The Future of Search and Discovery in Big Data Analytics: Ultrametric Information Spaces

Details The Future of Search and Discovery in Big Data Analytics: Ultrametric Information Spaces The Future of Search and Discovery in Big Data Analytics: Ultrametric Information Spaces

2012-02-15

arxiv.org/abs/1202.3451v1

Computer Science

Information Retrieval

10 pages

Scientific paper

Consider observation data, comprised of n observation vectors with values on a set of attributes. This gives us n points in attribute space. Having data structured as a tree, implied by having our observations embedded in an ultrametric topology, offers great advantage for proximity searching. If we have preprocessed data through such an embedding, then an observation's nearest neighbor is found in constant computational time, i.e. O(1) time. A further powerful approach is discussed in this work: the inducing of a hierarchy, and hence a tree, in linear computational time, i.e. O(n) time for n observations. It is with such a basis for proximity search and best match that we can address the burgeoning problems of processing very large, and possibly also very high dimensional, data sets.

Affiliated with

Also associated with

No associations

Rating

The Future of Search and Discovery in Big Data Analytics: Ultrametric Information Spaces does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with The Future of Search and Discovery in Big Data Analytics: Ultrametric Information Spaces, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and The Future of Search and Discovery in Big Data Analytics: Ultrametric Information Spaces will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-124630