
(Click to expand the image)
I've knocked up a diagram to explain how I think the Real Time web looks like at a systems level. Hopefully people will find this an interesting way to come up with new ideas.
Data Level
Huge amounts of data are produced from many elements: sensors, platforms, applications, media and sites.
Sensors
This includes data from physical sensors and sensors arrays. In the future we will have GPS location tracked in real time, with heart rate, altitude, tilt, speed, acceleration and all other variables that we can capture fired out to the web. This opens up the opportunities for new and exciting applications for fitness, socializing, games and business improvement. Sensors also include microphones, cameras and video cameras.
Platform Level
This data can be entered through a variety of platforms including mobile, social networks and browsers.
Application Level
Applications like tweet deck are really taking off now. This could be seen to be included into platforms but applications are more of a subset of the platforms. The applications ride on top of the platforms.
Media Level
Media like video, music, games or pictures now create their own data trail into the real time web. For example, inside games you can retweet your score. Picture tagging and real time music tracking are other examples of media creating a real time data source.
Site Level
This could be included in the platform level but there are specific dedicated sites like twitvid.io (for video) and twitpic.com (for pictures) that are accelerating the data creation (among other functions).
Filtering Level
We now have a huge amount of data to process. There are many ways to filter the data. Including but not limited to rating based, location based, time based and socially based.
Application Level
Applications can filter the data for us. E.g. inside tweetdeck groups can be made to filter by social relevancy, time, or keyword based.
Spam
Spam is massively on the rise inside twitter - new filters must be made to solve this. A very solvable problem.
Location filtering
One of the more exciting filtering methods. Location based filtering is taking off in a big way with a great example being the iphone app foursquare. Real time + location helps us move to an augmented reality.
Syndication
Once the data is filtered, the data is to be driven out to the web. Additional filtering may occur at this point. Syndication can be achieved in various ways.
Platform Level
This is being done at the moment but there are still many links that can be made. E.g. you can link up your twitter account to output facebook feed. The future will be more highly connected feed wise.
Widgets
We see simple widgets that give real time feeds. Twitter widgets are fairly exciting to watch and add a bit of spice to a site.
APIs
Apis are another great way for the data can be syndicated. The main example being the twitter firehose.
Applications
Applications like tweetdeck take the data back in to show to the user. It's a relatively immature space still.
Explicit Search
This is an active search through existing or future data. Good examples are scoopler.com and search.twitter.com
Implicit search
The search method is built into the platform or application layer as a passive filter. E.g. creating a group inside tweetdeck is a specific implicit search.
Push/pull search
Another method to search the real time web is to ask a question and have it answered for you when the answer comes up. Aadvark is a good example of this push type search where the data is generated specifically for the request. Pull search doesn't have to generate any more data, it just searches through the current data.
Monetization
Across the range of levels (data, filtering and syndication) will we see advertising, subscriptions and micropayments models being applied to all sections of the real time web.
Modifiers
The most interesting modifier to me is when the loop is set to be automated. E.g. having your GPS location fired out in real time. Or what one is up to being made public in real time and in an automatic fashion.
Verticals
Another way to cut this market is to be specific in a vertical. In many cases its highly important to do this. Focus can be on news, pictures, video, games or social graph.
Conclusion and Cycle Time
A major point is the cycle time through the system is a measure of the 'real timeness' of the system. The faster the data can be entered and syndicated out to the appropriate sources the more real time the system is. Once the speed goes below a couple of minutes - it becomes real time. One the cycle time goes below a few milliseconds it reaches a new milestone of being below the reaction speed of a human - this is truly real time. Another important point is the reactions from syndication go out to cause new data being created resulting in phenomena like hashtags, RTs and news hype - this is a type of real time feeback effect. As we see this cycle time fall for other systems there are new and interesting phenomena that will follow. E.g. a networking event running with an eye-fi camera setup to twitpic.com or facebook causes an effect on the a networking event. There are hundreds of new opportunities in this area.



