A real-time stream processing platform
The emerging Internet of Things is transforming the world into a giant source of live data streams. These streams range from slowly-trickling sensor samples to bandwidth-hungry video streams. They carry valuable data that – when processed in a continuous and timely manner – hold the potential to significantly improve people’s lives. For example, continuous streams of data can help us to make better-informed decisions, give us feedback for improved well-being, and connect homes and industries to create new levels of life automation. In addition to this, these devices will interact with each other and form control loops - and such control interactions require instant analysis and actions.
To execute on this vision - making individualized views of these data available, and triggering personalized actions and feedback - a new breed of software platforms is required – platforms that provide open, scalable, efficient, and programmable support for streaming data and fast-changing user contexts. Such open and programmable access to live streams will not only disrupt the way people consume and share streaming data – much like the original World Wide Web disrupted the way people consume and share predominantly stored data. It can be the seed of a much broader industry disruption – redefining how companies access, share & transform information streams.
Fragmentation in the IoT industry, rooted in disparate devices and applications built on proprietary protocols, can stifle innovation. This complex ecosystem makes it harder for application developers to innovate and create new applications cost effectively. With the rapid development of opportunities in the Internet of Things (IoT) marketplace, organizations are challenged in developing vertical business-specific solutions while ensuring maximum reusability across their organization. There is a need for software platforms that provide easy customization and onboarding of domain specific knowledge, algorithms and protocols.
Key features
A large-scale, geographically distributed, stream processing platform which can ingest, process and deliver large numbers of data and media streams in real-time between geographically distributed sources and sinks. Flexible chaining of pluggable analysis modules is possible, and new applications are authored centrally and distributed deployed on cloud, edge or device.
Applications on top of the World Wide Streams platform consist of one or more continuous queries or flows. Flows are authored in a novel Bell Labs language that is called XStream. XStream’s built-in operators cover most of the common stream processing operators (e.g. map, alter, join, partition, etc). Additionally, it supports the declaration of external operators which are not implemented in XStream itself, but whose inputs and outputs can be directly wired up with other XStream operators. XStream has been developed as a library and embedded in TypeScript, a JavaScript dialect with optional static typing.
Showcase and demonstrations
The connected car is going to play a fundamental role in the foreseeable Internet of Things. The connectivity aspect in combination with the available data (e.g. from GPS, on-board diagnostics, road sensors) and video (e.g. from dashcams and traffic cameras) streams enable a range of new applications. Five connected cars applications have been developed on top of World Wide Streams. Anomalous Driving is a scenario that detects cars that are rapidly accelerating or braking. Potential users of these scenarios can be fleet managers and parents. The Congested Areas application detects clusters of abnormal deceleration behavior, indicating rapid slow-down of multiple cars within a short time frame and geographic perimeter. Potential users of the events can be other drivers in the vicinity.
Remote Video Monitoring enables traffic officers to use a collection of the dashcam feeds within an area of interest (e.g. accident scene or congested square/crossing) to obtain insights in real time. Amber Alert allows a privileged user (e.g., a police department administrator) to initiate a simultaneous and large scale search for a particular license plate number (e.g., of a stolen car) within a certain geo-fence (e.g., a city district) using the available dashcams. It uses the dashcams to do license plate recognition, potentially leveraging the processing power available in the car. Sightings are reported on the map. Users can navigate to the car that did the sighting and see its live dashcam stream for immediate context. Aggregate Statistics provides city managers with real-time distribution of various events of interest such as speed, fuel consumption, and CO2 emission.
In smart grids, a fundamental shift is occurring for utilities. The historical one way electric power system (with a linear relation between generation, transmission, distribution and retail) is transforming into an autonomous, integrated and interactive energy cloud. In this energy cloud, multiple actors collaborate and compete to offer advanced and integrated services like demand response management, smart meter (data) management, and grid optimization and automation.
There is a need for common platforms and shared real-time data (and actuation) throughout this energy cloud to be able to create such encompassing services. The World Wide Streams platform is designed to support multiple stakeholders to collaborate in a marketplace of real-time information and actuation streams. The platform allows energy cloud actors to compete by onboarding and applying their specialized algorithms and application logic.
ShowMe is a mobile web app that allows users to get live streaming video footage of particular points of interest, such as tourist locations, bars or concerts. For instance, if you want to get a feel for the atmosphere right now at the San Marco Square in beautiful Venice, Italy. This touristic application allows the user to launch a live query and watch live video feeds from users on the San Marco Square, in real-time.
The app integrates seamlessly with Foursquare, allowing Foursquare users to be alerted when there’s an incoming live query for the place they’ve just checked into. All they have to do is follow a link and shoot video with their mobile camera, which is streamed in real-time to everyone interested. Streams from multiple users are aggregated on the fly, thus creating a collaborative and ad hoc live viewing experience of points of interest, anywhere on earth.
Scaling an application such as ShowMe requires transformative changes to the infrastructure of the world wide web and raises questions of how best to manage thousands of live queries and their associated streams. One can compare ShowMe to Facebook Live but the trigger to start to stream comes from the interested audience, not from the person at the point of interest.
Smart Mirrors will come to the fitting room in shops. But Smart Mirrors will also appear in the bathroom. These magic mirrors will show the main vital signs of the persons standing in front of it.
In this Smart Mirror application, whenever a person - standing in front of the mirror – moves her hand to her heart, her vital signs are shown on the mirror, next to her reflected image. For this, multiple streams are combined: live data on vital signs from the user’s wristband & gyroscope data from the same wristband to detect the gesture. This is complemented with gesture analysis on a video camera stream. The correlation of the gesture detected by the wristband and by the mirror’s built-in camera allows a secure verification of the user actually being in front of the mirror, thus safeguarding the user’s privacy.
Pong is one of the earliest arcade video games and the first sports arcade video game. In the Phone Pong game, a user can play pong by using the sensors such as the accelerometer of his personal smartphone. The sensor streams of the smartphone will control the bat in pong.
This toy example shows the easy onboarding of devices (the user's smartphone) and their sensors on the World Wide Streams platform.
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