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The Webzeitgeist Architecture: Revolutionizing Web design through Design Mining

The ease of conducting business transactions online has indeed been the driving force for developers to revolutionize electronic commerce. Through Web Mining, the ability to monitor the users’ browsing behaviour enables businesses to easily locate their specific target markets with web design Auckland. Web Usage Mining is defined as the identification of usage patterns from web data by application of data mining techniques to improve web design based tools and applications. It consists of 3 phases: the usage mining or click analysis, the content mining or text analysis, and the structure mining or link analysis. However, since visual presentation has played an important role in selling businesses online, the information that Web Usage Mining provides, eventually, may not suffice. This gave birth to the need for “Design Mining”.

Design Mining was introduced in 2013 and is expected to transform the way web design are constructed. Web knowledge Discovery and Data Mining Techniques are implemented to design demographics, automate design curation and support data-driven applications. This idea was coined from Webzeitgeist, a software platform for mining and machine learning on Web design. The Design Mining Webzeitgeist Architecture is a platform that integrates information about the design and structure of the pages with the web mining content to produce new web design applications. With Design Mining, web designers and developers are able to understand design demographics to determine trends and search for patterns, as well as address design problems, without the need for manual curation.

web-mining

Webzeitgeist Design Mining’s main features include the following:

Design Demographics. Webzeitgeist has the ability to examine the distribution of page-level and node-level Web properties. Furthermore, it is capable of determining the patterns between individual page assets. Lastly, since Webzeitgeist stores HTML properties with the design data, it enables the use of repository to revisit previous HTML demographics and deter correlations with current trends.

Design Queries. With Design Queries, Webzeitgeist is capable of rapidly creating dynamic clusters indicating specific design trends and characteristics. Also, inclusion of more constraints can be employed to build more queries.

Machine learning. The last main feature enables applications to stream controlled visual descriptors for specific page elements from a central repository. Furthermore, with the Webzeitgeist’s extensible architecture, new information may be stored and integrated with the repository for supervised learning applications.

Despite these advances in e-commerce, privacy has always been a profound issue in the society. Site administrators find it useful to determine the demographics of users and usage statistics, hence, being able to improve and customize their respective web design and contents. However, most users remain reluctant with the idea of somebody monitoring their web site visits. It is therefore still a challenge to strategize guidelines in which site administrators can evaluate web data while maintaining user anonymity.

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