TY - GEN
T1 - ASAP
T2 - 13th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2018
AU - Iyer, Anand Padmanabha
AU - Liu, Zaoxing
AU - Jin, Xin
AU - Venkataraman, Shivaram
AU - Braverman, Vladimir
AU - Stoica, Ion
N1 - Publisher Copyright:
© Proceedings of NSDI 2010: 7th USENIX Symposium on Networked Systems Design and Implementation. All rights reserved.
PY - 2007/1/1
Y1 - 2007/1/1
N2 - While there has been a tremendous interest in processing data that has an underlying graph structure, existing distributed graph processing systems take several minutes or even hours to mine simple patterns on graphs. This paper presents ASAP, a fast, approximate computation engine for graph pattern mining. ASAP leverages state-of-the-art results in graph approximation theory, and extends it to general graph patterns in distributed settings. To enable the users to navigate the tradeoff between the result accuracy and latency, we propose a novel approach to build the Error-Latency Profile (ELP) for a given computation. We have implemented ASAP on a general-purpose distributed dataflow platform and evaluated it extensively on several graph patterns. Our experimental results show that ASAP outperforms existing exact pattern mining solutions by up to 77×. Further, ASAP can scale to graphs with billions of edges without the need for large clusters.
AB - While there has been a tremendous interest in processing data that has an underlying graph structure, existing distributed graph processing systems take several minutes or even hours to mine simple patterns on graphs. This paper presents ASAP, a fast, approximate computation engine for graph pattern mining. ASAP leverages state-of-the-art results in graph approximation theory, and extends it to general graph patterns in distributed settings. To enable the users to navigate the tradeoff between the result accuracy and latency, we propose a novel approach to build the Error-Latency Profile (ELP) for a given computation. We have implemented ASAP on a general-purpose distributed dataflow platform and evaluated it extensively on several graph patterns. Our experimental results show that ASAP outperforms existing exact pattern mining solutions by up to 77×. Further, ASAP can scale to graphs with billions of edges without the need for large clusters.
UR - https://www.scopus.com/pages/publications/85074462709
M3 - Conference contribution
AN - SCOPUS:85074462709
T3 - Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2018
SP - 745
EP - 761
BT - Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2018
PB - USENIX Association
Y2 - 8 October 2018 through 10 October 2018
ER -