Sven Helmer
- Signal Processing top 2%
- Data Management and Algorithms 33
-
- Advanced Database Systems and Queries 34
- IoT and Edge/Fog Computing 7
- Advanced Data Storage Technologies 5
- Information Systems top 2%
- Data Mining Algorithms and Applications 7
- Cloud Computing and Resource Management 5
- Artificial Intelligence top 5%
- Algorithms and Data Compression 10
- Semantic Web and Ontologies 10
Sven Helmer
73 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 90
- Signal Processing 416
- Computer Networks and Communications 771
- Information Systems 418
- Artificial Intelligence 451
- Information Systems and Management 61
Countries citing papers authored by Sven Helmer
This map shows the geographic impact of Sven Helmer'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 Sven Helmer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sven Helmer more than expected).
Fields of papers citing papers by Sven Helmer
This network shows the impact of papers produced by Sven Helmer. 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 Sven Helmer. The network helps show where Sven Helmer may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Sven Helmer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 0 | |
| 2 | 2021 | 10 | |
| 3 | 2021 | 6 | |
| 4 | 2020 | 10 | |
| 5 | 2020 | 14 | |
| 6 | 2019 | 3 | |
| 7 | 2018 | 52 | |
| 8 | 2018 | 3 | |
| 9 | An Interval-based Query Language for High-level Surveillance Event Detection. | 2016 | 5 |
| 10 | 2016 | 35 | |
| 11 | 2016 | 11 | |
| 12 | 2015 | 76 | |
| 13 | Increasing the Performance of Fuzzy Retrieval Using Impact Ordering | 2009 | 2 |
| 14 | Measuring the structural similarity of semistructured documents using entropy | 2007 | 35 |
| 15 | XQuery Processing in Natix with an Emphasis on Join Ordering. | 2004 | 8 |
| 16 | Compiling Away Set Containment and Intersection Joins | 2002 | 1 |
| 17 | Natix: A Technology Overview | 2002 | 3 |
| 18 | Diag-Join: An Opportunistic Join Algorithm for 1:N Relationships | 1998 | 18 |
| 19 | Evaluation of main memory join algorithms for joins with subset join predicates | 1997 | 7 |
| 20 | Evaluation of Main Memory Join Algorithms for Joins with Set Comparison Join Predicates | 1996 | 38 |
About Sven Helmer
Sven Helmer is a scholar working on Signal Processing, Computer Networks and Communications and Information Systems, having authored 75 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (34 papers), Data Management and Algorithms (33 papers), Algorithms and Data Compression (10 papers), Semantic Web and Ontologies (10 papers), Data Mining Algorithms and Applications (7 papers), IoT and Edge/Fog Computing (7 papers), Advanced Data Storage Technologies (5 papers) and Cloud Computing and Resource Management (5 papers). The work is most often cited by research in Signal Processing (416 citations), Computer Networks and Communications (771 citations) and Information Systems (418 citations). Sven Helmer has collaborated with scholars based in Italy, Germany and United Kingdom. Frequent co-authors include Guido Moerkotte, Claus Pahl, Till Westmann, Carl-Christian Kanne, Nabil El Ioini, Brian Lee, Fabio Persia, Donald Kossmann, Pekka Abrahamsson and Xiaofeng Wang. Their work appears in journals such as Fuzzy Sets and Systems, Future Generation Computer Systems and Scientometrics.
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.