Mike Sips

1.1k total citations
38 papers, 690 citations indexed

About

Mike Sips is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Mike Sips has authored 38 papers receiving a total of 690 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computer Vision and Pattern Recognition, 16 papers in Signal Processing and 9 papers in Artificial Intelligence. Recurrent topics in Mike Sips's work include Data Visualization and Analytics (26 papers), Data Management and Algorithms (11 papers) and Time Series Analysis and Forecasting (6 papers). Mike Sips is often cited by papers focused on Data Visualization and Analytics (26 papers), Data Management and Algorithms (11 papers) and Time Series Analysis and Forecasting (6 papers). Mike Sips collaborates with scholars based in Germany, United States and Switzerland. Mike Sips's co-authors include Daniel A. Keim, Christian Panse, John Lewis, Boris Neubert, Pat Hanrahan, Jörn Schneidewind, Stephen C. North, Doris Dransch, Norbert Marwan and Sungkil Lee and has published in prestigious journals such as Hydrology and earth system sciences, Computers & Geosciences and IEEE Transactions on Visualization and Computer Graphics.

In The Last Decade

Mike Sips

37 papers receiving 657 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mike Sips Germany	15	490	198	195	72	63	38	690
Jorge Poco Brazil	13	595 1.2×	206 1.0×	181 0.9×	66 0.9×	42 0.7×	44	944
Johannes Kehrer Norway	13	591 1.2×	178 0.9×	233 1.2×	38 0.5×	37 0.6×	17	777
Rick Walker United Kingdom	9	468 1.0×	95 0.5×	159 0.8×	27 0.4×	53 0.8×	20	638
Mikael Jern Sweden	13	611 1.2×	255 1.3×	185 0.9×	223 3.1×	33 0.5×	56	891
Masahiro Takatsuka Australia	12	331 0.7×	115 0.6×	138 0.7×	102 1.4×	35 0.6×	59	565
Harish Doraiswamy United States	18	374 0.8×	241 1.2×	99 0.5×	59 0.8×	21 0.3×	37	790
Aritra Dasgupta United States	15	385 0.8×	84 0.4×	258 1.3×	21 0.3×	28 0.4×	40	606
Kristin Potter United States	15	498 1.0×	141 0.7×	214 1.1×	22 0.3×	22 0.3×	35	989
Dong Hyun Jeong United States	15	360 0.7×	158 0.8×	337 1.7×	22 0.3×	44 0.7×	60	811
Craig M. Wittenbrink United States	14	725 1.5×	185 0.9×	196 1.0×	50 0.7×	33 0.5×	47	1.2k

Countries citing papers authored by Mike Sips

Since Specialization

Citations

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

Fields of papers citing papers by Mike Sips

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mike Sips

This figure shows the co-authorship network connecting the top 25 collaborators of Mike Sips. A scholar is included among the top collaborators of Mike Sips 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 Mike Sips. Mike Sips is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Dahle, Christoph, Eva Boergens, Ingo Sasgen, et al.. (2025). GravIS: mass anomaly products from satellite gravimetry. Earth system science data. 17(2). 611–631. 1 indexed citations

Boergens, Eva, Andreas Güntner, Mike Sips, Christian Schwatke, & Henryk Dobslaw. (2024). Interannual variations of terrestrial water storage in the East African Rift region. Hydrology and earth system sciences. 28(20). 4733–4754. 5 indexed citations

Dransch, Doris, Daniel Eggert, & Mike Sips. (2023). F4ESS – a framework for interdisciplinary data-driven earth system science. International Journal of Digital Earth. 16(1). 3973–3986. 2 indexed citations

Sips, Mike, et al.. (2016). PyRQA—Conducting recurrence quantification analysis on very long time series efficiently. Computers & Geosciences. 104. 101–108. 36 indexed citations

Lucia, Marco De, et al.. (2016). Data-driven Surrogate Model Approach for Improving the Performance of Reactive Transport Simulations. Energy Procedia. 97. 447–453. 28 indexed citations

Eggert, Daniel, et al.. (2016). Towards Visual Analytics for Multi-Sensor Analysis of Remote Sensing Archives. Publication Database GFZ (GFZ German Research Centre for Geosciences). 7–11.

Marwan, Norbert, et al.. (2015). Analysing the degree of replication of palaeoclimate records. Publication Database GFZ (GFZ German Research Centre for Geosciences). 4960. 1 indexed citations

Sips, Mike, et al.. (2015). Massively Parallel Analysis of Similarity Matrices on Heterogeneous Hardware. Publication Database GFZ (GFZ German Research Centre for Geosciences). 56–62. 2 indexed citations

Witt, Carsten, et al.. (2015). Visual Analytics for Correlation‐Based Comparison of Time Series Ensembles. Computer Graphics Forum. 34(3). 411–420. 20 indexed citations

10.

Sips, Mike, et al.. (2014). Visual Analytics for Comparison of Ocean Model Output with Reference Data: Detecting and Analyzing Geophysical Processes Using Clustering Ensembles. IEEE Transactions on Visualization and Computer Graphics. 20(12). 1893–1902. 22 indexed citations

11.

Sips, Mike, et al.. (2012). A Visual Analytics Approach to Multiscale Exploration of Environmental Time Series. IEEE Transactions on Visualization and Computer Graphics. 18(12). 2899–2907. 27 indexed citations

12.

Schneidewind, Jörn, Mike Sips, & Daniel A. Keim. (2007). An Automated Approach for the Optimization of Pixel-Based Visualizations. Information Visualization. 6(1). 75–88. 4 indexed citations

13.

Sips, Mike, et al.. (2007). Highlighting space–time patterns: Effective visual encodings for interactive decision‐making. International Journal of Geographical Information Systems. 21(8). 879–893. 9 indexed citations

14.

Panse, Christian, Mike Sips, Daniel A. Keim, & Stephen C. North. (2006). Visualization of Geo-spatial Point Sets via Global Shape Transformation and Local Pixel Placement. IEEE Transactions on Visualization and Computer Graphics. 12(5). 749–756. 24 indexed citations

15.

Keim, Daniel A., Jörn Schneidewind, & Mike Sips. (2005). FP-Viz: Visual Frequent Pattern Mining. KOPS (University of Konstanz). 11 indexed citations

16.

Keim, Daniel A., Christian Panse, Jörn Schneidewind, & Mike Sips. (2004). Geo-Spatial Data Viewer: From Familiar Land-covering to Arbitrary Distorted Geo-Spatial Quadtree Maps. KOPS (University of Konstanz). 213–220. 4 indexed citations

17.

Keim, Daniel A., et al.. (2004). Exploring and Visualizing the History of InfoVis. 216. 9 indexed citations

18.

Keim, Daniel A., et al.. (2004). Finding Spatial Patterns in Network Data. KOPS (University of Konstanz). 1 indexed citations

19.

Keim, Daniel A., Christian Panse, Mike Sips, & Stephen C. North. (2004). Visual Data Mining in Large Geospatial Point Sets. IEEE Computer Graphics and Applications. 24(5). 36–44. 47 indexed citations

20.

Keim, Daniel A., Christian Panse, Mathias Schäfer, Mike Sips, & Stephen C. North. (2003). HistoScale: An Efficient Approach for Computing Pseudo-Cartograms. IEEE Visualization. 93. 10 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.

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