Ira J. Haimowitz
- Artificial Intelligence top 10%
- Signal Processing top 10%
- Computer Networks and Communications top 10%
- Surgery
- Information Systems
- Co-authors
- Isaac S. KohanePhillip LeFoster ProvostSalvatore J. StolfoTom FawcettHenry G. SchwarzÖzden Gür AliJames C. Fackler
- Topics
- AI-based Problem Solving and Planning (5 papers)Time Series Analysis and Forecasting (4 papers)Data Mining Algorithms and Applications (4 papers)
- Partner nations
- United StatesIsrael
In The Last Decade
Ira J. Haimowitz
14 papers receiving 196 citations
Peers
Comparison fields: 5 of 43
- Artificial Intelligence 139
- Signal Processing 93
- Computer Networks and Communications 90
- Surgery 49
- Information Systems 32
Countries citing papers authored by Ira J. Haimowitz
This map shows the geographic impact of Ira J. Haimowitz'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 Ira J. Haimowitz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ira J. Haimowitz more than expected).
Fields of papers citing papers by Ira J. Haimowitz
This network shows the impact of papers produced by Ira J. Haimowitz. 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 Ira J. Haimowitz. The network helps show where Ira J. Haimowitz may publish in the future.
Co-authorship network of co-authors of Ira J. Haimowitz
This figure shows the co-authorship network connecting the top 25 collaborators of Ira J. Haimowitz. A scholar is included among the top collaborators of Ira J. Haimowitz 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 Ira J. Haimowitz. Ira J. Haimowitz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | Healthcare Relationship Marketing: Strategy, Design and Measurement | 4 |
| 4 | Agent Mediated Knowledge Management for Tracking Internet Behavior | 1 |
| 5 | 2 | |
| 6 | 27 | |
| 7 | Integrating and mining distributed customer databases | 5 |
| 8 | 2 | |
| 9 | 54 | |
| 10 | 46 | |
| 11 | Knowledge-based Data Display Using TrenDx | 3 |
| 12 | An epistemology for clinically significant trends | 14 |
| 13 | Automated trend detection with alternate temporal hypotheses | 47 |
| 14 | Hypothesis-driven data abstraction with trend templates. | 17 |
| 15 | 4 | |
| 16 | Influences on the performance of hospital clinical event monitoring. | 1 |
About Ira J. Haimowitz
Ira J. Haimowitz is a scholar working on Signal Processing, Software and Artificial Intelligence, having authored 16 papers that have together received 228 indexed citations. Recurring topics across this work include AI-based Problem Solving and Planning (5 papers), Time Series Analysis and Forecasting (4 papers) and Data Mining Algorithms and Applications (4 papers). The work is most often cited by research in Signal Processing (93 citations), Artificial Intelligence (139 citations) and Computer Networks and Communications (90 citations). Ira J. Haimowitz has collaborated with scholars based in United States and Israel. Frequent co-authors include Isaac S. Kohane, Phillip Le, Foster Provost, Salvatore J. Stolfo, Tom Fawcett, Henry G. Schwarz, Özden Gür Ali, James C. Fackler and R. M. Mattheyses. Their work appears in journals such as Computer Methods and Programs in Biomedicine, Artificial Intelligence in Medicine and AI Magazine.
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.