phaas@cs.umass.edu

SELECTED PUBLICATIONS

Books

Published July 2002 and available from the Springer Catalog. Click here for errata. (Winner of I-Sim 2002 Outstanding Simulation Publication Award.)	Published in 2011 and available from Now Publishers.

Designing Information Systems for Decision Support

• "Scaling Package Queries to a Billion Tuples via Hierarchical Partitioning and Customized Optimization."A. Mai, P. Wang, A. Abouzied, M. Brucato, P. J. Haas, A. Meliou. PVLDV 17(5), 1146-1158 (2024). Download paper
• "In-database decision support: Opportunities and challenges." IEEE Data Engineering Bulletin, September 2022. Download paper.
• "Augmenting decision making via interactive what-if analysis." S. Gathani, M. Hulsebos, J. Gale, P.J. Haas, Ç. Demiralp. CIDR 2022. Download paper.
• "Stochastic package queries in probabilistic databases." M. Brucato, A. Abouzied, P.J. Haas, A. Meliou. SIGMOD 2020. Download paper.
• "sPaQLTools: A stochastic package query interface for scalable constrained optimization." M. Brucato, M. Mannino, A. Abouzied, Peter J. Haas, A. Meliou. Proc. VLDB Endowment 13(12), 2881-2884, 2020. (Winner of VLDB 2020 Best Demonstration Award.) Download paper.
• "SuDocu: Summarizing Documents by Example." A. Fariha, M. Brucato, P. J. Haas, and A. Meliou. Proc. VLDB Endowment 13(12): 2861-2864, 2020. (VLDB 2020 Best Demonstration Award Runner Up.) Download paper.

Simulation

• "Tutorial: Artificial neural networks for discrete-event simulation". P. J. Haas. 2024 WInter Simulation Conference. Download paper. Download slides.
• "Mechanistic modeling of social conditions in disease-prediction simulations via copulas and probabilistic graphical models: HIV case study". A. Khosheghbal, P. J. Haas, C. Gopalappa. Health Care Management Science , 2024. Download paper.
• "A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases-- application to sexually transmitted diseases". C. Gopalappa, H. Balasubramanian, P. J. Haas. Infectious Disease Modeling, 8, 2023, 84-100. Download paper
• "Causal dynamic Bayesian networks for simulation metamodelings." P. Amaranath, P. J. Haas, D. D. Jensen, and S. Witty. 2023 WInter Simulation Conference. (Best Paper finalist.) Download paper.
• "Efficient hybrid simulation optimization via graph neural network metamodeling." W. Cen and P. J. Haas. 2023 Winter Simulation Conference Download paper.
• "Enhanced simulation metamodeling via graph and generative neural networks." W. Cen and P. J. Haas. 2022 Winter Simulation Conference Download paper.
• "NIM: Modeling and generation of simulation inputs via generative neural networks." E. A. Herbert, W. Cen, and P. J. Haas. 2020 Winter Simulation Conference. (Best Paper finalist.) Download paper.
• On Transience and Recurrence in Irreducible Finite-State Stochastic Systems

  Peter W. Glynn, Peter J. Haas
  ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Don Iglehart, 2015
• "Improving the efficiency of stochastic composite simulation models via result caching." P. J. Haas. Proc. Winter Simulation Conference, 2014, 817-828. Download paper (corrected version).
• Model-data Ecosystems: challenges, tools, and trends

  Peter J. Haas
  PODS '14 Proceedings of the 33rd ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2014
• "Regenerative simulation." P. J. Haas. Encyclopedia of Operations Research and Management Science}, 3rd Edition, Springer, 2013. Details.
• "Splash: Simulation optimization in complex systems of systems." P. J. Haas, N. C. Barberis, P. Phoungphol, I. G. Terrizzano, W. C. Tan, P. G. Selinger, and P. P. Maglio. 50th Annual Allerton Conference on Communication, Control and Computing,, 2012. Download paper
• Splash: a platform for analysis and simulation of health

  Wang-Chiew Tan, Peter J. Haas, Ronald L. Mak, Cheryl A. Kieliszewski, Patricia G. Selinger, Paul P. Maglio, Susanne Glissman, Melissa Cefkin, Yinan Li
  IHI '12 Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, 2012
• "Data is dead...without what-if models." P. J. Haas, P. P. Maglio, P. G. Selinger, and W.-C. Tan. PVLDB, 4(12), 2011, 1486-1489. (Best Paper Honorable Mention.) Download paper
• "Social Factors in Creating an Integrated Capability for Health System Modeling and Simulation." P. P. Maglio, M. Cefkin, P. J. Haas, and P. Selinger. Proc. 2010 Intl. Conf. Social Computing, Behavioral Modeling, and Prediction (SBP 2010), 44-51. Download paper.
• "Laws of Large Numbers and Functional Central Limit Theorems for Generalized Semi-Markov Processes." P. W. Glynn and P. J. Haas. Commun. Statist. Stochastic Models, 22, 2006, 201-231. Download paper.
• "On Functional Central Limit Theorems for Semi-Markov and Related Processes." P. W. Glynn and P. J. Haas. Commun. Statist. Theory Methods, 33, 2004, 487-506. (Special issue on semi-Markov processes.) Download paper.
• "Estimation Methods for Delays in Non-Regenerative Discrete-Event Systems." P. J. Haas. Commun. Statist. Stochastic Models, 19, 2003, 1-35. Download paper.
• On the validity of long-run estimation methods for discrete-event systems

  Peter J. Haas, Peter W. Glynn
  ACM SIGMETRICS Performance Evaluation Review, 2002
• "On Simulation Output Analysis for Generalized Semi-Markov Processes." P. J. Haas. Commun. Statist. Stochastic Models, 15, 1999, 53-80. Download paper.
• "Estimation Methods for Passage Times Based on One-Dependent Cycles." P. J. Haas and G. S. Shedler. Discrete Event Dynamic Systems: Theory and Applications, 6, 1996, 43-72. Download paper.
• "Recurrence and Regeneration in Non-Markovian Networks of Queues." P. J. Haas and G. S. Shedler. Commun. Statist. Stochastic Models, 3, 1987, 29-52. Download paper.
• "Regenerative Generalized Semi-Markov Processes." P. J. Haas and G. S. Shedler. Commun. Statist. Stochastic Models, 3, 1987, 409-438. Download paper.

Managing Uncertain Data

• "Monte Carlo methods for uncertain data." P. J. Haas. Encyclopedia of Database Systems, 2nd Ed., Springer, 2017. Details.
• "Karp-Luby sampling." P. J. Haas. Encyclopedia of Database Systems, 2nd Ed., Springer, 2017. Details.
• "MCDB and SimSQL: Scalable Stochastic Analysis within the Database." P. J. Haas and C. Jermaine. 1st Intl. Workshop on Big Uncertain Data (BUDA), 2014. Download paper.
• Simulation of database-valued Markov chains using SimSQL

  Zhuhua Cai, Zografoula Vagena, Luis Perez, Subramanian Arumugam, Peter J. Haas, Christopher Jermaine
  SIGMOD '13 Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, 2013
• "Topic Models Over Spoken Language." N. Pansare, C. Jermaine, P. J. Haas, N. Rajput. Proc. 12th IEEE Intl. Conf. Data Mining, 2012, 1062–1067. Download paper.
• The Monte Carlo database system: Stochastic analysis close to the data

  Ravi Jampani, Fei Xu, Mingxi Wu, Luis Perez, Chris Jermaine, Peter J. Haas
  ACM Transactions on Database Systems (TODS), 2011
• "MCDB-R: Risk Analytics in the Database." S. Arumugam, R. Jampani, L. L. Perez, F. Xu, C. Jermaine, and P. J. Haas. PVLDB, 3(1), 2010, 782-793. Download paper.
• Uncertainty management in rule-based information extraction systems

  Eirinaios Michelakis, Rajasekar Krishnamurthy, Peter J. Haas, Shivakumar Vaithyanathan
  SIGMOD '09 Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, 2009

• E = MC³: managing uncertain enterprise data in a cluster-computing environment

  Fei Xu, Kevin Beyer, Vuk Ercegovac, Peter J. Haas, Eugene J. Shekita
  SIGMOD '09 Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, 2009
• "Database meets simulation: tools and techniques." P. J. Haas and C. Jermaine. Proc. 2009 INFORMS Simulation Society Research Workshop, 119-124. Download paper.
• MCDB: a Monte Carlo approach to managing uncertain data

  Ravi Jampani, Fei Xu, Mingxi Wu, Luis Leopoldo Perez, Christopher Jermaine, Peter J. Haas
  SIGMOD '08 Proceedings of the 2008 ACM SIGMOD international conference on Management of data, 2008
• "Resolution-Aware Query Answers for Business Intelligence." Y. Sismanis, L. Wang, A. Fuxman, P. J. Haas, and B. Reinwald. Proc. 25th Intl. Conf. Data Engineering, 2009, 976-987. Download paper.

Sampling for Query Optimization, Data Mining, Visualization, Machine Learning, and Integration

• "Unknown Examples & Machine Learning Model Generalization." Yeounoh Chung, Peter J. Haas, Eli Upfal, Tim Kraska. CoRR abs/1808.08294, 2018.
• "Sampling for Scalable Visual Analytics." B. C. Kwon, J. Verma, P. J. Haas, and Ç. Demiralp. IEEE Comput. Graphics Appl. 37(1), 2017, 100-108. Download paper.
• "Data-Stream Sampling: Basic Techniques and Results." P. J. Haas. In Data Stream Management: Processing High Speed Data Streams. Springer-Verlag, 2016.
• "Non-uniformity issues and workarounds in bounded-size sampling." R. Gemulla and P. J. Haas. VLDB J., 2013. Download paper.
• "Maintaining Bounded-Size Sample Synopses of Evolving Datasets." R. Gemulla, W. Lehner, and P. J. Haas. VLDB Journal, 17(2), 2008, 173-201. Download paper.
• Maintaining bernoulli samples over evolving multisets

  Rainer Gemulla, Wolfgang Lehner, Peter J. Haas
  PODS '07 Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2007
• "Techniques for Warehousing of Sample Data." P. G. Brown and P. J. Haas. Proc. 22nd Intl. Conf. Data Engrg., 2006. Download paper.
• "An Estimator of the Number of Species from Quadrat Sampling." P. J. Haas, Y. Liu, and L. Stokes. Biometrics, 62, 2006, 135-141. Download paper.)
• "Efficient Data Reduction Methods for On-Line Association Rule Discovery." H. Brönimann, B.Chen, M.Dash, P. J. Haas, Y. Qiao, and P. Scheuermann. In Data Mining: Next Generation Challenges and Future Directions. AAAI Press, 2004, 125-146. Details.
• CORDS: automatic discovery of correlations and soft functional dependencies

  Ihab F. Ilyas, Volker Markl, Peter Haas, Paul Brown, Ashraf Aboulnaga
  SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data, 2004
• "BHUNT: Automatic Discovery of Fuzzy Algebraic Constraints in Relational Data." P. G. Brown and P. J. Haas. Proc. 29th Intl. Conf. on Very Large Data Bases, 2003, 668-679. Download paper.
• "Efficient Data Reduction with EASE." H. Brönimann, B. Chen, M. Dash, P. J. Haas, P. Scheuermann. Proc. 9th Intl. Conf. Knowledge Discovery and Data Mining (KDD), 2003, 59-68. Download paper.
• A new two-phase sampling based algorithm for discovering association rules

  Bin Chen, Peter Haas, Peter Scheuermann
  KDD '02 Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 2002
• "Estimating the Number of Classes in a Finite Population." P. J. Haas and L. Stokes. J. Amer. Statist. Assoc., 93, 1998, 1475-1487. Download extended version of this paper.

  "The New Jersey Data Reduction Report." D. Barbara, W. DuMouchel, C. Faloutsos, P. J. Haas, J. M. Hellerstein, Y. Ioannidis, H. V. Jagadish, T. Johnson, R.Ng, V. Poosala, and K. Sevcik. Data Engrg. Bull., 20, 1997, 3-45. Download paper.

• "Selectivity and Cost Estimation for Joins Based on Random Sampling." P. J. Haas, J. F. Naughton, S. Seshadri, and A. N. Swami. ACM J. Computer Systems Sciences, 52, 1996, 550-569. (Special issue devoted to best papers from PODS '93.) Download paper.
• "Sampling-Based Selectivity Estimation Using Augmented Frequent Value Statistics." P. J. Haas and A. N. Swami. Proc. Eleventh Intl. Conf. Data Engineering, 1995, 522-531. Download paper.
• Sequential sampling procedures for query size estimation

  Peter J. Haas, Arun N. Swami
  SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data, 1992

Big Data Analytics

• "General temporally-biased sampling schemes for online model management." B. Hentschel, P. J. Haas, and Y. Tian. ACM Trans. Database Syst., 44(4), 2019, 14:1-14:45. Download paper.
• "MNC: Structure-exploiting sparsity estimation for matrix expressions." J. Sommer, M. Boehm, A. Evfimievski, B. Reinwald, P. J. Haas. Proc. 2019 ACM SIGMOD Intl. Conf. Management of Data, 1607-1623. Download paper.
• Compressed linear algebra for declarative large-scale machine learning

  Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
  Communications of the ACM, 2019
• "Temporally-biased sampling for online model management." B. Hentschel, P. J. Haas, and Y. Tian. Proc. 21st Intl. Conf. on Extending Database Technology (EDBT), 2018. (Best paper.) Download paper.
• "Compressed Linear Algebra for Large-Scale Machine Learning." A. Elgohary, M. Boehm, P. J. Haas, F. R. Reiss, and  B. Reinwald. PVLDB, 9(12), 2016, 960-971. (Best paper.) Download paper. Extended (and improved) journal version is here.
• "Dynamic interaction graphs with probabilistic edge decay." W. Xie, Y. Tian, Y. Sismanis, A. Balmin, and P. J. Haas. Proc. 31st Intl. Conf. Data Engrg. (ICDE), 2015, 1143-1154. Download paper.
• "Groupwise analytics via adaptive MapReduce." L. Peng, K. Zheng, A. Balmin, V. Ercegovac, P. J. Haas, and Y. Sismanis. Proc. 31st Intl. Conf. Data Engrg. (ICDE), 2015, 1059-1070. Download paper.
• Shared-memory and shared-nothing algorithms for matrix completion. F. Makari, C. Teflioudi, R. Gemulla, P. J. Haas, and Y. Sismanis. Knowledge Inform. Sys., 42(3), 2015, 493-523. Download paper.
• Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature

  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 M. Lisewski, Ana Lelescu, Houyin Zhang, Stephen Boyer, Griff Weber, Ying Chen, Lawrence Donehower, Scott Spangler, Olivier Lichtarge
  KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
• Automated hypothesis generation based on mining scientific literature

  Scott Spangler, Angela D. Wilkins, Benjamin J. Bachman, Meena Nagarajan, Tajhal Dayaram, Peter 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 Donehower, Ying Chen, Olivier Lichtarge
  KDD '14 Proceedings of the 20th ACM SIGKDD International Conference on Knowledge discovery and Data Mining, 2014
• Eagle-eyed elephant: split-oriented indexing in Hadoop

  Mohamed Y. Eltabakh, Fatma Özcan, Yannis Sismanis, Peter J. Haas, Hamid Pirahesh, Jan Vondrak
  EDBT '13 Proceedings of the 16th International Conference on Extending Database Technology, 2013
• "On aligning massive time-series data in Splash." P. J. Haas and Y. Sismanis. BigData 2012. Download paper.
• Large-scale matrix factorization with distributed stochastic gradient descent

  Rainer Gemulla, Erik Nijkamp, Peter J. Haas, Yannis Sismanis
  KDD '11 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011 (Also presented at XLDB 2011, and winner of Best Paper Award at 2011 NIPS Big Learning Workshop.) Extended technical report.
• Ricardo: integrating R and Hadoop

  Sudipto Das, Yannis Sismanis, Kevin S. Beyer, Rainer Gemulla, Peter J. Haas, John McPherson
  SIGMOD '10 Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010
• Distinct-value synopses for multiset operations

  Kevin Beyer, Rainer Gemulla, Peter J. Haas, Berthold Reinwald, Yannis Sismanis
  Communications of the ACM - A View of Parallel Computing, 2009
• "Main-memory scan sharing for multi-core CPUs." L. Qiao, V. Raman, F. Reiss, P. J. Haas, and G. M. Lohman. Proc. 34th Intl. Conf. on Very Large Data Bases, 2008, 610-621. Download paper.
• On synopses for distinct-value estimation under multiset operations

  Kevin Beyer, Peter J. Haas, Berthold Reinwald, Yannis Sismanis, Rainer Gemulla
  SIGMOD '07 Proceedings of the 2007 ACM SIGMOD international conference on Management of data, 2007
• "Toward automated large scale information integration and discovery." P. Brown, P. J. Haas, J. Myllymaki, H. Pirahesh, B. Reinwald, and Y. Sismanis. In Data Management in a Connected World, T. Härder and W. Lehner, eds. Springer-Verlag, 2005. Download paper.

Interactive/Approximate Query Processing and Data Exploration

• "Foresight: Recommending Visual Insights". Ç. Demiralp, P. J. Haas, S. Parthasarathy, and T. Pedapati. PVLDB, 10(12), 2017, 1937-1940. (Also presented at KDD IDEA 2017.) Download paper. An updated version with improved terminology was presented at IEEE VIS DSIA Workshop 2017, and can be found here.
• A bi-level Bernoulli scheme for database sampling

  Peter J. Haas, Christian König
  SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data, 2004
• "The Need for Speed: Speeding Up DB2 Using Sampling." P. J. Haas. IDUG Solutions Journal, 10(2), 2003, 32-34. Download article.
• A scalable hash ripple join algorithm

  Gang Luo, Curt J. Ellmann, Peter J. Haas, Jeffrey F. Naughton
  SIGMOD '02 Proceedings of the 2002 ACM SIGMOD international conference on Management of data, 2002
• "Hoeffding Inequalities for Join-Selectivity Estimation and Online Aggregation." P. J. Haas. Computing Science and Statistics, 31, 2000, 74-78. (Proceedings of Interface '99.) Download extended version of this paper.
• "Techniques for Online Exploration of Large Object-Relational Datasets." P. J. Haas. Proc. 11th Intl. Conf. Scientific and Statistical Database Management, 1999, 4-12 (keynote paper). Download paper.
• "Interactive Data Analysis: The CONTROL Project." J. M. Hellerstein, R. Avnur, A. Chou, C. Hidber, C. Olston, V. Raman, T. Roth, and P. J. Haas. IEEE Computer, 32, August 1999, 51-59 (cover feature). Download paper.
• Ripple joins for online aggregation

  Peter J. Haas, Joseph M. Hellerstein
  SIGMOD '97 Proceedings of the 1999 ACM SIGMOD international conference on Management of data, 1999. Extended IBM Technical Report.
• Online aggregation

  Joseph M. Hellerstein, Peter J. Haas, Helen J. Wang
  SIGMOD '97 Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, 1997 (Winner of 2007 SIGMOD Test-of-Time Award.) Reprinted in Readings in Database Systems, Third Edition, Morgan Kaufmann, San Francisco.
• "Large-Sample and Deterministic Confidence Intervals for Online Aggregation." P. J. Haas. Proc. Ninth Intl. Conf. Scientific and Statistical Database Management, 1997, 51-63. Download paper.

Stochastic Petri nets

• "Modeling and Simulation with Stochastic Petri Nets." (Invited tutorial.) Proc. Winter Simulation Conference '04. Download paper.
• "Estimation Methods for Non-Regenerative Stochastic Petri Nets." IEEE Trans. Software Engrg., 25, 1999, 218-236. (Special section devoted to the best papers from PNPM '97.) Download paper.
• "Passage Times in Colored Stochastic Petri Nets." P. J. Haas and G. S. Shedler. Commun. Statist. Stochastic Models, 9, 1993, 31-79. Download paper.
• "Stochastic Petri Nets: Modeling Power and Limit Theorems." P. J. Haas and G. S. Shedler. Probab. Engrg. Inform. Sciences, 5, 1991, 477-498. Download paper.
• "Stochastic Petri Nets With Timed and Immediate Transitions." P. J. Haas and G. S. Shedler. Commun. Statist. Stochastic Models, 5, 1989, 563-600. (Special issue devoted to Computer-Experimental Methods in Probability.) Download paper.
• "Regenerative Stochastic Petri Nets." P. J. Haas and G. S. Shedler. Performance Evaluation, 6, 189-204, 1986. Download paper.

Query Optimization and Learning Optimizers

• "Discovering and exploiting statistical properties for query optimization in relational databases: A survey." P. J. Haas, I. F. Ilyas, G. M. Lohman, and V. Markl. Statistical Analysis and Data Mining, 1, 2009, 223-250. Download paper.
• "Detecting attribute dependencies from query feedback." P. J. Haas, F. Hueske, and V. Markl. Proc. 33rd Intl. Conf. on Very Large Data Bases, 2007, 830-841. Download paper.
• "Consistent selectivity estimation via maximum entropy." V. Markl, P. J. Haas, M. Kutsch, N. Megiddo, and T. M. Tran. VLDB Journal, 16, 2007, 55-76. (Special issue devoted to best papers from VLDB 2005.) Download paper.
• "GORDIAN: Efficient and Scalable Discovery of All Composite Keys." Y. Sismanis, P. J. Haas, and B. Reinwald. Proc. 32nd Intl. Conf. on Very Large Data Bases, 2006, 691-702. Download paper.
• "ISOMER: Consistent histogram construction using query feedback." U. Srivastava, P. J. Haas, V. Markl, and N. Megiddo. Proc. 22nd Intl. Conf. Data Engrg., 2006. Download paper.
• "Statistical learning techniques for costing XML queries." N. Zhang, P. J. Haas, V. Josifovsky, G. M. Lohman, and C. Zhang. Proc. 31st Intl. Conf. on Very Large Data Bases, 2005, 289-300. Download paper.
• "Automated Statistics Collection in DB2 UDB." A. Aboulnaga, P. J. Haas, M. Kandil, S. Lightstone, G. Lohman, V. Markl, I. Popivanov, and V. Raman. Proc. 30th Intl. Conf. on Very Large Data Bases, 2004, 1146-1157. Download paper.
• Improved histograms for selectivity estimation of range predicates

  Viswanath Poosala, Peter J. Haas, Yannis E. Ioannidis, Eugene J. Shekita
  SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data, 1996

Miscellaneous

• "Watermarking Relational Data: Framework, Algorithms, and Analysis." R. Agrawal, P. J. Haas, and J. Kiernan. VLDB Journal, 12, 2003, 157-169. Download paper.
• "The Maximum and Mean of a Random Length Sequence." P. J. Haas. J. Appl. Probab., 29, 1992, 460-466. Download paper.
• "The Effects of NO2-Aerosol Interaction on Indices of Perceived Visibility Impairment." P. J. Haas and A. J. Fabrick. Atmospheric Environment, 15, 2171-2177, 1981. Download paper.

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