people powered search based on shared interests
Jumper 2.0 is a collaborative search engine. It delivers more targeted search results based on user provided knowledge within
select communities of users formed around skill-sets, disciplines, projects, or job descriptions. Our interactive, user-submitted recommendation
engine uses peer and social-networking principles to reference any information located in distributed storage devices, either inside or outside the
firewall. Users crowdsource knowledge by tagging, linking, and rating relevant content, media, or data - from both internal and external source
systems - to share a more holistic and targeted view of the information landscape. A revolution in enterprise search that consolidates everything
of value for a focused group of users in one search box.
Jumper is a unique combination of many tools you are already quite familiar with such as Wikipedia, Delicious, and Amazon. Jumper combines
these tools in a new kind of interactive, user-submitted search engine.
Something Familiar - And New
Like Wikipedia, Jumper allows users to become contributors and editors of the service by creating, editing, and managing
tag profiles. These tag profiles reference any distributed content, media,
or data. Users create a brief one or two paragraph description similar to what you find in Wikipedia that describes the information.
Jumper allows users to tag any information resource with keywords just like Delicious (although
without the plugin). Jumper allows users to tag information with much more than keywords. Our expanded and customizable
knowledge tags deliver the who, what, when, where, and why knowledge about the
information.
Jumper delivers a comment and rating system that is similar to Amazon. Users provide reviews, comments, and opinions about the information
resource to give the search results relevance and context. Users can also quickly rate and rank the information based
on its importance or value.