[logicrime]

I kinda hate these papers that just humble-brag clusterized setups without providing any abstract insights. This doesn't bring me any closer to understanding graph data any better, but I'm now ready to begin installation of a multi-million-dollar cluster of machines and storage.

The bit about k-means was interesting, but the rest was an irrelevant bore.

[Hytosys]

Imagine being one of the five authors of this paper, browsing this comments section. It's natural to distance yourself from people when you're behind a keyboard, but for fuck's sake — these are your peers. If you're going to be critical, do it with attention and care.

[lcswi]

The people were not critized, the paper was.

[logicrime]

If you think the authors give half a shit what HackerNews think, you've missed the point.

[cbsmith]

The purpose of the paper isn't to help you to understand graph data better. Heck, this is a VLDB presentation.

It is to help you to understand frameworks for working on graph data better.

If you see it as a humble brag that is an irrelevant bore, you aren't the intended audience.

[nl]

Given than this is a paper for VLDB[1] (the conference on Very Large Databases) perhaps there is some small chance that your personal judgement of what is an insight or relevant is... wrong?

Also, your idea that it is a humble-brag to talk about a computer cluster at VLDB seems more indicative of a lack of knowledge on your part rather than a lack of "abstract insights".

Irrelevant bore indeed.

[1] http://www.vldb.org/2015/

[chimtim]

VLDB accepts 150 papers and SIGMOD perhaps another 150. This is just top tier. I am pretty sure science can live without about more than 50% of those papers.

I disagree with the tone of the original comment. But I do not disagree with the sentiment. Just having a large installation does not make it interesting. However, Google's large systems almost always push the boundaries of science -- MapReduce, GFS, Spanner, Distbelief and have been a joy to read.