Themis Palpanas

7.6k total citations
208 papers, 4.6k citations indexed

About

Themis Palpanas is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Themis Palpanas has authored 208 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 115 papers in Signal Processing, 112 papers in Artificial Intelligence and 75 papers in Computer Networks and Communications. Recurrent topics in Themis Palpanas's work include Time Series Analysis and Forecasting (86 papers), Data Management and Algorithms (76 papers) and Advanced Database Systems and Queries (56 papers). Themis Palpanas is often cited by papers focused on Time Series Analysis and Forecasting (86 papers), Data Management and Algorithms (76 papers) and Advanced Database Systems and Queries (56 papers). Themis Palpanas collaborates with scholars based in France, Italy and United States. Themis Palpanas's co-authors include George Papadakis, Mikalai Tsytsarau, Kostas Zoumpatianos, Paul Boniol, Michele Linardi, Eamonn Keogh, Yannis Velegrakis, John Paparrizos, Davide Mottin and Karima Echihabi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and ACM Computing Surveys.

In The Last Decade

Themis Palpanas

203 papers receiving 4.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Themis Palpanas France 38 2.7k 2.4k 1.2k 993 805 208 4.6k
Ada Wai-Chee Fu Hong Kong 38 3.1k 1.1× 2.0k 0.8× 1.1k 0.9× 261 0.3× 900 1.1× 106 5.1k
Nikos Mamoulis Hong Kong 46 2.9k 1.1× 3.4k 1.5× 2.1k 1.8× 433 0.4× 2.0k 2.5× 216 6.5k
Ihab F. Ilyas Canada 40 2.7k 1.0× 2.3k 1.0× 2.4k 2.0× 2.1k 2.1× 1.5k 1.9× 114 5.4k
Jianhua Feng China 38 2.0k 0.7× 1.9k 0.8× 1.6k 1.3× 1.4k 1.4× 1.5k 1.9× 126 4.5k
M. TAMER ÖZSU Canada 41 2.9k 1.1× 2.9k 1.2× 3.8k 3.1× 332 0.3× 1.8k 2.3× 243 7.0k
Wei Fan United States 38 4.2k 1.6× 862 0.4× 1.1k 0.9× 409 0.4× 1.1k 1.4× 133 5.4k
Nicholas Jing Yuan China 34 2.1k 0.8× 881 0.4× 422 0.4× 413 0.4× 1.7k 2.1× 80 4.7k
Tim Kraska United States 38 2.1k 0.8× 1.1k 0.5× 2.6k 2.2× 826 0.8× 1.9k 2.4× 149 5.0k
Sihem Amer-Yahia France 29 1.4k 0.5× 1.0k 0.4× 1.1k 0.9× 349 0.4× 1.2k 1.5× 149 3.0k
Philippe Cudré-Mauroux Switzerland 31 2.1k 0.8× 559 0.2× 1.3k 1.1× 519 0.5× 1.1k 1.4× 173 3.9k

Countries citing papers authored by Themis Palpanas

Since Specialization
Citations

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

Fields of papers citing papers by Themis Palpanas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Themis Palpanas

This figure shows the co-authorship network connecting the top 25 collaborators of Themis Palpanas. A scholar is included among the top collaborators of Themis Palpanas 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 Themis Palpanas. Themis Palpanas 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
1.
Boniol, Paul, et al.. (2025). $k$-Graph: A Graph Embedding for Interpretable Time Series Clustering. IEEE Transactions on Knowledge and Data Engineering. 37(5). 2680–2694. 1 indexed citations
2.
Boniol, Paul, A. Krishna, Qinghua Liu, et al.. (2025). VUS: effective and efficient accuracy measures for time-series anomaly detection. The VLDB Journal. 34(3). 10 indexed citations
3.
Echihabi, Karima, et al.. (2025). Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Art. Proceedings of the ACM on Management of Data. 3(1). 1–31. 2 indexed citations
4.
Rafiq, Hasan, et al.. (2024). A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (NILM) in residential context. Energy and Buildings. 305. 113890–113890. 32 indexed citations
5.
Papadakis, George, et al.. (2024). A Critical Re-evaluation of Record Linkage Benchmarks for Learning-Based Matching Algorithms. ANU Open Research (Australian National University). 3435–3448. 2 indexed citations
6.
Boniol, Paul, John Paparrizos, & Themis Palpanas. (2024). An Interactive Dive into Time-Series Anomaly Detection. 5382–5386. 7 indexed citations
7.
Wang, Zeyu, et al.. (2024). Steiner -Hardness: A Query Hardness Measure for Graph-Based ANN Indexes. Proceedings of the VLDB Endowment. 17(13). 4668–4682. 2 indexed citations
8.
Palpanas, Themis, et al.. (2024). Efficiently Mitigating the Impact of Data Drift on Machine Learning Pipelines. Proceedings of the VLDB Endowment. 17(11). 3072–3081. 3 indexed citations
9.
Wang, Zeyu, et al.. (2023). Dumpy: A Compact and Adaptive Index for Large Data Series Collections. Proceedings of the ACM on Management of Data. 1(1). 1–27. 12 indexed citations
10.
Palpanas, Themis, et al.. (2023). ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series. Proceedings of the VLDB Endowment. 17(3). 553–562.
11.
Boniol, Paul, et al.. (2023). dCNN/dCAM: anomaly precursors discovery in multivariate time series with deep convolutional neural networks. SHILAP Revista de lepidopterología. 4. 2 indexed citations
12.
Wang, Peng, et al.. (2023). Towards a Generic Framework for Mechanism-guided Deep Learning for Manufacturing Applications. 5532–5543. 1 indexed citations
13.
Lissandrini, Matteo, Davide Mottin, Themis Palpanas, & Yannis Velegrakis. (2020). Graph-Query Suggestions for Knowledge Graph Exploration. VBN Forskningsportal (Aalborg Universitet). 2549–2555. 19 indexed citations
14.
Akbarinia, Reza, et al.. (2020). BestNeighbor: efficient evaluation of kNN queries on large time series databases. Knowledge and Information Systems. 63(2). 349–378. 19 indexed citations
15.
Simonini, Giovanni, George Papadakis, Themis Palpanas, & Sonia Bergamaschi. (2018). Schema-Agnostic Progressive Entity Resolution. IEEE Transactions on Knowledge and Data Engineering. 31(6). 1208–1221. 25 indexed citations
16.
Linardi, Michele & Themis Palpanas. (2018). Scalable, variable-length similarity search in data series: the ULISSE approach. Very Large Data Bases. 11(13). 2236–2248. 31 indexed citations
17.
Catania, Barbara, Tania Cerquitelli, Silvia Chiusano, et al.. (2013). New Trends in Databases and Information Systems: 17th East European Conference on Advances in Databases and Information Systems. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 3 indexed citations
18.
Palpanas, Themis, et al.. (2013). Monitoring and diagnosing indicators for business analytics. Institutional Research Information System (Università degli Studi di Trento). 177–191. 2 indexed citations
19.
Keller, Frank, et al.. (2010). Corpus Evidence for Age Effects on Priming in Child Language. Proceedings of the Annual Meeting of the Cognitive Science Society. 32(32). 218–223. 6 indexed citations
20.
Palpanas, Themis, Vana Kalogeraki, & Dimitrios Gunopulos. (2007). Online Distribution Estimation for Streaming Data: Framework and Applications.. Institutional Research Information System (Università degli Studi di Trento). 430–438. 3 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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