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PANDA: toward partial topology-based search on large networks in a single machine

PANDA: toward partial topology-based search on large networks in a single machine A large body of research has focused on efficient and scalable processing of subgraph search queries on large networks. In these efforts, a query is posed in the form of a connected query graph. Unfortunately, in practice end users may not always have precise knowledge about the topological relationships between nodes in a query graph to formulate a connected query. In this paper, we present a novel graph querying paradigm called partial topology-based network search and propose a query processing framework called panda to efficiently process partial topology query (ptq) in a single machine. A ptq is a disconnected query graph containing multiple connected query components. ptqs allow an end user to formulate queries without demanding precise information about the complete topology of a query graph. To this end, we propose an exact and an approximate algorithm called sen-panda and po-panda, respectively, to generate top-k matches of a ptq. We also present a subgraph simulation-based optimization technique to further speedup the processing of ptqs. Using real-life networks with millions of nodes, we experimentally verify that our proposed algorithms are superior to several baseline techniques. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

PANDA: toward partial topology-based search on large networks in a single machine

Xie, Miao; Bhowmick, Sourav; Cong, Gao; Wang, Qing
The VLDB Journal , Volume 26 (2) – Nov 18, 2016
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References (9)

Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
DOI
10.1007/s00778-016-0447-0
Publisher site
See Article on Publisher Site

Abstract

A large body of research has focused on efficient and scalable processing of subgraph search queries on large networks. In these efforts, a query is posed in the form of a connected query graph. Unfortunately, in practice end users may not always have precise knowledge about the topological relationships between nodes in a query graph to formulate a connected query. In this paper, we present a novel graph querying paradigm called partial topology-based network search and propose a query processing framework called panda to efficiently process partial topology query (ptq) in a single machine. A ptq is a disconnected query graph containing multiple connected query components. ptqs allow an end user to formulate queries without demanding precise information about the complete topology of a query graph. To this end, we propose an exact and an approximate algorithm called sen-panda and po-panda, respectively, to generate top-k matches of a ptq. We also present a subgraph simulation-based optimization technique to further speedup the processing of ptqs. Using real-life networks with millions of nodes, we experimentally verify that our proposed algorithms are superior to several baseline techniques.

Journal

The VLDB Journal – Springer Journals

Published: Nov 18, 2016

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