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: Gibbons:Phillip_B=
Contributed to:
2015
2014
20132012
20112010
20102010
2008
20082005200520032003
2002200220012000
1999199919991998199719961986Wrote 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.