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Date: Tue, 05 Nov 1996 20:58:29 GMTServer: NCSA/1.5Content-type: text/htmlLast-modified: Thu, 28 Sep 1995 17:30:31 GMTContent-length: 1931<HEAD><TITLE>NEXT! description</TITLE></HEAD><BODY><H1><A></A>What is NEXT!?</H1><H2></H2><HR>The goal of the NEXT! project is to develop tools for exploratoryanalysis of large, non-traditional datasets. The name of the projectis an acronym (Non-traditional data EXploration Toolkit) that isalso a play on the fact that a major emphasis is on supporting*sequence* data exploration. We use the term *sequence* broadly,and include as examples trace data, time series data from a variety of domains, and satellite or sensor observations.<P>The NEXT! project has three main components. First, there is avisual data exploration tool called <!WA0><!WA0><A HREF="http://www.cs.wisc.edu/~jussi/devise/devise.html"> DEVise </A>that supports aparadigm called *visual queries*. Each visual query is in essencean abstract description of how a collection of sequence data is to be mapped to the user's screen. The query serves as atemplate that can be applied to any data set with the appropriateschema, and the visual presentation can subsequently be exploredin detail by standard operations such as scrolling, zooming andprobing.<P>The second component of NEXT! is a system called <!WA1><!WA1><A HREF="http://www.cs.wisc.edu/~praveen/projects/seq.html"> SEQ </A>that supportsSQL-style queries over sequence data. SEQ is distinguished fromstandard SQL in that it provides easy (and efficient) ways topose queries that use the sequentiality in the data set, forexample, ``Find the N-day moving average of IBM''. SEQ can beused in conjunction with DeVise, with DeVise serving as a visualbrowser and SEQ as a data manipulation backend.<P>The third component of NEXT! is an on-going effort to investigatenovel ways of analysing large data sets (often called *data mining*).In particular, we have developed a tool called <!WA2><!WA2><A HREF="http://www.cs.wisc.edu/condor/NEXT/birch.html"> BIRCH </A>that caneffectively cluster very large data sets.<P></BODY>⌨️ 快捷键说明
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