Peter J. Haas

ORCID

Publication Activity (10 Years)

Years Active: 1985-2024
Publications (10 Years): 33

Top Venues

CoRR

Proc. VLDB Endow.

ACM Trans. Model. Comput. Simul.

WSC

Publications

Anh L. Mai, Pengyu Wang, Azza Abouzied, Matteo Brucato, Peter J. Haas, Alexandra Meliou
Scaling Package Queries to a Billion Tuples via Hierarchical Partitioning and Customized Optimization. Proc. VLDB Endow. 17 (5) (2024)
Brian Hentschel, Peter J. Haas, Yuanyuan Tian
Exact PPS sampling with bounded sample size. Inf. Process. Lett. 182 (2023)
Pracheta Amaranath, Peter J. Haas, David D. Jensen, Sam Witty
Causal Dynamic Bayesian Networks for Simulation Metamodeling. WSC (2023)
Wang Cen, Peter J. Haas
Efficient Hybrid Simulation Optimization via Graph Neural Network Metamodeling. WSC (2023)
Anh L. Mai, Pengyu Wang, Azza Abouzied, Matteo Brucato, Peter J. Haas, Alexandra Meliou
Scaling Package Queries to a Billion Tuples via Hierarchical Partitioning and Customized Optimization. CoRR (2023)
Wang Cen, Peter J. Haas
NIM: Generative Neural Networks for Automated Modeling and Generation of Simulation Inputs. ACM Trans. Model. Comput. Simul. 33 (3) (2023)
Azza Abouzied, Peter J. Haas, Alexandra Meliou
In-Database Decision Support: Opportunities and Challenges. IEEE Data Eng. Bull. 45 (3) (2022)
Sneha Gathani, Zhicheng Liu, Peter J. Haas, Çagatay Demiralp
Predictive and Prescriptive Analytics in Business Decision Making: Needs and Concerns. CoRR (2022)
Sneha Gathani, Madelon Hulsebos, James Gale, Peter J. Haas, Çagatay Demiralp
Augmenting Decision Making via Interactive What-If Analysis. CIDR (2022)
Wang Cen, Peter J. Haas
Enhanced Simulation Metamodeling via Graph and Generative Neural Networks. WSC (2022)
Sneha Gathani, Madelon Hulsebos, James Gale, Peter J. Haas, Çagatay Demiralp
Augmenting Decision Making via Interactive What-If Analysis. CoRR (2021)
Matteo Brucato, Nishant Yadav, Azza Abouzied, Peter J. Haas, Alexandra Meliou
Stochastic Package Queries in Probabilistic Databases. CoRR (2021)
Peter J. Haas, Georgios Theodoropoulos
Introduction to the Special Issue for Towards an Ecosystem of Simulation Models and Data. ACM Trans. Model. Comput. Simul. 30 (4) (2020)
Wang Cen, Emily A. Herbert, Peter J. Haas
NIM: Modeling and Generation of Simulation Inputs Via Generative Neural Networks. WSC (2020)
Anna Fariha, Matteo Brucato, Peter J. Haas, Alexandra Meliou
SuDocu: Summarizing Documents by Example. Proc. VLDB Endow. 13 (12) (2020)
Matteo Brucato, Miro Mannino, Azza Abouzied, Peter J. Haas, Alexandra Meliou
sPaQLTooLs: A Stochastic Package Query Interface for Scalable Constrained Optimization. Proc. VLDB Endow. 13 (12) (2020)
Matteo Brucato, Nishant Yadav, Azza Abouzied, Peter J. Haas, Alexandra Meliou
Stochastic Package Queries in Probabilistic Databases. SIGMOD Conference (2020)
Brian Hentschel, Peter J. Haas, Yuanyuan Tian
General Temporally Biased Sampling Schemes for Online Model Management. ACM Trans. Database Syst. 44 (4) (2019)
Johanna Sommer, Matthias Boehm, Alexandre V. Evfimievski, Berthold Reinwald, Peter J. Haas
MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions. SIGMOD Conference (2019)
Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
Compressed linear algebra for declarative large-scale machine learning. Commun. ACM 62 (5) (2019)
Emily A. Herbert, Wang Cen, Peter J. Haas
NIM: generative neural networks for modeling and generation of simulation inputs. SummerSim (2019)
Brian Hentschel, Peter J. Haas, Yuanyuan Tian
Online Model Management via Temporally Biased Sampling. SIGMOD Rec. 48 (1) (2019)
Brian Hentschel, Peter J. Haas, Yuanyuan Tian
Temporally-Biased Sampling Schemes for Online Model Management. CoRR (2019)
Brian Hentschel, Peter J. Haas, Yuanyuan Tian
Temporally-Biased Sampling for Online Model Management. EDBT (2018)
Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
Compressed linear algebra for large-scale machine learning. VLDB J. 27 (5) (2018)
Yeounoh Chung, Peter J. Haas, Eli Upfal, Tim Kraska
Unknown Examples & Machine Learning Model Generalization. CoRR (2018)
Brian Hentschel, Peter J. Haas, Yuanyuan Tian
Temporally-Biased Sampling for Online Model Management. CoRR (2018)
Çagatay Demiralp, Peter J. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati
Foresight: Recommending Visual Insights. CoRR (2017)
Bum Chul Kwon, Janu Verma, Peter J. Haas, Çagatay Demiralp
Sampling for Scalable Visual Analytics. IEEE Computer Graphics and Applications 37 (1) (2017)
Çagatay Demiralp, Peter J. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati
Foresight: Rapid Data Exploration Through Guideposts. CoRR (2017)
Çagatay Demiralp, Peter J. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati
Foresight: Recommending Visual Insights. Proc. VLDB Endow. 10 (12) (2017)
Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
Scaling Machine Learning via Compressed Linear Algebra. SIGMOD Rec. 46 (1) (2017)
Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
Compressed Linear Algebra for Large-Scale Machine Learning. Proc. VLDB Endow. 9 (12) (2016)
Wenlei Xie, Yuanyuan Tian, Yannis Sismanis, Andrey Balmin, Peter J. Haas
Dynamic interaction graphs with probabilistic edge decay. ICDE (2015)
Peter W. Glynn, Peter J. Haas
On Transience and Recurrence in Irreducible Finite-State Stochastic Systems. ACM Trans. Model. Comput. Simul. 25 (4) (2015)
Liping Peng, Vuk Ercegovac, Kai Zeng, Peter J. Haas, Andrey Balmin, Yannis Sismanis
Groupwise analytics via adaptive MapReduce. ICDE (2015)
Peter W. Glynn, Peter J. Haas
Guest Editors' Introduction to Special Issue Honoring Donald L. Iglehart. ACM Trans. Model. Comput. Simul. 25 (4) (2015)
Faraz Makari, Christina Teflioudi, Rainer Gemulla, Peter J. Haas, Yannis Sismanis
Shared-memory and shared-nothing stochastic gradient descent algorithms for matrix completion. Knowl. Inf. Syst. 42 (3) (2015)
Meenakshi Nagarajan, Angela D. Wilkins, Benjamin J. Bachman, Ilya B. Novikov, Shenghua Bao, Peter J. Haas, María E. Terrón-Díaz, Sumit Bhatia, Anbu K. Adikesavan, Jacques J. Labrie, Sam Regenbogen, Christie M. Buchovecky, Curtis R. Pickering, Linda Kato, Andreas Martin Lisewski, Ana Lelescu, Houyin Zhang, Stephen Boyer, Griff Weber, Ying Chen, Lawrence A. Donehower, W. Scott Spangler, Olivier Lichtarge
Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature. KDD (2015)
Peter J. Haas, Shane G. Henderson, Pierre L'Ecuyer
Guest editors' introduction to special issue on the third INFORMS simulation society research workshop. ACM Trans. Model. Comput. Simul. 24 (1) (2014)
Peter J. Haas
Model-data Ecosystems: challenges, tools, and trends. PODS (2014)
W. Scott Spangler, Angela D. Wilkins, Benjamin J. Bachman, Meena Nagarajan, Tajhal Dayaram, Peter J. Haas, Sam Regenbogen, Curtis R. Pickering, Austin Comer, Jeffrey N. Myers, Ioana Stanoi, Linda Kato, Ana Lelescu, Jacques J. Labrie, Neha Parikh, Andreas Martin Lisewski, Lawrence A. Donehower, Ying Chen, Olivier Lichtarge
Automated hypothesis generation based on mining scientific literature. KDD (2014)
Peter J. Haas
Improving the efficiency of stochastic composite simulation models via result caching. WSC (2014)
Zhuhua Cai, Zografoula Vagena, Luis Leopoldo Perez, Subramanian Arumugam, Peter J. Haas, Christopher M. Jermaine
Simulation of database-valued markov chains using SimSQL. SIGMOD Conference (2013)
Russell C. H. Cheng, Stewart Robinson, Peter J. Haas, Lee Schruben, Theresa M. Roeder
Panel: Are we effectively preparing our students to be certified analytics professionals? WSC (2013)
Rainer Gemulla, Peter J. Haas, Wolfgang Lehner
Non-uniformity issues and workarounds in bounded-size sampling. VLDB J. 22 (6) (2013)
Mohamed Y. Eltabakh, Fatma Özcan, Yannis Sismanis, Peter J. Haas, Hamid Pirahesh, Jan Vondrák
Eagle-eyed elephant: split-oriented indexing in Hadoop. EDBT (2013)
Graham Cormode, Minos N. Garofalakis, Peter J. Haas, Chris Jermaine
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches. Found. Trends Databases 4 (1-3) (2012)
Wang Chiew Tan, Peter J. Haas, Ronald L. Mak, Cheryl A. Kieliszewski, Patricia G. Selinger, Paul P. Maglio, Susanne Glissmann, Melissa Cefkin, Yinan Li
Splash: a platform for analysis and simulation of health. IHI (2012)
Peter W. Glynn, Peter J. Haas
On simulation of non-Markovian stochastic Petri nets with heavy-tailed firing times. WSC (2012)
Peter J. Haas, Nicole C. Barberis, Piyaphol Phoungphol, Ignacio G. Terrizzano, Wang-Chiew Tan, Patricia G. Selinger, Paul P. Maglio
Splash: Simulation optimization in complex systems of systems. Allerton Conference (2012)
Niketan Pansare, Chris Jermaine, Peter J. Haas, Nitendra Rajput
Topic Models over Spoken Language. ICDM (2012)
Peter J. Haas
Sketches get sketchier. Commun. ACM 54 (8) (2011)
Rainer Gemulla, Erik Nijkamp, Peter J. Haas, Yannis Sismanis
Large-scale matrix factorization with distributed stochastic gradient descent. KDD (2011)
Peter J. Haas, Paul P. Maglio, Patricia G. Selinger, Wang Chiew Tan
Data is Dead... Without What-If Models. Proc. VLDB Endow. 4 (12) (2011)
Ravi Jampani, Fei Xu, Mingxi Wu, Luis Leopoldo Perez, Chris Jermaine, Peter J. Haas
The monte carlo database system: Stochastic analysis close to the data. ACM Trans. Database Syst. 36 (3) (2011)
Joseph P. Bigus, Murray Campbell, Boaz Carmeli, Melissa Cefkin, Henry Chang, Ching-Hua Chen-Ritzo, William F. Cody, Shahram Ebadollahi, Alexandre V. Evfimievski, Ariel Farkash, Susanne Glissmann, David Gotz, Tyrone Grandison, Daniel Gruhl, Peter J. Haas, Mark J. H. Hsiao, Pei-Yun Sabrina Hsueh, Jianying Hu, Joseph M. Jasinski, James H. Kaufman, Cheryl A. Kieliszewski, Martin S. Kohn, Sarah E. Knoop, Paul P. Maglio, Ronald L. Mak, Haim Nelken, Chalapathy Neti, Hani Neuvirth, Yue Pan, Yardena Peres, Sreeram Ramakrishnan, Michal Rosen-Zvi, Sondra R. Renly, Pat Selinger, Amnon Shabo, Robert Sorrentino, Jimeng Sun, Tanveer Fathima Syeda-Mahmood, Wang Chiew Tan, Ying Y. Y. Tao, Reza Yaesoubi, Xinxin Zhu
Information technology for healthcare transformation. IBM J. Res. Dev. 55 (5) (2011)
Paul P. Maglio, Melissa Cefkin, Peter J. Haas, Patricia G. Selinger
Social Factors in Creating an Integrated Capability for Health System Modeling and Simulation. SBP (2010)
Sudipto Das, Yannis Sismanis, Kevin S. Beyer, Rainer Gemulla, Peter J. Haas, John McPherson
Ricardo: integrating R and Hadoop. SIGMOD Conference (2010)
Peter J. Haas
From MUD to MIRE: Managing Inherent Risk in the Enterprise. MUD (2010)
Subi Arumugam, Ravi Jampani, Luis Leopoldo Perez, Fei Xu, Christopher M. Jermaine, Peter J. Haas
MCDB-R: Risk Analysis in the Database. Proc. VLDB Endow. 3 (1) (2010)
Peter J. Haas, Ihab F. Ilyas, Guy M. Lohman, Volker Markl
Discovering and Exploiting Statistical Properties for Query Optimization in Relational Databases: A Survey. Stat. Anal. Data Min. 1 (4) (2009)
Peter J. Haas, Dan Suciu
Special issue on uncertain and probabilistic databases. VLDB J. 18 (5) (2009)
Fei Xu, Kevin S. Beyer, Vuk Ercegovac, Peter J. Haas, Eugene J. Shekita
: managing uncertain enterprise data in a cluster-computing environment. SIGMOD Conference (2009)
Yannis Sismanis, Ling Wang, Ariel Fuxman, Peter J. Haas, Berthold Reinwald
Resolution-Aware Query Answering for Business Intelligence. ICDE (2009)
Kevin S. Beyer, Rainer Gemulla, Peter J. Haas, Berthold Reinwald, Yannis Sismanis
Distinct-value synopses for multiset operations. Commun. ACM 52 (10) (2009)
Eirinaios Michelakis, Rajasekar Krishnamurthy, Peter J. Haas, Shivakumar Vaithyanathan
Uncertainty management in rule-based information extraction systems. SIGMOD Conference (2009)
Anish Das Sarma, Ander de Keijzer, Amol Deshpande, Peter J. Haas, Ihab F. Ilyas, Christoph Koch, Thomas Neumann, Dan Olteanu, Martin Theobald, Vasilis Vassalos
08421 Working Group: Classification, Representation and Modeling. Uncertainty Management in Information Systems (2008)
Ravi Jampani, Fei Xu, Mingxi Wu, Luis Leopoldo Perez, Christopher M. Jermaine, Peter J. Haas
MCDB: a monte carlo approach to managing uncertain data. SIGMOD Conference (2008)
Rainer Gemulla, Wolfgang Lehner, Peter J. Haas
Maintaining bounded-size sample synopses of evolving datasets. VLDB J. 17 (2) (2008)
Lin Qiao, Vijayshankar Raman, Frederick Reiss, Peter J. Haas, Guy M. Lohman
Main-memory scan sharing for multi-core CPUs. Proc. VLDB Endow. 1 (1) (2008)
Christoph Koch, Peter J. Haas, Hans-Joachim Lenz, Dan Olteanu, Christopher Ré, Maurice van Keulen, Jeff Z. Pan
08421 Working Group: Report of the Probabilistic Databases Benchmarking. Uncertainty Management in Information Systems (2008)
Rainer Gemulla, Wolfgang Lehner, Peter J. Haas
On reservoir sampling with deletions. Monde des Util. Anal. Données 36 (2007)
Rainer Gemulla, Wolfgang Lehner, Peter J. Haas
Maintaining bernoulli samples over evolving multisets. PODS (2007)
Alexander Behm, Volker Markl, Peter J. Haas, Keshava Murthy
Integrating Query-Feedback Based Statistics into Informix Dynamic Server. BTW (2007)
Kevin S. Beyer, Peter J. Haas, Berthold Reinwald, Yannis Sismanis, Rainer Gemulla
On synopses for distinct-value estimation under multiset operations. SIGMOD Conference (2007)
Peter J. Haas, Fabian Hueske, Volker Markl
Detecting Attribute Dependencies from Query Feedback. VLDB (2007)
Volker Markl, Peter J. Haas, Marcel Kutsch, Nimrod Megiddo, Utkarsh Srivastava, Tam Minh Tran
Consistent selectivity estimation via maximum entropy. VLDB J. 16 (1) (2007)
Paul G. Brown, Peter J. Haas
Techniques for Warehousing of Sample Data. ICDE (2006)
Rainer Gemulla, Wolfgang Lehner, Peter J. Haas
A Dip in the Reservoir: Maintaining Sample Synopses of Evolving Datasets. VLDB (2006)
Yannis Sismanis, Paul Brown, Peter J. Haas, Berthold Reinwald
GORDIAN: Efficient and Scalable Discovery of Composite Keys. VLDB (2006)
Marcel Kutsch, Peter J. Haas, Volker Markl, Nimrod Megiddo, Tam Minh Tran
Integrating a Maximum-Entropy Cardinality Estimator into DB2 UDB. EDBT (2006)
Utkarsh Srivastava, Peter J. Haas, Volker Markl, Marcel Kutsch, Tam Minh Tran
ISOMER: Consistent Histogram Construction Using Query Feedback. ICDE (2006)
Sam Lightstone, Guy M. Lohman, Peter J. Haas, Volker Markl, Jun Rao, Adam J. Storm, Maheswaran Surendra, Daniel C. Zilio
Making DB2Products Self-Managing: Strategies and Experiences. IEEE Data Eng. Bull. 29 (3) (2006)
Volker Markl, Marcel Kutsch, Tam Minh Tran, Peter J. Haas, Nimrod Megiddo
MAXENT: consistent cardinality estimation in action. SIGMOD Conference (2006)
Paul Brown, Peter J. Haas, Jussi Myllymaki, Hamid Pirahesh, Berthold Reinwald, Yannis Sismanis
Toward Automated Large-Scale Information Integration and Discovery. Data Management in a Connected World (2005)
Peter J. Haas, Mokhtar Kandil, Alberto Lerner, Volker Markl, Ivan Popivanov, Vijayshankar Raman, Daniel C. Zilio
Automated statistics collection in action. SIGMOD Conference (2005)
Ning Zhang, Peter J. Haas, Vanja Josifovski, Guy M. Lohman, Chun Zhang
Statistical Learning Techniques for Costing XML Queries. VLDB (2005)
Volker Markl, Nimrod Megiddo, Marcel Kutsch, Tam Minh Tran, Peter J. Haas, Utkarsh Srivastava
Consistently Estimating the Selectivity of Conjuncts of Predicates. VLDB (2005)
Ihab F. Ilyas, Volker Markl, Peter J. Haas, Paul Brown, Ashraf Aboulnaga
CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies. SIGMOD Conference (2004)
Ihab F. Ilyas, Volker Markl, Peter J. Haas, Paul G. Brown, Ashraf Aboulnaga
Automatic Relationship Discovery in Self-Managing Database Systems. ICAC (2004)
Ashraf Aboulnaga, Peter J. Haas, Sam Lightstone, Guy M. Lohman, Volker Markl, Ivan Popivanov, Vijayshankar Raman
Automated Statistics Collection in DB2 UDB. VLDB (2004)
Peter J. Haas, Christian Koenig
A Bi-Level Bernoulli Scheme for Database Sampling. SIGMOD Conference (2004)
Peter J. Haas
Stochastic Petri Nets for Modelling and Simulation. WSC (2004)
Ihab F. Ilyas, Volker Markl, Peter J. Haas, Paul G. Brown, Ashraf Aboulnaga
CORDS: Automatic Generation of Correlation Statistics in DB2. VLDB (2004)
Rakesh Agrawal, Peter J. Haas, Jerry Kiernan
A System for Watermarking Relational Databases. SIGMOD Conference (2003)
Hervé Brönnimann, Bin Chen, Manoranjan Dash, Peter J. Haas, Peter Scheuermann
Efficient data reduction with EASE. KDD (2003)
Rakesh Agrawal, Peter J. Haas, Jerry Kiernan
Watermarking relational data: framework, algorithms and analysis. VLDB J. 12 (2) (2003)
Paul Brown, Peter J. Haas
BHUNT: Automatic Discovery of Fuzzy Algebraic Constraints in Relational Data. VLDB (2003)
Bin Chen, Peter J. Haas, Peter Scheuermann
FAST: A New Sampling-Based Algorithm for Discovering Association Rules. ICDE (2002)

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Publication Activity (10 Years)

Top Topics

Top Venues

Peter J. Haas