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 Frederick Reiss
Since
Specialization
Citations
This map shows the geographic impact of Frederick Reiss'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 Frederick Reiss with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Frederick Reiss more than expected).
This network shows the impact of papers produced by Frederick Reiss. 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 Frederick Reiss. The network helps show where Frederick Reiss may publish in the future.
Co-authorship network of co-authors of Frederick Reiss
This figure shows the co-authorship network connecting the top 25 collaborators of Frederick Reiss.
A scholar is included among the top collaborators of Frederick Reiss 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 Frederick Reiss. Frederick Reiss 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.
Reiss, Frederick, et al.. (2021). What makes a data-driven business model? A consolidated taxonomy. Journal of the Association for Information Systems.6 indexed citations
2.
Chiticariu, Laura, Yunyao Li, & Frederick Reiss. (2015). Transparent Machine Learning for Information Extraction: State-of-the-Art and the Future. Empirical Methods in Natural Language Processing.3 indexed citations
3.
Fagin, Ronald, Benny Kimelfeld, Frederick Reiss, & Stijn Vansummeren. (2015). Document Spanners. Journal of the ACM. 62(2). 1–51.44 indexed citations
4.
Böhm, Matthias, Douglas Burdick, Alexandre Evfimievski, et al.. (2014). SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs.. IEEE Data(base) Engineering Bulletin. 37. 52–62.28 indexed citations
Chiticariu, Laura, et al.. (2012). WizIE: A Best Practices Guided Development Environment for Information Extraction. Meeting of the Association for Computational Linguistics. 109–114.12 indexed citations
8.
Li, Yunyao, Frederick Reiss, & Laura Chiticariu. (2011). SystemT: A Declarative Information Extraction System. Meeting of the Association for Computational Linguistics. 109–114.21 indexed citations
9.
Chiticariu, Laura, et al.. (2010). Refining Information Extraction Rules using Data Provenance.. IEEE Data(base) Engineering Bulletin. 33. 17–24.6 indexed citations
10.
Chiticariu, Laura, Rajasekar Krishnamurthy, Yunyao Li, Frederick Reiss, & Shivakumar Vaithyanathan. (2010). Domain Adaptation of Rule-Based Annotators for Named-Entity Recognition Tasks. Empirical Methods in Natural Language Processing. 1002–1012.87 indexed citations
11.
Chiticariu, Laura, Rajasekar Krishnamurthy, Yunyao Li, et al.. (2010). SystemT: An Algebraic Approach to Declarative Information Extraction. Meeting of the Association for Computational Linguistics. 128–137.100 indexed citations
Reiss, Frederick, Kurt Stockinger, Kesheng Wu, Arie Shoshani, & Joseph M. Hellerstein. (2007). Efficient Analysis of Live and Historical Streaming Data and its Application to Cybersecurity. University of North Texas Digital Library (University of North Texas).3 indexed citations
Franklin, Michael J., Shawn R. Jeffery, Sailesh Krishnamurthy, et al.. (2005). Design Considerations for High Fan-In Systems: The HiFi Approach.. Conference on Innovative Data Systems Research. 290–304.113 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.