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Blog

2

Oct

2017

51Degrees: More Than Three Times Faster!

51Degrees compares performance of our enhanced algorithm on three different commodity hardware platforms.

51Degrees is regularly evaluated against competitors DeviceAtlas and WURFL from ScientiaMobile for performance, accuracy and memory consumption. This blog post builds on the previous performance blog from 2016 with the same commodity hardware; 1) a Raspberry Pi, 2) a low-end desktop PC and 3) a high-end multi CPU server, but using an all new algorithm.

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29

Sep

2017

51Degrees talk to Mobile Marketing Magazine

About the problems that still plague publishers and advertisers when it comes to mobile

Author: Anonym

James Roswell, CEO, talks to Mobile Marketing Magazine about the data problems that still plague publishers and advertisers when it comes to mobile, and how they can increase revenue and performance.

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Categories: News

Tags: Performance, dmexco, Video

28

Nov

2016

Device Detection Performance Analysis

51Degrees compares the Raspberry Pi against two modestly priced Intel platforms.

51Degrees is regularly evaluated against competitors DeviceAtlas and WURFL from ScientiaMobile for performance, accuracy and memory consumption. This blog post builds on a 2015 performance blog with three new commodity hardware benchmarks on; 1) a Raspberry Pi, 2) a low-end desktop PC and 3) a high-end multi CPU server to provide information engineers will find helpful when selecting a device intelligence solution.

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21

Jul

2015

Memory and Performance Impact of 51Degrees

Load testing 51Degrees with DotNetNuke CMS

The purpose of testing is to investigate the impact of 51Degrees device detection and image optimisation modules on the performance of the DotNetNuke (DNN) Content Management System. The DNN platform was tested with high user page loads. DNN was chosen as a representative .NET platform for testing 51Degrees .NET API.

This article providea a table of expected memory consumption for common configurations.

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8

Jul

2015

Apple Standalone on Device Detection

Exposing the crippling solution used to detect iPhone and iPad models

Device vendors as varied as the mighty Samsung, to brands like Pantech and Micromax all recognise the importance of identifying different device models when accessing web sites. However, Apple do not fully support this approach.

Some device detection solutions have tried to address the problem by artificially stressing the iPhone or iPad to determine how quickly its processor can complete iterative tasks. Understanding how the device performs under stress provides an indication as to the CPU available and therefore the model of the device. The approach can yield more information but ironically at the expense of user experience as the page load time increases and the battery is drained more rapidly.

If every web site were to stress iPhones and iPads to gain model information the web would slow down for Apple users. This isn’t in Apple’s interest. However, if Apple make a simple change the problem can be avoided.

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8

Jul

2015

Device Detection for Apple iPhone and iPad

Which techniques to use and avoid

Author: Anonym

51Degrees data analysis team stress tested a range of real iPhone and iPad devices, both old and new using all the major web browsers in order to determine if CPU stress testing is a viable additional method to identify iPhone and iPad models alongside User-Agent analysis, screen information and WebGL API

The full results are available for download along with the analysis of the differences between each device and browser.

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8

Apr

2015

5 Foundations of a High Performance Website

A reminder from bricks and mortar retailing

Published by Digital Marketing Magazine our founder James Rosewell references long established high street retailing to provide a reminder of 5 non technical factors which form the foundations of a high performance website.

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Categories: Opinion

Tags: Performance

1

Dec

2014

How the On-line Gaming Industry Delivers Mobile Web Sites Everyday

What can web professionals learn from Black Friday and on-line gaming?

The media coverage of the Black Friday failure of major web sites including Tesco, Argos, Best Buy and Boots highlights how consumer expectation of the internet has changed in just a short period of time. When a competitor is just a screen tap away performance and availability is essential.

The on-line gaming industry has been faster than most sectors to embrace the latest server side techniques to not only deliver stable high performance multi-screen experiences to smartphones, tablets, TVs and laptops but also deliver continuous enhancements to keep their users engaged. In this blog I highlight in marketing and business terms the simple techniques used by two leading gaming web sites to deliver continuous upgrades and quick response times to mobile phones, tablets and desktops/laptops before summarising the benefits of the server side approach used.

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25

Nov

2014

Calculating Detection Speed

How do we know each detection only takes 0.0016ms?

Learn how we obtained the figure of 0.0016ms average time for a single detection. Verify this figure on your system in 4 easy steps.

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11

Jul

2014

Why Fast Device Detection Matters

How 51Degrees Beats the Competition

Detecting mobile devices is important as websites designed with large desktop computer screens in mind rarely perform well on a mobile device. Detecting such devices allows you to present content in the right fashion for the viewing device which improves website performance for your visitors and leads to better user experience. You will find many device detection products on the market and they all claim to be the fastest and most accurate but they do not provide proof. 51Degrees device detection solution achieves 99.97 percent accuracy with 5 million detections per second on hardware that costs less than 1000 USD which makes our solution the fastest, most efficient and hence most environmentally friendly. 51Degrees device detection is easy to deploy in PHP, .NET, Java, Perl, Python and C.  In this article you will find test results for the 3 test hardware platforms we used as well as instruction on how to test our device detection solution in your environment in just 4 easy steps.

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