Cloud Analytics Consortium presents: Analytics in the Cloud: Ready for Primetime

Personal data is a highly valuable asset. The winners of the future will be the organisations that make privacy intrinsic to data innovation. Join this webinar to learn how emerging best practices and technological solutions are helping financial institutions tackle data privacy in analytics and ML and drive commercial benefit.

Privitar is a leading privacy engineering software company. Privitar enables organisations to use, share and derive insight data safely. Privitar creates opportunities by allowing broader use of valuable information assets for collaboration and sharing, whilst reducing the risk associated with storing, processing and using sensitive data due to data breaches, regulatory penalties and the misuse of data.

Radiant is a robust tool for business analytics and running sophisticated models without any need for code development. It leverages the functions and tools in R and at the same time provides a user-friendly interface. With Radiant, you can manipulate and visualize your data, run different models from simple OLS to decision trees (CART) and neural networks, and evaluate your results.

The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vicent Nijs. In this webinar, we review the tools available in Radiant and explain how easily you can use this tool without any setup or installation on your system.

Radiant key features:

• Explore: Quickly and easily summarize, visualize, and analyze your data
• Run different models: OLS, GLM, Neural Networks, Naïve Bayes and CART.
• Cross-platform: It runs in a browser on Windows, Mac, and Linux
• Reproducible: Recreate results and share work with others as a state-file or an Rmarkdown report
• Programming: Integrate Radiant's analysis functions with your own R-code
• Context: Data and examples focus on business applications

After this webinar you will learn:

• Data manipulation and running different models
• How to run advanced analytics in a browser on any device even in your tablet or iPad.


Presenter bio:

Ali has a Ph.D. in Finance from the University of Neuchatel in Switzerland and a BS in Electrical Engineering. He has extensive experience in financial modeling, quantitative modeling, and financial risk management in several US banks.

From automated vehicles to ride hailing apps, transportation as we know it is changing - and fast. But new technologies alone won't help communities build the efficient, equitable, and sustainable transportation networks communities want. In fact, these innovative technologies could do just the opposite, especially if they are not deployed wisely. Cities must collect the right data and enact the right policies to ensure they do not exacerbate problems like inequity and traffic, and to hold themselves accountable to the promise of new mobility technologies.

In this webinar, you will find out why - and how - the smartest cities of tomorrow will be those that adopt data-driven transportation strategies today. Join for Laura Schewel's presentation to gain insights into:

• Why the status quo for transportation data collection is no longer good enough
• The types of Massive Mobile Data that are useful for transportation and urban planning
• Algorithmic processing techniques that are critical for making this data useful
• Case studies from California and Virginia that demonstrate why Massive Mobile Data drives more effective transportation planning
• A forward-looking blueprint for using Massive Mobile Data to maximize the potential benefits of new transportation technologies - and minimize negative impacts

Laura Schewel founded StreetLight Data, a mobility analytics provider, after spending more than a decade as an advanced transportation researcher and statistician at the Rocky Mountain Institute and FERC. She has particular expertise in transportation systems, sustainability and safety, and vehicle/system modeling and analysis.

IoT is a technology that has the potential to make us healthy, wealthy, and wise especially in healthcare. Healthcare is just now adopting IoT to improve patient outcomes and decrease the cost of care.

In this webinar, you’ll learn:

- How to identify if an IoT solution will work for your use case.
- What others in healthcare are using IoT for.
- The challenges of IoT in healthcare

Whenever there is data, there is the chance to visualize it and gain valuable insights that can drive change and improvements. Governments have realized the potential that data holds for transforming our towns, cities, living spaces and communities to better address the needs of our modern society.

Governments may want to change public transport services to suit commuters who move away from city centers due to increasing living costs, or develop programs that deliver more support services to areas showing high incidences of mental illnesses, or simply monitor bike traffic to assess the necessity of additional cycle lanes and bike share programs in our capitals. Data and data visualization can help us identify the needs of our communities and can support us in addressing them effectively.

In this webinar Andy and Eva will present examples of Government using data to improve services for communities and will share how you can get involved through analyzing open data and becoming part of the wider 'dataviz' community.

We will examine challenges regarding business model design in the emerging context of the Internet of Things (IoT).
An unrefined IoT business model implies a significant risk in terms of investment and implementation.

The key question is, "How can IoT application models be subjected to a reality check taking into account challenges pertaining technology, organization and target markets?"

Moving from A-B is slowly being revolutionised through data. Car-sharing and ride-hailing are just the beginning. Thousands of connected devices are currently monitoring data points, and although stand-alone analysis can be useful, true innovation occurs when these data sets are combined to transform into something new.

In the near future, IoT data explosion and the API revolution will collide to change city planning, urban movement and the role of the car in the 21st century.

First off, we set out to understand what big data means in the context of transportation, answering questions such as what is it, where is it coming from, and what can you do with it.
- Next, we'll zoom out and apply these learning to transport innovation in a wider context, considering how it will influence concepts such as urban movement, social mobility, and quality of life.
- Finally, we'll discuss the relationship between open data and innovation.

For decades “things” have been connected to the Internet. Embedded in carefully planned end-to-end solutions, the what and why of the data arising from these devices has often been hard-coded. In other words, in the M2M world, it is usually clear from the outset what is going to happen with the data. In a future IoT, this won’t necessarily be the case. In a world full of connected devices, the meaning and the potential of the device data is only going to become clear in the context where it is needed in.

But how can software tell that your connected thermometer is useful for a medical application, or that a car in the drive way is likely an indicator of your partner’s presence? This is where device catalogues, information models and ontologies come in handy.

While the talk is not specifically tailored towards a smart city focus, it should become clear how these technologies can be useful in such environment.

Fog computing represents a tectonic shift for the future of transaction management, distributed supply chain and overall experience. It blurs the lines between the edge and the cloud and puts the focus on the systems which manage and balance the delivery of coherent, end-to-end sessions and associated transaction level agreements. As a result, this new technology is pervasive in several industries.

Join this panel of experts as they discuss solutions with specific industry use cases from smart fog for Cities, Buildings, Ports, and Maritime.

Moderator: Katalin Walcott, Work Group Chair Manageability at OpenFog Consortium & Principal Engineer - IoT/Fog Computing Orchestration Architecture at Intel

Panelists:
- Jeff Fedders, President at OpenFog Consortium & Chief Strategist, IoTG Strategy and Technology Office at Intel
- Mark Dixon, Senior Architect for Smarter Cities at IBM
- Matthew Bailey, President, Powering IoT - Smart City advisor and strategist to governments, technology corporations, and economic development agencies

See how Netflix built its analytics in the cloud with Tableau and Amazon Web Services

Building out a data platform doesn't have to be like building a House of Cards, and our friends at Netflix know this better than anyone else. With 86 million members and counting, and more than 700 billion events per day, Netflix has had to expand their data capabilities by developing a scalable and flexible analytics platform built on Tableau and AWS.

Attend this webinar to hear from Albert Wong, analytics expert at Netflix, to see how they simplified their data stack by building a data lake/data warehouse strategy which allows Netflix to collect and store massive amounts of data, supporting thousands of Tableau users with managed data.

You'll learn about:

How to set up effective analytics on top of enormous data sets
How Netflix serves large groups of people with governed data
The details of Netflix's data lake/data warehouse strategy
How Netflix manages Hadoop with Tableau

Fog computing has the potential to resolve the issues with network latency since the media rich content can be delivered through such nodes directly.

Fog Computing inside the mobile network providers opens up a window of revenue opportunities for MNO's and creates a innovative space in content & application delivery platform.

Join this webinar to learn how to tackle the different challenges with fog computing and its role in the IoT cycle.

In this talk, Ram will provide a unified framework for Internet of Things, Cyber-Physical Systems, and Smart Networked Systems and Societies, and then discuss the role of ontologies for interoperability.

The Internet, which has spanned several networks in a wide variety of domains, is having a significant impact on every aspect of our lives. These networks are currently being extended to have significant sensing capabilities, with the evolution of the Internet of Things (IoT). With additional control, we are entering the era of Cyber-physical Systems (CPS). In the near future, the networks will go beyond physically linked computers to include multimodal-information from biological, cognitive, semantic, and social networks.

This paradigm shift will involve symbiotic networks of people (social networks), smart devices, and smartphones or mobile personal computing and communication devices that will form smart net-centric systems and societies (SNSS) or Internet of Everything. These devices – and the network -- will be constantly sensing, monitoring, interpreting, and controlling the environment.

A key technical challenge for realizing SNSS/IoE is that the network consists of things (both devices & humans) which are heterogeneous, yet need to be interoperable. In other words, devices and people need to interoperate in a seamless manner. This requires the development of standard terminologies (or ontologies) which capture the meaning and relations of objects and events. Creating and testing such terminologies will aid in effective recognition and reaction in a network-centric situation awareness environment.

Before joining the Software and Systems Division (his current position), Ram was the leader of the Design and Process group in the Manufacturing Systems Integration Division, Manufacturing Engineering Lab, where he conducted research on standards for interoperability of computer-aided design systems.

We are observing a monumental effort from the industry and academia to make everything connected. Naturally, to understand the needs of these connected things, we need a better understanding of humans and where, when, and how they interact. This behavioural understanding would help us to create digital services and capabilities that fundamentally change the way we experience our lives.

In this talk, I will explore the system and algorithmic challenges in modelling human behaviour. I will discuss how mobile and wearable devices together with the wireless network can be used as a multi-sensory computational platform to learn and infer human behaviour and to design user-centred connected services across Enterprise, Urban City and Lifestyle.

Dr Fahim Kawsar leads the Internet of Things research at Bell Labs and holds a Design United Professorship at TU Delft. His current research explores novel algorithms and system design techniques to build transformative multi-sensory systems for disruptive mobile, wearable and IoT services. He borrows tenets from Social Psychology, learns from Behavioural Economics and applies Computer Science methods to drive his research. He is a frequent keynote, panel and tutorial speaker, hold 15+ patents, organised and chaired numerous conferences, (co-)authored 100+ publications and had projects commissioned. He is a former Microsoft Research Fellow and has worked before at Nokia Research, and Lancaster University. His work and publications can be viewed at http://www.fahim-kawsar.net.