Integrated Security Platforms, announced that it has registered a total of 23 new strategic patents in US which are essential to the digital transformation applications that are fueling the growth in the IoT market.With a rich portfolio of more than 46 patent families, covering over 100 fundamental individual patents, and another 22 patents under review, WISeKey continues to expand its technology footprint in various domains including the design of secure chips, near field communication (NFC), the development of security firmware and backend software, the secure management of data, the improvement of security protocols between connected objects and advanced cryptography.

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DEPLOYING MACHINE LEARNING TO HANDLE INFLUX OF IOT DATA

Article | February 18, 2020

The Internet of Things is gradually penetrating every aspect of our lives. With the growth in numbers of internet-connected sensors built into cars, planes, trains, and buildings, we can say it is everywhere. Be it smart thermostats or smart coffee makers, IoT devices are marching ahead into mainstream adoption. But, these devices are far from perfect. Currently, there is a lot of manual input required to achieve optimal functionality — there is not a lot of intelligence built-in. You must set your alarm, tell your coffee maker when to start brewing, and manually set schedules for your thermostat, all independently and precisely.

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Four ways to ensure IoT success

Article | April 15, 2020

Three out of four IoT projects are considered a failure, according to Cisco. This is troubling but even more so when Cisco also found 61 per cent of companies say they believe they’ve barely begun to scratch the surface of IoT can do for their business? Businesses believe in the long-term value offered by integrating IoT into their business plan, however, they lack the knowledge of what is required to ensure the success of such a complex project. By studying past failed projects, technology leaders can gain a better understanding of why they failed and what they can do differently when evaluating and undertaking new IoT initiatives.

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How Will the Emergence of 5G Affect Federated Learning?

Article | April 10, 2020

As development teams race to build out AI tools, it is becoming increasingly common to train algorithms on edge devices. Federated learning, a subset of distributed machine learning, is a relatively new approach that allows companies to improve their AI tools without explicitly accessing raw user data. Conceived by Google in 2017, federated learning is a decentralized learning model through which algorithms are trained on edge devices. In regard to Google’s “on-device machine learning” approach, the search giant pushed their predictive text algorithm to Android devices, aggregated the data and sent a summary of the new knowledge back to a central server. To protect the integrity of the user data, this data was either delivered via homomorphic encryption or differential privacy, which is the practice of adding noise to the data in order to obfuscate the results.

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Spotlight

Qubole

Qubole delivers a Self-Service Platform for Big Data Analytics built on Amazon Web Services, Microsoft and Google Clouds. We were started by the team that built and ran Facebook's Data Service when they founded and authored Apache Hive. With Qubole, a data scientist can now spin up hundreds of clusters on their public cloud of choice and begin creating ad hoc and/or batch queries in under five minutes and have the system autoscale to the optimal compute levels as needed. Please feel free to test Qubole Data Services for yourself by clicking "Free Trial"​ on the website.

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