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		   B O S T O N   U N I V E R S I T Y
                                       
		      Computer Science Department
                                       
			  C O L L O Q U I U M
                                       

		Indexing and Mining Multimedia Databases

                              Philip Korn			
                       Dept. of Computer Science		
                         University of Maryland		

	             Thursday, February 12th, 4:00 pm
                        (Coffee served at 3:45pm)
                                       
                         Seminar Room / MCS 135
                                      

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This talk focuses on similarity  searching  and  data  mining  in
large  multimedia  databases.   Similarity  search  involves  the
retrieval of multimedia objects (e.g., images, time series)  that
are  most  "similar"  to   a   query  object, for example:  `Find
images  from  a  given collection of X-Rays that contain a nodule
similar  to  the  given  tumor  shape,' and `Find all stocks with
movement  similar  to  that  of  IBM.'    Using   concepts   from
mathematical morphology and tools from state-of-the-art indexing,
we developed a system that efficiently searches for similar tumor
shapes   while   attaining   correct   output   (i.e.,  no  false
dismissals).  The system  is  27  times  faster  than  sequential
scanning,  and  exhibits  excellent  precision  (80%)  at perfect
recall (100%).

The second part of the talk examines data mining.  The goal is to
support  ad hoc queries on large data matrices that might not fit
on disk.  Such a matrix could have, e.g., customers for rows  and
days  of  the year for columns, with each cell value representing
the amount spent on products.  The target queries are single-cell
queries  ('Find  the  amount  spent  by  Smith  on  1/1/96')  and
aggregate queries ('Find the sales of customers from New York  on
December  1st').   We  propose  a compression format that permits
random access, and thus  efficiently  supports  ad  hoc  queries.
Towards  this  end,  we developed SVDD, a novel lossy compression
method for very large data matrices, which reduces the matrix  to
2%  of  the  original  space (i.e., a 50:1 compression ratio) and
achieves 0.5% reconstruction error, as experiments on  real  data
(e.g., AT&T customer sales) showed.



(This is a faculty candidate colloquium.)

Host: Wayne Snyder (snyder@cs)

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For colloquium info, including directions, see http://cs-www.bu.edu/colloquium
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