BibSLEIGH — S_Muthukrishnan

Travelled to:

1 × Egypt
1 × France
1 × Iceland
1 × Israel
1 × Norway
1 × Poland
1 × Spain
18 × USA
2 × Canada
2 × China
2 × Germany
2 × Greece
2 × Italy
2 × United Kingdom

Collaborated with:

G.Cormode F.Korn D.Srivastava N.Koudas ∅ M.Strauss P.Indyk A.C.Gilbert S.C.Sahinalp G.Franceschini T.Johnson Y.Kotidis A.Nikolov I.Rozenbaum Y.Zhu H.V.Jagadish S.Guha P.Ferragina V.Shkapenyuk M.Farach R.Hariharan K.V.Palem O.Spatscheck T.Suel Y.Wu S.More E.A.M.Shriver M.d.Berg S.Khanna S.Skiena M.O.Rabin Y.Mansour M.Yung D.J.Mir R.N.Wright K.Yi Q.Zhang T.S.Jayram A.McGregor E.Vee M.N.Garofalakis R.Rastogi T.Dasu M.Datar Z.Chen M.Paterson L.Gravano P.G.Ipeirotis V.Poosala K.C.Sevcik B.Ghosh F.T.Leighton B.M.Maggs C.G.Plaxton R.Rajaraman A.W.Richa R.E.Tarjan D.Zuckerman

Talks about:

data (15) stream (14) optim (9) algorithm (7) approxim (6) aggreg (6) effici (5) mine (5) histogram (4) network (4)

Person: S. Muthukrishnan

DBLP: Muthukrishnan:S=

Contributed to:

2012

2011

2010

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

Wrote 50 papers:

ICALP-v1-2012-RabinMMY #performance #strict #transaction #validation: Strictly-Black-Box Zero-Knowledge and Efficient Validation of Financial Transactions (MOR, YM, SM, MY), pp. 738–749.
STOC-2012-MuthukrishnanN: Optimal private halfspace counting via discrepancy (SM, AN), pp. 1285–1292.
PODS-2011-MirMNW #algorithm #sketching #statistics: Pan-private algorithms via statistics on sketches (DJM, SM, AN, RNW), pp. 37–48.
PODS-2011-Muthukrishnan #data type: Theory of data stream computing: where to go (SM), pp. 317–319.
PODS-2010-CormodeMYZ #distributed: Optimal sampling from distributed streams (GC, SM, KY, QZ), pp. 77–86.
VLDB-2010-Muthukrishnan #data transformation #internet #mining: Data Management and Mining in Internet Ad Systems (SM), pp. 1655–1656.
ICALP-A-2008-Muthukrishnan #internet: Internet Ad Auctions: Insights and Directions (SM), pp. 14–23.
ICALP-2007-FranceschiniM #sorting: In-Place Suffix Sorting (GF, SM), pp. 533–545.
PODS-2007-JayramMMV #data type #probability #statistics: Estimating statistical aggregates on probabilistic data streams (TSJ, AM, SM, EV), pp. 243–252.
STOC-2007-FranceschiniM: Optimal suffix selection (GF, SM), pp. 328–337.
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-KornMW #data type #modelling: Modeling skew in data streams (FK, SM, YW), pp. 181–192.
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.
SIGMOD-2005-JohnsonMR #algorithm: Sampling Algorithms in a Stream Operator (TJ, SM, IR), pp. 1–12.
VLDB-2005-CormodeMR #data type #mining: Summarizing and Mining Inverse Distributions on Data Streams via Dynamic Inverse Sampling (GC, SM, IR), pp. 25–36.
VLDB-2005-JohnsonMSS: A Heartbeat Mechanism and Its Application in Gigascope (TJ, SM, VS, OS), pp. 1079–1088.
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.
SIGMOD-2003-KornMZ #correlation #named #network #visual notation: IPSOFACTO: A Visual Correlation Tool for Aggregate Network Traffic Data (FK, SM, YZ), p. 677.
VLDB-2003-CormodeKMS #data type: Finding Hierarchical Heavy Hitters in Data Streams (GC, FK, SM, DS), pp. 464–475.
VLDB-2003-KornMZ #database #monitoring #network #problem #quality: Checks and Balances: Monitoring Data Quality Problems in Network Traffic Databases (FK, SM, YZ), pp. 536–547.
ICALP-2002-GuhaIMS #data type #performance: Histogramming Data Streams with Fast Per-Item Processing (SG, PI, SM, MS), pp. 681–692.
SIGMOD-2002-DasuJMS #database #how #mining #quality: Mining database structure; or, how to build a data quality browser (TD, TJ, SM, VS), pp. 240–251.
STOC-2002-GilbertGIKMS #algorithm #approximate #maintenance #performance: Fast, small-space algorithms for approximate histogram maintenance (ACG, SG, PI, YK, SM, MS), pp. 389–398.
STOC-2002-GilbertGIMS #fourier: Near-optimal sparse fourier representations via sampling (ACG, SG, PI, SM, MS), pp. 152–161.
VLDB-2002-CormodeDIM #data type #how #using: Comparing Data Streams Using Hamming Norms (How to Zero In) (GC, MD, PI, SM), pp. 335–345.
VLDB-2002-GilbertKMS #how #maintenance: How to Summarize the Universe: Dynamic Maintenance of Quantiles (ACG, YK, SM, MS), pp. 454–465.
VLDB-2002-KornMS #data type #nearest neighbour: Reverse Nearest Neighbor Aggregates Over Data Streams (FK, SM, DS), pp. 814–825.
ICALP-2001-CormodeMS #editing #permutation: Permutation Editing and Matching via Embeddings (GC, SM, SCS), pp. 481–492.
PODS-2001-FerraginaKMS #2d #string: Two-dimensional Substring Indexing (PF, NK, SM, DS).
PODS-2001-GilbertKMS #approximate #statistics #summary: Optimal and Approximate Computation of Summary Statistics for Range Aggregates (ACG, YK, SM, MS).
VLDB-2001-GilbertKMS #approximate #query #summary: Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries (ACG, YK, SM, MS), pp. 79–88.
VLDB-2001-GravanoIJKMS #approximate #database #for free #string: Approximate String Joins in a Database (Almost) for Free (LG, PGI, HVJ, NK, SM, DS), pp. 491–500.
PODS-2000-ChenKKM #estimation #query: Selectivity Estimation for Boolean Queries (ZC, FK, NK, SM), pp. 216–225.
PODS-2000-KoudasMS #query: Optimal Histograms for Hierarchical Range Queries (NK, SM, DS), pp. 196–204.
SIGMOD-2000-KornM #nearest neighbour #query #set: Influence Sets Based on Reverse Nearest Neighbor Queries (FK, SM), pp. 201–212.
STOC-2000-MuthukrishnanS #approximate #comparison #nearest neighbour #sequence: Approximate nearest neighbors and sequence comparison with block operations (SM, SCS), pp. 416–424.
VLDB-2000-KoudasIM #identification #roadmap #set #sketching #using: Identifying Representative Trends in Massive Time Series Data Sets Using Sketches (PI, NK, SM), pp. 363–372.
PODS-1999-MoreMS #performance: Efficient Sequencing Tape-Resident Jobs (SM, SM, EAMS), pp. 33–43.
STOC-1999-FerraginaMB #approach #geometry #multi #problem #string: Multi-Method Dispatching: A Geometric Approach With Applications to String Matching Problems (PF, SM, MdB), pp. 483–491.
STOC-1999-MuthukrishnanPSS #grid #multi #network: Compact Grid Layouts of Multi-Level Networks (SM, MP, SCS, TS), pp. 455–463.
VLDB-1999-KoudasMJ #database #mining: Mining Deviants in a Time Series Database (HVJ, NK, SM), pp. 102–113.
VLDB-1998-JagadishKMPSS #quality: Optimal Histograms with Quality Guarantees (HVJ, NK, SM, VP, KCS, TS), pp. 275–286.
ICALP-1997-KhannaMS #array #clustering #performance: Efficient Array Partitioning (SK, SM, SS), pp. 616–626.
ICALP-1996-FarachM #random: Optimal Logarithmic Time Randomized Suffix Tree Construction (MF, SM), pp. 550–561.
STOC-1995-GhoshLMMPRRTZ #algorithm #analysis: Tight analyses of two local load balancing algorithms (BG, FTL, BMM, SM, CGP, RR, AWR, RET, DZ), pp. 548–558.
ICALP-1994-HariharanM #algorithm #parallel: Optimal Parallel Algorithms for Prefix Matching (RH, SM), pp. 203–214.
STOC-1994-MuthukrishnanP #algorithm #complexity #standard #string: Non-standard stringology: algorithms and complexity (SM, KVP), pp. 770–779.