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Travelled to:
1 × China
1 × Germany
1 × Italy
1 × Norway
1 × United Kingdom
12 × USA
2 × Greece
4 × Canada
Collaborated with:
S.Nath S.Chen V.Poosala T.C.Mowry Y.Matias S.Acharya M.N.Garofalakis G.E.Blelloch M.A.Kozuch A.Deshpande S.Seshan O.Ruwase J.Shun J.T.Fineman S.Ramaswamy S.S.Muchnick A.Manjhi C.R.Palmer C.Faloutsos M.L.Goodstein E.Vlachos D.Spoonhower R.Harper A.Ailamaki G.Valentin N.Alon M.Szegedy S.Ganguly A.Silberschatz M.Athanassoulis A.Ailamaki R.Stoica Y.Ke B.Karp G.Pekhimenko T.Huberty R.Cai O.Mutlu B.Falsafi
Talks about:
queri (8) approxim (7) join (7) answer (6) sampl (5) parallel (4) fast (4) sensor (3) effici (3) onlin (3)

Person: Phillip B. Gibbons

DBLP DBLP: Gibbons:Phillip_B=

Contributed to:

HPCA 20152015
ASPLOS 20142014
PPoPP 20132013
PPoPP 20122012
SIGMOD 20112011
ASPLOS 20102010
PLDI 20102010
SIGMOD 20102010
ICFP 20082008
VLDB 20082008
SIGMOD 20052005
VLDB 20052005
SIGMOD 20032003
VLDB 20032003
KDD 20022002
SIGMOD 20022002
VLDB 20012001
SIGMOD 20002000
PODS 19991999
SIGMOD 19991999
VLDB 19991999
SIGMOD 19981998
VLDB 19971997
SIGMOD 19961996
Best of PLDI 20041986

Wrote 30 papers:

HPCA-2015-PekhimenkoHCMGK #reuse
Exploiting compressed block size as an indicator of future reuse (GP, TH, RC, OM, PBG, MAK, TCM), pp. 51–63.
ASPLOS-2014-RuwaseKGM #approach #hardware #named
Guardrail: a high fidelity approach to protecting hardware devices from buggy drivers (OR, MAK, PBG, TCM), pp. 655–670.
PPoPP-2013-ShunBFG
Reducing contention through priority updates (JS, GEB, JTF, PBG), pp. 299–300.
PPoPP-2012-BlellochFGS #algorithm #parallel #performance
Internally deterministic parallel algorithms can be fast (GEB, JTF, PBG, JS), pp. 181–192.
SIGMOD-2011-AthanassoulisCAGS #named #online #performance
MaSM: efficient online updates in data warehouses (MA, SC, AA, PBG, RS), pp. 865–876.
ASPLOS-2010-GoodsteinVCGKM #adaptation #analysis #data flow #monitoring #parallel
Butterfly analysis: adapting dataflow analysis to dynamic parallel monitoring (MLG, EV, SC, PBG, MAK, TCM), pp. 257–270.
ASPLOS-2010-VlachosGKCFGM #monitoring #named #online #parallel #thread
ParaLog: enabling and accelerating online parallel monitoring of multithreaded applications (EV, MLG, MAK, SC, BF, PBG, TCM), pp. 271–284.
PLDI-2010-RuwaseCGM #correctness #optimisation #tool support
Decoupled lifeguards: enabling path optimizations for dynamic correctness checking tools (OR, SC, PBG, TCM), pp. 25–35.
SIGMOD-2010-ChenGN #named #statistics
PR-join: a non-blocking join achieving higher early result rate with statistical guarantees (SC, PBG, SN), pp. 147–158.
ICFP-2008-SpoonhowerBHG #functional #parallel #profiling #source code
Space profiling for parallel functional programs (DS, GEB, RH, PBG), pp. 253–264.
VLDB-2008-NathG #maintenance #online #random #scalability
Online maintenance of very large random samples on flash storage (SN, PBG), pp. 970–983.
SIGMOD-2005-ManjhiNG #network #performance #robust
Tributaries and Deltas: Efficient and Robust Aggregation in Sensor Network Streams (AM, SN, PBG), pp. 287–298.
VLDB-2005-ChenAGM
Inspector Joins (SC, AA, PBG, TCM), pp. 817–828.
SIGMOD-2003-DeshpandeNGS #database
Cache-and-Query for Wide Area Sensor Databases (AD, SN, PBG, SS), pp. 503–514.
SIGMOD-2003-DeshpandeNGS03a #named
IrisNet: Internet-scale Resource-Intensive Sensor Services (AD, SN, PBG, SS), p. 667.
VLDB-2003-NathDKGKS #architecture #named
IrisNet: An Architecture for Internet-scale Sensing Services (SN, AD, YK, PBG, BK, SS), pp. 1137–1140.
KDD-2002-PalmerGF #data mining #graph #mining #named #performance #scalability
ANF: a fast and scalable tool for data mining in massive graphs (CRP, PBG, CF), pp. 81–90.
SIGMOD-2002-ChenGMV #optimisation #performance
Fractal prefetching B±Trees: optimizing both cache and disk performance (SC, PBG, TCM, GV), pp. 157–168.
SIGMOD-2002-GarofalakisG #fault
Wavelet synopses with error guarantees (MNG, PBG), pp. 476–487.
VLDB-2001-GarofalakisG #approximate #query
Approximate Query Processing: Taming the TeraBytes (MNG, PBG).
VLDB-2001-Gibbons #query
Distinct Sampling for Highly-Accurate Answers to Distinct Values Queries and Event Reports (PBG), pp. 541–550.
SIGMOD-2000-AcharyaGP #approximate #query
Congressional Samples for Approximate Answering of Group-By Queries (SA, PBG, VP), pp. 487–498.
PODS-1999-AlonGMS #self
Tracking Join and Self-Join Sizes in Limited Storage (NA, PBG, YM, MS), pp. 10–20.
SIGMOD-1999-AcharyaGPR #approximate #query
The Aqua Approximate Query Answering System (SA, PBG, VP, SR), pp. 574–576.
SIGMOD-1999-AcharyaGPR99a #approximate #query
Join Synopses for Approximate Query Answering (SA, PBG, VP, SR), pp. 275–286.
VLDB-1999-AcharyaGP #approximate #named #performance #query #using
Aqua: A Fast Decision Support Systems Using Approximate Query Answers (SA, PBG, VP), pp. 754–757.
SIGMOD-1998-GibbonsM #approximate #query #statistics #summary
New Sampling-Based Summary Statistics for Improving Approximate Query Answers (PBG, YM), pp. 331–342.
VLDB-1997-GibbonsMP #approximate #incremental #maintenance #performance
Fast Incremental Maintenance of Approximate Histograms (PBG, YM, VP), pp. 466–475.
SIGMOD-1996-GangulyGMS #estimation
Bifocal Sampling for Skew-Resistant Join Size Estimation (SG, PBG, YM, AS), pp. 271–281.
Best-of-PLDI-1986-MuchnickG #architecture #performance #pipes and filters #scheduling
Efficient instruction scheduling for a pipelined architecture (with retrospective) (SSM, PBG), pp. 167–174.

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