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.
Countries citing papers authored by Michael Cafarella
Since
Specialization
Citations
This map shows the geographic impact of Michael Cafarella'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 Michael Cafarella with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Cafarella more than expected).
Fields of papers citing papers by Michael Cafarella
This network shows the impact of papers produced by Michael Cafarella. 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 Michael Cafarella. The network helps show where Michael Cafarella may publish in the future.
Co-authorship network of co-authors of Michael Cafarella
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Cafarella.
A scholar is included among the top collaborators of Michael Cafarella 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 Michael Cafarella. Michael Cafarella is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cafarella, Michael, et al.. (2020). Constructing Expressive Relational Queries with Dual-Specification Synthesis.. Conference on Innovative Data Systems Research.2 indexed citations
Anderson, Michael R., et al.. (2016). Runtime Support for Human-in-the-Loop Feature Engineering System.. IEEE Data(base) Engineering Bulletin. 39. 62–84.4 indexed citations
Anderson, Michael R., Victor Bittorf, Matthew Burgess, et al.. (2013). Brainwash: A data system for feature engineering. Conference on Innovative Data Systems Research.68 indexed citations
11.
Cafarella, Michael, et al.. (2013). Ringtail: Feature Selection For Easier Nowcasting.. 49–54.7 indexed citations
12.
Cafarella, Michael. (2009). Extracting and Querying a Comprehensive Web Database.. Conference on Innovative Data Systems Research.22 indexed citations
13.
Cafarella, Michael, Alon Halevy, Yang Zhang, Daisy Zhe Wang, & Eugene Wu. (2008). Uncovering the Relational Web.91 indexed citations
14.
Cafarella, Michael, Christopher Ré, Dan Suciu, Oren Etzioni, & Michele Banko. (2007). Structured querying of web text. Conference on Innovative Data Systems Research.21 indexed citations
Cafarella, Michael, Dan Suciu, & Oren Etzioni. (2007). Navigating Extracted Data with Schema Discovery..15 indexed citations
17.
Cafarella, Michael, Christopher Ré, Dan Suciu, & Oren Etzioni. (2007). Structured Querying of Web Text Data: A Technical Challenge.. Conference on Innovative Data Systems Research. 225–234.39 indexed citations
18.
Etzioni, Oren, Michele Banko, & Michael Cafarella. (2006). Machine reading. National Conference on Artificial Intelligence. 1517–1519.68 indexed citations
19.
Cafarella, Michael, Oren Etzioni, & Dan Suciu. (2006). Structured Queries Over Web Text.. IEEE Data(base) Engineering Bulletin. 29(4). 45–51.4 indexed citations
20.
Etzioni, Oren, Michael Cafarella, Doug Downey, et al.. (2004). Methods for domain-independent information extraction from the web: an experimental comparison. National Conference on Artificial Intelligence. 391–398.73 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.