- From: Kingsley Idehen <kidehen@openlinksw.com>
- Date: Mon, 24 Nov 2014 07:48:00 -0500
- To: public-hydra@w3.org
- Message-ID: <54732900.9020107@openlinksw.com>
On 11/24/14 3:30 AM, Ruben Verborgh wrote: >>> Hence, we are able to host them through the HDT compressed triple format, >>> which gives excellent performance for those cases, >>> far better than what I've seen any DBMS do. >> How can that be so? > Because HDT is much simpler than a DBMS; > it can only do read-only operations. Everything is static. Simple question: are you inferring that SPARQL query solutions against your HDT based static datatet are faster than Virtuoso against the same static dataset? I really need to know since this is the first time I am hearing you make such a claim. > > We have measured that HDT performs the combination of > “looking up data corresponding to a triple pattern” > plus “give an estimate count of the number of matching triples” > faster than Virtuoso or any other DBMS we tested. > For numbers, see http://linkeddatafragments.org/publications/ldow2014.pdf#page=7. Why a PDF? You should have a collection of SPARQL Protocol URLs to backup up your claim. Then we have something that we can respond to, with ease :) > >> As far as I know, you haven't made claims about trumping a SPARQL DBMS in the performance stakes. > I indeed did not. > > If you look at my original reply, out of which the above snippet was taken, > you will see it was in the context of triple pattern fragments (the “them” above). > I said that HDT is faster than all DBMSes I have tested > for serving triple pattern fragments—but an HDT file is static. BTW -- can you share with me, via a URL, an example of a multiple statement triple pattern fragment? > > Best, > > Ruben > -- Regards, Kingsley Idehen Founder & CEO OpenLink Software Company Web: http://www.openlinksw.com Personal Weblog 1: http://kidehen.blogspot.com Personal Weblog 2: http://www.openlinksw.com/blog/~kidehen Twitter Profile: https://twitter.com/kidehen Google+ Profile: https://plus.google.com/+KingsleyIdehen/about LinkedIn Profile: http://www.linkedin.com/in/kidehen Personal WebID: http://kingsley.idehen.net/dataspace/person/kidehen#this
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Received on Monday, 24 November 2014 12:48:21 UTC