Moving to Web 3.0

21 Mar

Whenever we explain what we do to non-nerds, we try to give context to the overall market. The market is moving to Web 3.0. But what is Web 3.0?

Think about Web 1.0 as content written by authors, and laid out on the static web. A good example would be a blog, or a map of a location online. Web 2.0 was all about user created content. These would be users writing reviews of restaurants, or writing content on Facebook. Web 3.0 is often called the semantic web. This is just a fancy way to say that Web 3.0 will be about data, and what computers can do with data that individual humans can’t.

A perfect example is Google Traffic. Google takes data from millions of Android devices and watches the device speed. They then match the speed of the device to the road that the driver is on, and see if traffic is moving faster or slower than it should be. How could an individual user do that? They couldn’t. But it’s useful information, no?



One of the missing meta data to any brick and mortar today is understanding what the ambience will be like when you arrive. You know that you can get directions from a map, find reviews from a review site, but you have no idea how long the line will be, or if there will be any line at all! Using mobile data, we can extend Google Traffic to bricks and mortars. Below is a sample map for San Francisco.



Very interesting, and we can watch how mobile location data changes over time to extrapolate what parts of the city are the busiest when. This type of data can be collected via telcos. The FCC published E911 regulations which requires mobile devices to emit GPS data in the case of emergencies. You will, however, notice that the granularity is quite coarse. Data from the telcos are only accurate to 100m tiles in metro areas. Outside metro centers the tiles can grow to 1000m.

But the growing use of WiFi and local connectivity is increasing these accuracies. A phone on a WiFi network can produce location accuracy sub 5 meters. Another image is shown below, with more accurate location signals.

Screen shot 2010-04-05 at 9.04.47 AM


Now we can get down to the proprietor level and determine which specific businesses are busier than the others. This type of information helps us determine when places will be busy. A screenshot from our private application is shown, where we list businesses and how busy they will be. This requires understanding how things like weather, events, etc will impact customer turnout. We even show how a user can compare venues side-by-side to find the experience they’re looking for.

Map ViewCrowd Scroll