Travelled to:1 × Germany
1 × Korea
1 × Norway
13 × USA
2 × Australia
2 × China
2 × France
2 × Greece
3 × Canada
Collaborated with:S.Muthukrishnan D.Srivastava M.N.Garofalakis A.McGregor F.Korn ∅ K.Yi N.G.Duffield A.Chakrabarti C.M.Procopiuc A.Wirth A.Deligiannakis P.Indyk M.Hadjieleftheriou J.Zhang X.Xiao J.Thaler E.Cohen H.J.Karloff S.Tirthapura R.Keralapura J.Ramamirtham I.Rozenbaum S.C.Sahinalp S.Papadopoulos L.Wang G.Luo Q.Zhang N.Li T.Li R.Berinde M.J.Strauss S.Bhagat B.Krishnamurthy T.Yu Q.Zhang R.Rastogi M.Datar G.Özsoyoglu N.H.Balkir Z.M.Özsoyoglu K.J.Ahn S.Guha X.He A.Machanavajjhala P.K.Agarwal Z.Huang J.M.Phillips Z.Wei L.Golab X.Zhang T.Johnson O.Spatscheck
Talks about:stream (20) data (20) distribut (6) anonym (5) track (4) sampl (4) use (4) probabilist (3) algorithm (3) hierarch (3)
Person: Graham Cormode
DBLP: Cormode:Graham
Contributed to:
2015
2015
2015
2015
2014201420132012201120112011
201020102010
20092009200920092008
20082008200720062006200620052005200520042003200320022001
2000Wrote 42 papers:
- ICML-2015-AhnCGMW #clustering #correlation #data type
- Correlation Clustering in Data Streams (KJA, GC, SG, AM, AW), pp. 2237–2246.
- PODS-2015-Cormode #dataset #scalability #summary
- Compact Summaries over Large Datasets (GC), pp. 157–158.
- SIGMOD-2015-ZhangCPSX #graph #statistics #using
- Private Release of Graph Statistics using Ladder Functions (JZ, GC, CMP, DS, XX), pp. 731–745.
- VLDB-2015-HeCMPS #named #synthesis #using
- DPT: Differentially Private Trajectory Synthesis Using Hierarchical Reference Systems (XH, GC, AM, CMP, DS), pp. 1154–1165.
- KDD-2014-CormodeD #big data #tutorial
- Sampling for big data: a tutorial (GC, NGD), p. 1975.
- SIGMOD-2014-ZhangCPSX #named #network
- PrivBayes: private data release via bayesian networks (JZ, GC, CMP, DS, XX), pp. 1423–1434.
- SIGMOD-2013-PapadopoulosCDG #algebra #authentication #data type #lightweight #linear #query
- Lightweight authentication of linear algebraic queries on data streams (SP, GC, AD, MNG), pp. 881–892.
- SIGMOD-2013-WangLYC #case study #data type
- Quantiles over data streams: an experimental study (LW, GL, KY, GC), pp. 737–748.
- PODS-2012-AgarwalCHPWY #summary
- Mergeable summaries (PKA, GC, ZH, JMP, ZW, KY), pp. 23–34.
- VLDB-2012-CormodeTY11 #interactive #proving #streaming #verification
- Verifying Computations with Streaming Interactive Proofs (GC, JT, KY), pp. 25–36.
- KDD-2011-Cormode #learning #privacy
- Personal privacy vs population privacy: learning to attack anonymization (GC), pp. 1253–1261.
- VLDB-2011-CohenCD #flexibility #summary
- Structure-Aware Sampling: Flexible and Accurate Summarization (EC, GC, NGD), pp. 819–830.
- CIKM-2010-CormodeKW #algorithm #dataset #scalability #set
- Set cover algorithms for very large datasets (GC, HJK, AW), pp. 479–488.
- PODS-2010-CormodeMYZ #distributed
- Optimal sampling from distributed streams (GC, SM, KY, QZ), pp. 77–86.
- VLDB-2010-CormodeLLS
- Minimizing Minimality and Maximizing Utility: Analyzing Method-based attacks on Anonymized Data (GC, NL, TL, DS), pp. 1045–1056.
- ICALP-v1-2009-ChakrabartiCM #data type
- Annotations in Data Streams (AC, GC, AM), pp. 222–234.
- PODS-2009-BerindeCIS #bound #fault
- Space-optimal heavy hitters with strong error bounds (RB, GC, PI, MJS), pp. 157–166.
- SIGMOD-2009-CormodeGKMSZ #dependence #functional
- Estimating the confidence of conditional functional dependencies (GC, LG, FK, AM, DS, XZ), pp. 469–482.
- SIGMOD-2009-CormodeS #generative #modelling
- Anonymized data: generation, models, usage (GC, DS), pp. 1015–1018.
- VLDB-2009-CormodeDGM #probability
- Probabilistic Histograms for Probabilistic Data (GC, AD, MNG, AM), pp. 526–537.
- VLDB-2009-CormodeSBK #graph #network #social
- Class-based graph anonymization for social network data (GC, DS, SB, BK), pp. 766–777.
- PODS-2008-CormodeKT
- Time-decaying aggregates in out-of-order streams (GC, FK, ST), pp. 89–98.
- PODS-2008-CormodeM #algorithm #approximate #clustering #nondeterminism
- Approximation algorithms for clustering uncertain data (GC, AM), pp. 191–200.
- STOC-2008-ChakrabartiCM #bound #communication #robust
- Robust lower bounds for communication and stream computation (AC, GC, AM), pp. 641–650.
- VLDB-2008-CormodeH #data type
- Finding frequent items in data streams (GC, MH), pp. 1530–1541.
- VLDB-2008-CormodeSYZ #graph #using
- Anonymizing bipartite graph data using safe groupings (GC, DS, TY, QZ), pp. 833–844.
- SIGMOD-2007-CormodeG #data type #probability #sketching
- Sketching probabilistic data streams (GC, MNG), pp. 281–292.
- SIGMOD-2007-CormodeG07a #data type #distributed #query #streaming
- Streaming in a connected world: querying and tracking distributed data streams (GC, MNG), pp. 1178–1181.
- PODS-2006-CormodeKMS #algorithm #data type
- Space- and time-efficient deterministic algorithms for biased quantiles over data streams (GC, FK, SM, DS), pp. 263–272.
- SIGMOD-2006-KeralapuraCR #distributed #monitoring
- Communication-efficient distributed monitoring of thresholded counts (RK, GC, JR), pp. 289–300.
- VLDB-2006-CormodeG #streaming
- Streaming in a Connected World (GC, MNG), p. 1266.
- PODS-2005-CormodeM #mining #multi #performance
- Space efficient mining of multigraph streams (GC, SM), pp. 271–282.
- SIGMOD-2005-CormodeGMR #approximate #distributed
- Holistic Aggregates in a Networked World: Distributed Tracking of Approximate Quantiles (GC, MNG, SM, RR), pp. 25–36.
- VLDB-2005-CormodeG #approximate #distributed #query #sketching
- Sketching Streams Through the Net: Distributed Approximate Query Tracking (GC, MNG), pp. 13–24.
- VLDB-2005-CormodeMR #data type #mining
- Summarizing and Mining Inverse Distributions on Data Streams via Dynamic Inverse Sampling (GC, SM, IR), pp. 25–36.
- SIGMOD-2004-CormodeKMS #multi
- Diamond in the Rough: Finding Hierarchical Heavy Hitters in Multi-Dimensional Data (GC, FK, SM, DS), pp. 155–166.
- SIGMOD-2004-JohnsonCKMSS #streaming
- Holistic UDAFs at streaming speeds (GC, TJ, FK, SM, OS, DS), pp. 35–46.
- PODS-2003-CormodeM #what
- What’s hot and what’s not: tracking most frequent items dynamically (GC, SM), pp. 296–306.
- VLDB-2003-CormodeKMS #data type
- Finding Hierarchical Heavy Hitters in Data Streams (GC, FK, SM, DS), pp. 464–475.
- VLDB-2002-CormodeDIM #data type #how #using
- Comparing Data Streams Using Hamming Norms (How to Zero In) (GC, MD, PI, SM), pp. 335–345.
- ICALP-2001-CormodeMS #editing #permutation
- Permutation Editing and Matching via Embeddings (GC, SM, SCS), pp. 481–492.
- ADL-2000-OzsoyogluBCO #library
- Electronic Books in Digital Libraries (GÖ, NHB, GC, ZMÖ), pp. 5–14.