John Paparrizos

2.3k total citations · 1 hit paper
44 papers, 1.5k citations indexed

About

John Paparrizos is a scholar working on Artificial Intelligence, Signal Processing and Computer Networks and Communications. According to data from OpenAlex, John Paparrizos has authored 44 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Artificial Intelligence, 30 papers in Signal Processing and 12 papers in Computer Networks and Communications. Recurrent topics in John Paparrizos's work include Time Series Analysis and Forecasting (29 papers), Anomaly Detection Techniques and Applications (22 papers) and Complex Systems and Time Series Analysis (9 papers). John Paparrizos is often cited by papers focused on Time Series Analysis and Forecasting (29 papers), Anomaly Detection Techniques and Applications (22 papers) and Complex Systems and Time Series Analysis (9 papers). John Paparrizos collaborates with scholars based in United States, France and Greece. John Paparrizos's co-authors include Luis Gravano, Michael J. Franklin, Paul Boniol, Themis Palpanas, Aaron J. Elmore, Chunwei Liu, Eric Horvitz, Ryen W. White, Ruey S. Tsay and Hao Jiang and has published in prestigious journals such as Proceedings of the VLDB Endowment, ACM SIGMOD Record and ACM Transactions on Database Systems.

In The Last Decade

John Paparrizos

42 papers receiving 1.5k citations

Hit Papers

k-Shape 2015 2026 2018 2022 2015 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. k-Shape
    2015 411 citations John Paparrizos, Luis Gravano profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John Paparrizos United States 18 893 891 290 233 121 44 1.5k
Yan Zhu United States 17 698 0.8× 647 0.7× 288 1.0× 184 0.8× 90 0.7× 45 1.3k
Chin‐Chia Michael Yeh United States 17 761 0.9× 715 0.8× 134 0.5× 176 0.8× 52 0.4× 52 1.2k
Thanawin Rakthanmanon United States 16 1.2k 1.4× 879 1.0× 105 0.4× 262 1.1× 49 0.4× 35 1.7k
Jun’ichi Takeuchi Japan 13 386 0.4× 855 1.0× 430 1.5× 45 0.2× 48 0.4× 65 1.3k
Congrui Huang China 5 403 0.5× 599 0.7× 239 0.8× 31 0.1× 72 0.6× 8 892
Jesin Zakaria United States 10 917 1.0× 611 0.7× 81 0.3× 187 0.8× 50 0.4× 11 1.2k
Vincent Oria United States 12 1.0k 1.1× 419 0.5× 212 0.7× 67 0.3× 37 0.3× 66 1.4k
Bill Chiu United States 9 1.4k 1.6× 1.0k 1.1× 213 0.7× 297 1.3× 70 0.6× 12 2.1k
Tak-chung Fu Hong Kong 11 902 1.0× 606 0.7× 86 0.3× 301 1.3× 74 0.6× 21 1.3k
Alp Kut Türkiye 8 355 0.4× 419 0.5× 100 0.3× 37 0.2× 71 0.6× 35 1.2k

Countries citing papers authored by John Paparrizos

Since Specialization
Citations

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

Fields of papers citing papers by John Paparrizos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Paparrizos

This figure shows the co-authorship network connecting the top 25 collaborators of John Paparrizos. A scholar is included among the top collaborators of John Paparrizos 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 John Paparrizos. John Paparrizos 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.
Paparrizos, John, et al.. (2025). Advances in Time-Series Anomaly Detection: Algorithms, Benchmarks, and Evaluation Measures. SPIRE - Sciences Po Institutional REpository. 6151–6161. 5 indexed citations
2.
Paparrizos, John, et al.. (2025). ShapX Engine: A Demonstration of Shapley Value Approximations. 107–110. 3 indexed citations
3.
Papapetrou, Odysseas, et al.. (2025). A Structured Study of Multivariate Time-Series Distance Measures. Proceedings of the ACM on Management of Data. 3(3). 1–29. 6 indexed citations
4.
Paparrizos, John, et al.. (2025). Understanding the Black Box: A Deep Empirical Dive into Shapley Value Approximations for Tabular Data. Proceedings of the ACM on Management of Data. 3(3). 1–31. 3 indexed citations
5.
Paparrizos, John, et al.. (2025). SPARTAN: Data-Adaptive Symbolic Time-Series Approximation. Proceedings of the ACM on Management of Data. 3(3). 1–30. 6 indexed citations
6.
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
7.
Gounaris, Anastasios, et al.. (2025). BURST: Rendering Clustering Techniques Suitable for Evolving Streams. Proceedings of the VLDB Endowment. 18(11). 4054–4063. 3 indexed citations
8.
Liu, Qinghua, Seunghak Lee, & John Paparrizos. (2025). TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection. Proceedings of the VLDB Endowment. 18(11). 4364–4379. 3 indexed citations
9.
Boniol, Paul, John Paparrizos, & Themis Palpanas. (2024). An Interactive Dive into Time-Series Anomaly Detection. 5382–5386. 7 indexed citations
10.
Liu, Qinghua, Paul Boniol, Themis Palpanas, & John Paparrizos. (2024). Time-Series Anomaly Detection: Overview and New Trends. Proceedings of the VLDB Endowment. 17(12). 4229–4232. 13 indexed citations
11.
Boniol, Paul, et al.. (2024). ADecimo: Model Selection for Time Series Anomaly Detection. 5441–5444. 6 indexed citations
12.
Liu, Chunwei, John Paparrizos, & Aaron J. Elmore. (2024). AdaEdge: A Dynamic Compression Selection Framework for Resource Constrained Devices. 1506–1519. 3 indexed citations
13.
Paparrizos, John, et al.. (2024). The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark. 108231–108261. 3 indexed citations
14.
Liu, Shinan, et al.. (2023). AMIR. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 7(1). 1–26. 7 indexed citations
15.
Paparrizos, John, et al.. (2023). Odyssey: An Engine Enabling the Time-Series Clustering Journey. Proceedings of the VLDB Endowment. 16(12). 4066–4069. 9 indexed citations
16.
Boniol, Paul, John Paparrizos, Themis Palpanas, et al.. (2022). Theseus. Proceedings of the VLDB Endowment. 15(12). 3702–3705. 15 indexed citations
17.
Paparrizos, John, et al.. (2021). VergeDB: A Database for IoT Analytics on Edge Devices.. Conference on Innovative Data Systems Research. 19 indexed citations
18.
Meier, Alexandra von, et al.. (2021). k-ShapeStream: Probabilistic Streaming Clustering for Electric Grid Events. 1–6. 16 indexed citations
19.
Paparrizos, John & Luis Gravano. (2017). Fast and Accurate Time-Series Clustering. ACM Transactions on Database Systems. 42(2). 1–49. 136 indexed citations
20.
Paparrizos, John & Luis Gravano. (2015). k-Shape. 1855–1870. 411 indexed citations breakdown →

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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