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2015 ; 2015
(ä): 1105-1110
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Ringo: Interactive Graph Analytics on Big-Memory Machines
#MMPMID27081215
Perez Y
; Sosi? R
; Banerjee A
; Puttagunta R
; Raison M
; Shah P
; Leskovec J
Proc ACM SIGMOD Int Conf Manag Data
2015[May]; 2015
(ä): 1105-1110
PMID27081215
show ga
We present Ringo, a system for analysis of large graphs. Graphs provide a way to
represent and analyze systems of interacting objects (people, proteins, webpages)
with edges between the objects denoting interactions (friendships, physical
interactions, links). Mining graphs provides valuable insights about individual
objects as well as the relationships among them. In building Ringo, we take
advantage of the fact that machines with large memory and many cores are widely
available and also relatively affordable. This allows us to build an easy-to-use
interactive high-performance graph analytics system. Graphs also need to be built
from input data, which often resides in the form of relational tables. Thus,
Ringo provides rich functionality for manipulating raw input data tables into
various kinds of graphs. Furthermore, Ringo also provides over 200 graph
analytics functions that can then be applied to constructed graphs. We show that
a single big-memory machine provides a very attractive platform for performing
analytics on all but the largest graphs as it offers excellent performance and
ease of use as compared to alternative approaches. With Ringo, we also
demonstrate how to integrate graph analytics with an iterative process of
trial-and-error data exploration and rapid experimentation, common in data mining
workloads.