how to convert "sloppy data" into a time series?

Hi All,

   First let me state that I did search for a while on r-help,  
google, and
using the "sos" package inside of 'R', without much luck.  I want to  
know
how to create a univariate time series from a set of data that will  
have
huge time gaps in it.  For instance, here is a snapshot of a piece  
of data
that I would like to analyze:

*Row             queued_time       processTime
50  2010-06-15 21:50:42.443 6.399989e-02 secs
63  2010-06-15 21:51:57.347 6.300020e-02 secs
156 2010-06-29 14:53:26.073 3.011863e+06 secs
175 2010-07-22 10:14:57.503 4.334879e+06 secs
278 2010-08-05 11:29:56.713 6.155674e+06 secs
509 2010-08-05 11:29:57.443 3.120779e+06 secs
531 2010-08-05 11:29:57.543 3.120779e+06 secs
555 2010-08-05 11:29:57.647 3.120779e+06 secs
190 2010-08-05 11:29:57.943 3.120778e+06 secs
230 2010-08-05 11:29:58.047 3.120778e+06 secs
211 2010-08-05 11:29:58.917 3.120777e+06 secs
251 2010-08-05 11:29:59.077 3.120777e+06 secs
298 2010-08-05 11:29:59.297 3.120777e+06 secs
320 2010-08-05 11:29:59.397 3.120777e+06 secs
366 2010-08-05 11:29:59.707 3.120777e+06 secs
342 2010-08-05 11:30:00.987 3.120775e+06 secs
380 2010-08-05 11:30:01.200 3.120775e+06 secs
120 2010-08-19 09:31:47.207 2.358866e+06 secs
141 2010-08-19 09:31:47.500 2.358866e+06 secs
842 2010-09-03 13:58:21.463 3.641194e+06 secs
*
   I would like to be able to take the second column, the  
"processTime",
and put it into a time series using the first column as the key to  
say when
it occurred.  But everything I could find, such as ts(), went on the
assumption that I had fully univariate data to start with, and all I  
needed
to do was set the frequency & start date (in the case of ts() ).
   I can adjust the "queued time" arbitrarily as needed, so that if,  
for
instance, the data set would end up far too sparse & empty by  
keeping the
current precision, I could cut the "queued_time" precision down to  
just the
year, month, day, hour.  But in that case, how would the time series  
handle
the fact that there are several (varying) entries with the same value
stored.

   The reason I want to do this is because I next want to be able to  
use
all the very nice modeling capabilities that a univariate time series
allows, such as arima, etc.