Im very happy with the OpenDDR dataset. I would easily put the accuracy in the high 90% area and its only getting better with time. The accuracy is due to the fact that OpenDDR identifies devices using pattern matching. This is a highly effective and simple technique and gives it a huge advantage over other DDR products. For example, I evaluated WURFL and found it had subpar accuracy, poor performance, and a bloated DDR.
The monthly releases are very "fresh". Been using OpenDDR since late 2011 and haven't had any devices slip thru. I would make sure you update your DDR several times a year to stay ahead of any new devices. Device discovery will obviously improve as these projects get more visibility. Reza -----Original Message----- From: Stefano Andreani [mailto:[email protected]] Sent: Thursday, August 16, 2012 12:25 PM To: [email protected] Subject: Re: device routing and feature detection Hi Reza, Thanks for sharing this: there is still a lot of confusion in the market about device detection, and your classification is very clear and meaningful. TheWeatherChannel has a large set of devices accessing its contents, so you should have a good visibility about accuracy of the dataset under use. What is your opinion on the accuracy of OpenDDR dataset? Are the monthly updates "fresh" enough to allow a correct mapping of new devices accessing TWC web sites? Thanks, Stefano. On 13/ago/2012, at 22.32, Naghibi, Reza wrote: > I recently had an interesting conversation around device detection and > decided to share it with the list. I categorize device detection into the > following: > > -Backend device detection and request routing > Looking at the HTTP User-Agent field allows requests to be accurately > routed to device specific sites (or services or resources). This gives you a > lot of design flexibility. You can go ahead and target a given site to > devices where it's most appropriate and not have to worry about how to make > that design work across a wider array of devices. This has many performance > benefits, both backend, network, and frontend. > > -Client side feature detection > This represents the other side of the device detection spectrum. What > is important here is that you detect the presence (or lack of) features and > not the devices themselves. This is done very differently than backend device > detection and is usually accomplished with CSS and Javascript. Feature > detection on the client side will provide for a much more responsive design > across devices than device detection. > > -Client side device detection > I found client side device detection only helpful in limited scenarios > like metrics, ad targeting, and client side analytics. Client side device > detection can be done by exposing backend device detection as a JSON service > and having the browser make a call to the service. > > I found the most value in combining backend device detection routing with > client side feature detection. This allows sites and services to be designed > with specific device classes in mind (feature phones, smart phones, tablets, > desktop browsers) which allows for the reduction of responsiveness complexity > and a design which is more native to the target device. The key here is to be > flexible. Purely responsive designs and purely device detected designs are > not flexible enough to meet all performance and design demands. > > I recently open sourced our backend device detection framework which uses > OpenDDR and provides backend integration: > https://github.com/TheWeatherChannel/dClass. dClass is used for backend > device routing and for client side device detection service (both in Varnish). > > Reza Naghibi > >
