Michele Dallachiesa
- Artificial Intelligence top 10%
- Management Science and Operations Research top 5%
- Signal Processing top 5%
- Computer Networks and Communications top 10%
- Information Systems top 10%
- Co-authors
- Themis PalpanasIhab F. IlyasAhmed EldawyNan TangMourad OuzzaniAhmed K. ElmagarmidBesmira NushiKun‐Lung Wu
- Topics
- Data Management and Algorithms (6 papers)Time Series Analysis and Forecasting (5 papers)Anomaly Detection Techniques and Applications (3 papers)
- Journals
- Proceedings of the VLDB EndowmentKnowledge and Information SystemsData & Knowledge Engineering
- Partner nations
- ItalyFranceUnited States
In The Last Decade
Michele Dallachiesa
9 papers receiving 294 citations
Peers
Comparison fields: 5 of 36
- Artificial Intelligence 173
- Management Science and Operations Research 155
- Signal Processing 138
- Computer Networks and Communications 101
- Information Systems 94
Countries citing papers authored by Michele Dallachiesa
This map shows the geographic impact of Michele Dallachiesa'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 Michele Dallachiesa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michele Dallachiesa more than expected).
Fields of papers citing papers by Michele Dallachiesa
This network shows the impact of papers produced by Michele Dallachiesa. 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 Michele Dallachiesa. The network helps show where Michele Dallachiesa may publish in the future.
Co-authorship network of co-authors of Michele Dallachiesa
This figure shows the co-authorship network connecting the top 25 collaborators of Michele Dallachiesa. A scholar is included among the top collaborators of Michele Dallachiesa 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 Michele Dallachiesa. Michele Dallachiesa 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 | 8 | |
| 3 | 46 | |
| 4 | 10 | |
| 5 | 13 | |
| 6 | 12 | |
| 7 | 176 | |
| 8 | 38 | |
| 9 | 7 |
About Michele Dallachiesa
Michele Dallachiesa is a scholar working on Signal Processing, Artificial Intelligence and Information Systems, having authored 9 papers that have together received 311 indexed citations. Recurring topics across this work include Data Management and Algorithms (6 papers), Time Series Analysis and Forecasting (5 papers) and Anomaly Detection Techniques and Applications (3 papers). The work is most often cited by research in Signal Processing (138 citations), Management Science and Operations Research (155 citations) and Artificial Intelligence (173 citations). Michele Dallachiesa has collaborated with scholars based in Italy, France and United States. Frequent co-authors include Themis Palpanas, Ihab F. Ilyas, Ahmed Eldawy, Nan Tang, Mourad Ouzzani, Ahmed K. Elmagarmid, Besmira Nushi, Kun‐Lung Wu, Gabriela Jacques-Silva and Buğra Gedik. Their work appears in journals such as Proceedings of the VLDB Endowment, Knowledge and Information Systems and Data & Knowledge Engineering.
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.