51Degrees.mobi-WebMatrix 3.1.2.3
The fastest, most accurate tools. Deployed by millions. Request.Browser properties will be populated with data from 51Degrees Lite Device Data. Other features include automatic image optimisation, monitoring of network conditions and client side feature detection.
<package xmlns="http://schemas.microsoft.com/packaging/2011/08/nuspec.xsd">
<metadata>
<id>51Degrees.mobi-WebMatrix</id>
<version>3.1.2.3</version>
<title>51Degrees Mobile Detection & Optimisation for WebMatrix</title>
<authors>James Rosewell, Thomas Holmes</authors>
<owners>51Degrees.mobi Limited</owners>
<licenseUrl>http://51degrees.codeplex.com/license</licenseUrl>
<projectUrl>http://51degrees.com/Support/Documentation/NET</projectUrl>
<iconUrl>https://51degrees.com/portals/0/Logos/Square%20Logo.png?width=128</iconUrl>
<requireLicenseAcceptance>false</requireLicenseAcceptance>
<description>The fastest, most accurate tools. Deployed by millions.
Request.Browser properties will be populated with data from 51Degrees Lite Device Data. Other features
include automatic image optimisation, monitoring of network conditions and client side feature detection.</description>
<summary>Fast & Accurate Device Detection. Deployed by millions.</summary>
<releaseNotes>Important: Upgrading users of Enhanced Data need version 3.1 format data.
Device detection algorithm is over 100 times faster than version 2. Regular expressions and levenshtein distance calculations are no longer used.
The device detection algorithm performance is no longer limited by the number of device combinations contained in the dataset.
Two modes of operation are available:
Memory – the detection data set is loaded into memory and there is no continuous connection to the source data file. Slower initialisation time but faster detection performance.
Stream – relevant parts of the data set are loaded into memory when required and cached to improve performance. Rapid initialisation time but approximately 50% slower detection performance. This mode is used when operated in a web environment.
JPG and PNG format images can be optimised to improve performance.
Bandwidth and response times can be monitored to understand in real time the end users experience.
Feature detection is used to override properties in the data set to provide details such as iPhone model or the screen orientation. These values become available to the server from the 2nd request from the device onwards.
Multi-threading is no longer used within the matching algorithm.
When used in a web environment the detection results are stored within the session when available and are no longer cached separately.
-- Changes between version 3.1.2.3 and 3.1.1.12 --
1. The ‘Cookies’ property override in the HttpBrowserCapabilities class no longer defaults to false if no data is available in the data set. This fixes a problem some users were having with forms authentication with the Lite data set.
2. Null user agents are treated as empty strings for the purposes of device detection resolving an issue associated with null exceptions in the detection process.
3. SQL.cs class has been modified to include methods to retrieve device information from the Device ID formatted as a string in the form A-B-C-D. Also the internal implementation of some logic has been changed to use iterators reducing strain on garbage collection. Match.cs class now has a new property called DeviceIdAsByteArray which returns the profile Ids as a byte array for use by SQL.cs.</releaseNotes>
<copyright>51Degrees Mobile Experts Limited</copyright>
<language>en-GB</language>
<tags>ASPNETWEBPAGES mobile phone detection device data handset tablet responsive design images 51degrees</tags>
<dependencies>
<dependency id="Microsoft.Web.Infrastructure" version="1.0.0.0" />
</dependencies>
</metadata>
</package>