Please use this identifier to cite or link to this item: https://doi.org/10.1145/1376616.1376647
Title: Just-in-time query retrieval over partially indexed data on structured P2P overlays
Authors: Wu, S. 
Li, J.
Ooi, B.C. 
Tan, K.-L. 
Keywords: BATON
CAN
Just-in-time
Partial indexing
Peer-to-peer
Sampling
Self-tuning
Issue Date: 2008
Source: Wu, S.,Li, J.,Ooi, B.C.,Tan, K.-L. (2008). Just-in-time query retrieval over partially indexed data on structured P2P overlays. Proceedings of the ACM SIGMOD International Conference on Management of Data : 279-290. ScholarBank@NUS Repository. https://doi.org/10.1145/1376616.1376647
Abstract: Structured peer-to-peer (P2P) overlays have been successfully employed in many applications to locate content. However, they have been less effective in handling massive amounts of data because of the high overhead of maintaining indexes. In this paper, we propose PISCES, a Peer-based system that Indexes Selected Content for Efficient Search. Unlike traditional approaches that index all data, PISCES identifies a subset of tuples to index based on some criteria (such as query frequency, update frequency, index cost, etc.). In addition, a coarse-grained range index is built to facilitate the processing of queries that cannot be fully answered by the tuple-level index. More importantly, PISCES can adaptively self-tune to optimize the subset of tuples to be indexed. That is, the (partial) index in PISCES is built in a Just-In-Time (JIT) manner. Beneficial tuples for current users are pulled for indexing while indexed tuples with infrequent access and high maintenance cost are discarded. We also introduce a light-weight monitoring scheme for structured networks to collect the necessary statistics. We have conducted an extensive experimental study on PlanetLab to illustrate the feasibility, practicality and efficiency of PISCES. The results show that PISCES incurs lower maintenance cost and offers better search and query efficiency compared to existing methods. Copyright 2008 ACM.
Source Title: Proceedings of the ACM SIGMOD International Conference on Management of Data
URI: http://scholarbank.nus.edu.sg/handle/10635/42019
ISBN: 9781605581026
ISSN: 07308078
DOI: 10.1145/1376616.1376647
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

19
checked on Dec 11, 2017

Page view(s)

54
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.