CCing the mailing list again. It's currently not on the radar. Do you have a use case for it? I can bring it up during 1.6 roadmap planning tomorrow.
On Mon, Aug 24, 2015 at 8:28 PM, alexis GILLAIN <ila...@hotmail.com> wrote: > Hi, > > I just realized the article I mentioned is cited in the jira and not in > the code so I guess you didn't use this result. > > Do you plan to implement sequence with timestamp and gap constraint as in : > > https://people.mpi-inf.mpg.de/~rgemulla/publications/miliaraki13mg-fsm.pdf > > 2015-08-25 7:06 GMT+08:00 Feynman Liang <fli...@databricks.com>: > >> Hi Alexis, >> >> Unfortunately, both of the papers you referenced appear to be >> translations and are quite difficult to understand. We followed >> http://doi.org/10.1109/ICDE.2001.914830 when implementing PrefixSpan. >> Perhaps you can find the relevant lines in there so I can elaborate further? >> >> Feynman >> >> On Thu, Aug 20, 2015 at 9:07 AM, alexis GILLAIN <ila...@hotmail.com> >> wrote: >> >>> I want to use prefixspan so I had a look at the code and the cited paper >>> : "Distributed PrefixSpan Algorithm Based on MapReduce". >>> >>> There is a result in the paper I didn't really undertstand and I >>> could'nt find where it is used in the code. >>> >>> Suppose a sequence database S = {1,2...n}, a sequence <a...> is a >>> length-(L-1) (2≤L≤n) sequential pattern, in projected databases which is a >>> prefix of a length-(L-1) sequential pattern <a...a>, when the support count >>> of <a> is not less than min_support, it is equal to obtaining a length-L >>> sequential pattern < a ... a > from projected databases that obtaining a >>> length-L sequential pattern < a ... a > from a sequence database S. >>> >>> According to the paper It's supposed to add a pruning step in the reduce >>> function but I couldn't find where. >>> >>> This result seems to come from a previous paper : "Wang Linlin, Fan Jun. >>> Improved Algorithm for Sequential Pattern Mining Based on PrefixSpan [J]. >>> Computer Engineering, 2009, 35(23): 56-61" but it didn't help me to >>> understand it and how it can improve the algorithm. >>> >> >> >
