Arno Siebes

32 papers receiving 130 citations

Peers

Arno Siebes
Comparison fields: 5 of 75
  • Artificial Intelligence 68
  • Signal Processing 41
  • Information Systems 39
  • Molecular Biology 25
  • Computational Theory and Mathematics 24
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Countries citing papers authored by Arno Siebes

Since Specialization
Citations

This map shows the geographic impact of Arno Siebes'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 Arno Siebes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arno Siebes more than expected).

Fields of papers citing papers by Arno Siebes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arno Siebes. 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 Arno Siebes. The network helps show where Arno Siebes may publish in the future.

Co-authorship network of co-authors of Arno Siebes

This figure shows the co-authorship network connecting the top 25 collaborators of Arno Siebes. A scholar is included among the top collaborators of Arno Siebes 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 Arno Siebes. Arno Siebes 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
#WorkIndexed citations
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5 15
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Characterising Seismic Data.
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8 5
9 10
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Local Pattern Detection: International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
5
11
Knowledge Discovery in Inductive Databases: Third International Workshop, KDID 2004, Pisa, Italy, September 20, 2004, Revised Selected and Invited Papers (Lecture Notes in Computer Science)
1
12
A framework for query optimization to support data mining
0
13
DEGAS : a temporal active data model based on object autonomy
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14
Deductive Databases: Challenges, Opportunities and Future Directions (Panel Discussion)
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15
Data surveying foundations of an inductive query language
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A data model for autonomous objects
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Homogeneous discoveries contain no surprises: inferring risk-profiles from large databases
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Schema refinement and schema integration in object-oriented databases.
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Towards a design theory for Database triggers.
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Using a graph rewriting system for databases
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About Arno Siebes

Arno Siebes is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications, having authored 37 papers that have together received 148 indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (11 papers), Data Mining Algorithms and Applications (6 papers) and Data Management and Algorithms (6 papers). The work is most often cited by research in Signal Processing (41 citations), Health Informatics (3 citations) and Artificial Intelligence (68 citations). Arno Siebes has collaborated with scholars based in Netherlands, United Kingdom and Canada. Frequent co-authors include Zbigniew R. Struzik, Ad Feelders, Michael R. Berthold, Wouter Duivesteijn, Antti Ukkonen, Robert C. Glen, Katharina Morik, Jean‐François Boulicaut, Martin Kersten and Robert Castelo. Their work appears in journals such as Expert Systems with Applications, Computer Methods and Programs in Biomedicine and ACM Transactions on Information Systems.

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.

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