Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Online aggregation
1997629 citationsJoseph M. Hellerstein, Peter J. Haas et al.profile →
Large-scale matrix factorization with distributed stochastic gradient descent
2011393 citationsPeter J. Haas, Yannis Sismanis et al.profile →
When does power listen to truth? A constructivist approach to the policy process
This map shows the geographic impact of Peter J. Haas's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Peter J. Haas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter J. Haas more than expected).
This network shows the impact of papers produced by Peter J. Haas. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Peter J. Haas. The network helps show where Peter J. Haas may publish in the future.
Co-authorship network of co-authors of Peter J. Haas
This figure shows the co-authorship network connecting the top 25 collaborators of Peter J. Haas.
A scholar is included among the top collaborators of Peter J. Haas based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Peter J. Haas. Peter J. Haas is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cormode, Graham, Minos Garofalakis, Peter J. Haas, & Chris Jermaine. (2011). Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches. 4(1-3). 1–294.304 indexed citations breakdown →
7.
Behm, Alexander, et al.. (2007). Integrating Query-Feedback Based Statistics into Informix Dynamic Server.. BTW. 582–600.1 indexed citations
8.
Haas, Peter J., Fabian Hueske, & Volker Markl. (2007). Detecting attribute dependencies from query feedback. Very Large Data Bases. 830–841.6 indexed citations
Kalimi, Isaac & Peter J. Haas. (2006). Biblical interpretation in Judaism and Christianity. T&T Clark eBooks.4 indexed citations
11.
Zhang, Ning, Peter J. Haas, Vanja Josifovski, Guy M. Lohman, & Chun Zhang. (2005). Statistical learning techniques for costing XML queries. Very Large Data Bases. 289–300.48 indexed citations
12.
Markl, Volker, et al.. (2005). Consistently estimating the selectivity of conjuncts of predicates. Very Large Data Bases. 373–384.28 indexed citations
13.
Haas, Peter J., et al.. (2005). The Jewish tradition. Praeger eBooks.7 indexed citations
Barbará, Daniel, William DuMouchel, Christos Faloutsos, et al.. (1997). The New Jersey Data Reduction Report.. IEEE Data(base) Engineering Bulletin. 20. 3–45.152 indexed citations
17.
Haas, Peter J., Jeffrey F. Naughton, S. Seshadri, & Lynne Stokes. (1995). Sampling-Based Estimation of the Number of Distinct Values of an Attribute. Very Large Data Bases. 311–322.184 indexed citations
18.
Haas, Peter J., et al.. (1993). Institutions for the Earth. Harvard international review.119 indexed citations
19.
Haas, Peter J.. (1989). A Comparative Analysis of State Mental Health Policy. 6(4).3 indexed citations
20.
Elmore, D., H. Kagan, D. Ciampa, et al.. (1984). AN ELECTROSTATIC BEAMLINE FOR ACCELERATOR MASS SPECTROSCOPY OF EXOTIC PARTICLES. Nuclear Instruments and Methods.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.