traditional search technologies Early search engines were simple directories of data to url As the internet grew meta tags, meta data, titles and keywords were used to help search engines identify and keep track of the growing volumes of information in more complex databases. With current levels of data, search engines like Google use ever more complex algorithms to rank the results based on a range of factors including relevance and popularity(from monitoring sites like Alexa or digg like votes as yahoo buzz). There is a trend to make search results more visually rich. Searches like tafiti combine graphic technologies like silverlight to allow results to be stacked and data dragged to a pasteboard. Some search engines like dogpile combine results from several individual search engines like ask,google and yahoo into a single set of results.
With the current volume of data attempts are now being made to improve the quality not quantity of search results. Sites like Clusty, use high quality text clustering and labelling and linguistics to second guess what users are really looking for. Recognising the difference between “a drive by killing” and “making a killing”. Sites like Mahalo, a “human search engine” are concentrating on having real people write result pages with meaningful explanations examples, tips and links. Sites like Wikipedia provide a single source for user generated information. Wikia search is a sort of hybrid search engine with social content. True knowledge is an attempt to create semantic search engine that will answer real questions, like when was Elvis born or what age is Madona newer search technologies
Booleans statements or search maths allow you to search using multiple search terms at the same time to widen or narrow your search.
Use AND, OR (+) and NOT(-)
Example:You might want to find a hotel in paris texas.
Use AND or OR (+) to expand the search to look for all terms
paris AND texas AND hotel ( paris + texas + hotel)
Use NOT (-) to reduce the search to remove some terms
paris AND texas AND hotel NOT france NOT hilton
( paris + texas + hotel -france -hilton)
Use SITE: to limit your search to specific websites Example: Site:co.uk travel would only find travel sites with.co.uk urls. Site:microsoft.co.uk sharepoint Use inURL: to limit it to site with the term in the url. Example: inURL:baltic would find only sites with baltic in the actual url Use filetype:<type> to limit it to site with the term in the url. Example: filetype:pdf baltic would find only pdf documents refering to baltic. specify operands
Search engines already sort data by file type, image, news, web etc they will arrange result into cluster groups by context. For example software may be clustered into groups by platform, function, cost etc
Search engines will match your search patterns to guess what you may want to search for next, based on what other people have searched for next.
Geo aware searching
With increased numbers of mobile devices including gps / gprs technology, mobile searches will be increasingly geo aware, knowing where you are and returning search results that are close by.
Your search engine will learn your tastes, humour and preferred way of receiving information and show you only products, images or jobs that it thinks you’ll like in the way that you like to see them.
You will be able to ask, real questions like who designed this, or when was he born.and get short simle answers.
Site data will be translated into a common language and tagged with Semantic tags so that when you search the web you will get all the results not just those writen in the language you happen to speak.
what are we likely to see in the future...
<contributions> Most of the data in this document was found using search engines, And social bookmarking sites. Much of the content came from online communities and Documents shared using creative commons.. <Produced by> david coxon www.davidcoxon.com
its about finding not searching... targeted searches yield quality results