BibSLEIGH — Peter_J

Travelled to:

1 × Australia
1 × Austria
1 × France
1 × Germany
1 × Korea
1 × Norway
1 × Switzerland
15 × USA
2 × China
4 × Canada

Collaborated with:

V.Markl R.Gemulla J.F.Naughton A.N.Swami P.Brown Y.Sismanis J.M.Hellerstein W.Lehner K.S.Beyer G.M.Lohman A.Aboulnaga F.Xu B.Chen P.Scheuermann ∅ L.L.Perez C.M.Jermaine C.Koenig S.Seshadri V.Raman E.J.Shekita B.Reinwald I.F.Ilyas M.Kutsch T.M.Tran N.Megiddo R.Jampani F.Hueske R.Agrawal J.Kiernan H.J.Wang I.Popivanov E.Nijkamp P.P.Maglio P.G.Selinger W.C.Tan E.Michelakis R.Krishnamurthy S.Vaithyanathan G.Luo C.J.Ellmann V.Poosala Y.E.Ioannidis L.Stokes V.Ercegovac L.Qiao F.Reiss N.Zhang V.Josifovski C.Zhang P.G.Brown H.Brönnimann M.Dash Z.Cai Z.Vagena S.Arumugam S.Das J.McPherson S.Arumugam M.Wu U.Srivastava M.Kandil A.Lerner D.C.Zilio S.Lightstone A.D.Wilkins B.J.Bachman J.J.Labrie S.Regenbogen C.R.Pickering L.Kato A.M.Lisewski A.Lelescu Y.Chen L.A.Donehower W.S.Spangler O.Lichtarge M.Nagarajan T.Dayaram A.Comer J.N.Myers I.Stanoi N.Parikh M.Nagarajan I.B.Novikov S.Bao M.E.Terrón-Díaz S.Bhatia A.K.Adikesavan C.M.Buchovecky H.Zhang S.Boyer G.Weber

Talks about:

estim (8) sampl (7) data (6) base (5) discoveri (4) statist (4) databas (4) select (4) join (4) automat (3)

Person: Peter J. Haas

DBLP: Haas:Peter_J=

Contributed to:

2015

2014

2013

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

1999

1997

1996

1995

1994

1993

1992

Wrote 37 papers:

KDD-2015-NagarajanWBNBHT #analysis #predict: Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature (MN, ADW, BJB, IBN, SB, PJH, METD, SB, AKA, JJL, SR, CMB, CRP, LK, AML, AL, HZ, SB, GW, YC, LAD, WSS, OL), pp. 2019–2028.
KDD-2014-SpanglerWBNDHRPCMSKLLPLDCL #automation #generative #mining: Automated hypothesis generation based on mining scientific literature (WSS, ADW, BJB, MN, TD, PJH, SR, CRP, AC, JNM, IS, LK, AL, JJL, NP, AML, LAD, YC, OL), pp. 1877–1886.
PODS-2014-Haas #challenge #ecosystem #roadmap #tool support: Model-data Ecosystems: challenges, tools, and trends (PJH), pp. 76–87.
SIGMOD-2013-CaiVPAHJ #markov #simulation #using: Simulation of database-valued markov chains using SimSQL (ZC, ZV, LLP, SA, PJH, CMJ), pp. 637–648.
KDD-2011-GemullaNHS #distributed #matrix #probability #scalability: Large-scale matrix factorization with distributed stochastic gradient descent (RG, EN, PJH, YS), pp. 69–77.
VLDB-2011-HaasMST #modelling: Data is Dead... Without What-If Models (PJH, PPM, PGS, WCT), pp. 1486–1489.
SIGMOD-2010-DasSBGHM #named: Ricardo: integrating R and Hadoop (SD, YS, KSB, RG, PJH, JM), pp. 987–998.
VLDB-2010-HaasJAXPJ #analysis #database #named: MCDB-R: Risk Analysis in the Database (SA, RJ, LLP, FX, CMJ, PJH), pp. 782–793.
SIGMOD-2009-MichelakisKHV #information management #nondeterminism #rule-based: Uncertainty management in rule-based information extraction systems (EM, RK, PJH, SV), pp. 101–114.
SIGMOD-2009-XuBEHS #clustering #enterprise #nondeterminism: E = MC3: managing uncertain enterprise data in a cluster-computing environment (FX, KSB, VE, PJH, EJS), pp. 441–454.
SIGMOD-2008-JampaniXWPJH #approach #monte carlo #named #nondeterminism: MCDB: a monte carlo approach to managing uncertain data (RJ, FX, MW, LLP, CMJ, PJH), pp. 687–700.
VLDB-2008-QiaoRRHL #in memory #manycore: Main-memory scan sharing for multi-core CPUs (LQ, VR, FR, PJH, GML), pp. 610–621.
PODS-2007-GemullaLH #evolution #maintenance #multi: Maintaining bernoulli samples over evolving multisets (RG, WL, PJH), pp. 93–102.
SIGMOD-2007-BeyerHRSG #estimation #multi #on the: On synopses for distinct-value estimation under multiset operations (KSB, PJH, BR, YS, RG), pp. 199–210.
VLDB-2007-HaasHM #dependence #detection #feedback #query: Detecting Attribute Dependencies from Query Feedback (PJH, FH, VM), pp. 830–841.
SIGMOD-2006-MarklKTHM #consistency #estimation #named: MAXENT: consistent cardinality estimation in action (VM, MK, TMT, PJH, NM), pp. 775–777.
VLDB-2006-GemullaLH #dataset #evolution #maintenance: A Dip in the Reservoir: Maintaining Sample Synopses of Evolving Datasets (RG, WL, PJH), pp. 595–606.
VLDB-2006-SismanisBHR #named #performance #scalability: GORDIAN: Efficient and Scalable Discovery of Composite Keys (YS, PB, PJH, BR), pp. 691–702.
SIGMOD-2005-HaasKLMPRZ #automation #statistics: Automated statistics collection in action (PJH, MK, AL, VM, IP, VR, DCZ), pp. 933–935.
VLDB-2005-MarklMKTHS: Consistently Estimating the Selectivity of Conjuncts of Predicates (VM, NM, MK, TMT, PJH, US), pp. 373–384.
VLDB-2005-ZhangHJLZ #cost analysis #learning #query #statistics #xml: Statistical Learning Techniques for Costing XML Queries (NZ, PJH, VJ, GML, CZ), pp. 289–300.
SIGMOD-2004-HaasK #database: A Bi-Level Bernoulli Scheme for Database Sampling (PJH, CK), pp. 275–286.
SIGMOD-2004-IlyasMHBA #automation #correlation #dependence #functional #named: CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies (IFI, VM, PJH, PB, AA), pp. 647–658.
VLDB-2004-AboulnagaHLLMPR #automation #statistics: Automated Statistics Collection in DB2 UDB (AA, PJH, SL, GML, VM, IP, VR), pp. 1146–1157.
VLDB-2004-IlyasMHBA #automation #correlation #generative #named #statistics: CORDS: Automatic Generation of Correlation Statistics in DB2 (IFI, VM, PJH, PGB, AA), pp. 1341–1344.
KDD-2003-BronnimannCDHS #performance #reduction: Efficient data reduction with EASE (HB, BC, MD, PJH, PS), pp. 59–68.
SIGMOD-2003-AgrawalK #database #relational: A System for Watermarking Relational Databases (RA, PJH, JK), p. 674.
VLDB-2003-HaasB #algebra #automation #constraints #fuzzy #named #relational: BHUNT: Automatic Discovery of Fuzzy Algebraic Constraints in Relational Data (PB, PJH), pp. 668–679.
KDD-2002-ChenHS #algorithm: A new two-phase sampling based algorithm for discovering association rules (BC, PJH, PS), pp. 462–468.
SIGMOD-2002-LuoEHN #algorithm #scalability: A scalable hash ripple join algorithm (GL, CJE, PJH, JFN), pp. 252–262.
SIGMOD-1999-HaasH #online: Ripple Joins for Online Aggregation (PJH, JMH), pp. 287–298.
SIGMOD-1997-HellersteinHW #online: Online Aggregation (JMH, PJH, HJW), pp. 171–182.
SIGMOD-1996-PoosalaIHS #estimation: Improved Histograms for Selectivity Estimation of Range Predicates (VP, YEI, PJH, EJS), pp. 294–305.
VLDB-1995-HaasNSS #estimation: Sampling-Based Estimation of the Number of Distinct Values of an Attribute (PJH, JFN, SS, LS), pp. 311–322.
PODS-1994-HaasNS #estimation #on the: On the Relative Cost of Sampling for Join Selectivity Estimation (PJH, JFN, ANS), pp. 14–24.
PODS-1993-HaasNSS #estimation: Fixed-Precision Estimation of Join Selectivity (PJH, JFN, SS, ANS), pp. 190–201.
SIGMOD-1992-HaasS #estimation #query: Sequential Sampling Procedures for Query Size Estimation (PJH, ANS), pp. 341–350.