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.
Data Science and its Relationship to Big Data and Data-Driven Decision Making
2013868 citationsFoster Provost, Tom FawcettBig Dataprofile →
Robust Classification for Imprecise Environments
2001829 citationsFoster Provost, Tom Fawcettprofile →
Get another label? improving data quality and data mining using multiple, noisy labelers
2008787 citationsVictor S. Sheng, Foster Provost et al.profile →
Countries citing papers authored by Foster Provost
Since
Specialization
Citations
This map shows the geographic impact of Foster Provost'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 Foster Provost with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Foster Provost more than expected).
This network shows the impact of papers produced by Foster Provost. 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 Foster Provost. The network helps show where Foster Provost may publish in the future.
Co-authorship network of co-authors of Foster Provost
This figure shows the co-authorship network connecting the top 25 collaborators of Foster Provost.
A scholar is included among the top collaborators of Foster Provost 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 Foster Provost. Foster Provost is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Fernández, Carlos, et al.. (2019). Counterfactual Explanations for Data-Driven Decisions. Journal of the Association for Information Systems.8 indexed citations
2.
Provost, Foster, et al.. (2016). Matrix-Factorization-Based Dimensionality Reduction in the Predictive Modeling Process: A Design Science Perspective. The Faculty Digital Archive (New York University).2 indexed citations
Provost, Foster & Tom Fawcett. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data. 1(1). 51–59.868 indexed citations breakdown →
7.
Provost, Foster & Tom Fawcett. (2013). Data science for business. CERN Document Server (European Organization for Nuclear Research).45 indexed citations
Martens, David & Foster Provost. (2011). Pseudo-social network targeting from consumer transaction data. The Faculty Digital Archive (New York University).18 indexed citations
10.
Sheng, Victor S., Foster Provost, & Panagiotis G. Ipeirotis. (2008). Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers. The Faculty Digital Archive (New York University).58 indexed citations
Perlich, Claudia & Foster Provost. (2005). ACORA: Distribution-Based Aggregation for Relational Learning from Identifier Attributes. The Faculty Digital Archive (New York University).16 indexed citations
14.
Macskassy, Sofus A. & Foster Provost. (2004). Simple Models and Classification in Networked Data. The Faculty Digital Archive (New York University).6 indexed citations
15.
Bernstein, Abraham, et al.. (2002). Discovering Knowledge from Relational Data Extracted from Business News. The Faculty Digital Archive (New York University).19 indexed citations
16.
Lee, Doheon, Mario Schkolnick, Foster Provost, & Ramakrishnan Srikant. (2001). Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. Knowledge Discovery and Data Mining.20 indexed citations
17.
Dhar, Vasant, et al.. (2000). Discovering Interesting Patterns for Investment Decision Making with Glower C - a Genetic Learner Overlaid with Entropy Reduction. The Faculty Digital Archive (New York University).2 indexed citations
Fawcett, Tom & Foster Provost. (1996). Combining data mining and machine learning for effective user profiling. Knowledge Discovery and Data Mining. 8–13.115 indexed citations
20.
Aronis, John M. & Foster Provost. (1994). Efficiently constructing relational features from background knowledge for inductive machine learning. Knowledge Discovery and Data Mining. 347–358.11 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.