John Paparrizos

56 papers A* 7A 2B 1Journal 38Unranked 7
YearRankTypeTitle / Venue / Authors
2026 A conf
A Comprehensive Guide to Time-Series Anomaly Detection.
WSDM
John Paparrizos, Paul Boniol, Qinghua Liu, Themis Palpanas
2025 J jnl
A Structured Study of Multivariate Time-Series Distance Measures.
Proc. ACM Manag. Data
Jens E. d'Hondt, Haojun Li, Fan Yang, Odysseas Papapetrou, John Paparrizos
2025 — conf
Advances in Time-Series Anomaly Detection: Algorithms, Benchmarks, and Evaluation Measures.
KDD (2)
John Paparrizos, Paul Boniol, Qinghua Liu, Themis Palpanas
2025 J jnl
BURST: Rendering Clustering Techniques Suitable for Evolving Streams.
Proc. VLDB Endow.
Apostolos Giannoulidis, Anastasios Gounaris, John Paparrizos
2025 J jnl
Beyond Compression: A Comprehensive Evaluation of Lossless Floating-Point Compression.
Proc. VLDB Endow.
Kaisei Hishida, Chunwei Liu, John Paparrizos, Aaron J. Elmore
2025 J jnl
EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection.
Proc. VLDB Endow.
Qinghua Liu, Seunghak Lee, John Paparrizos
2025 J jnl
Front Matter.
Proc. VLDB Endow.
Sonia Bergamaschi, Sourav S. Bhowmick, Philippe Bonnet, Surajit Chaudhuri, Xiaoou Ding, Hakan Ferhatosmanoglu, Raul Castro Fernandez, Jana Giceva, Madelon Hulsebos, Alexandra Meliou, Nikos Ntarmos, Themis Palpanas, John Paparrizos, Norman Paton, Subhadeep Sarkar, Giovanni Simonini, Nesime Tatbul, Jiuqi Wei, Jingren Zhou
2025 J jnl
MLLM4TS: Leveraging Vision and Multimodal Language Models for General Time-Series Analysis.
CoRR
Qinghua Liu, Sam Heshmati, Zheda Mai, Zubin Abraham, John Paparrizos, Liu Ren
2025 J jnl
MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection.
CoRR
Emmanouil Sylligardos, John Paparrizos, Themis Palpanas, Pierre Senellart, Paul Boniol
2025 J jnl
MSAD: A deep dive into model selection for time series anomaly detection.
VLDB J.
Emmanouil Sylligardos, John Paparrizos, Themis Palpanas, Pierre Senellart, Paul Boniol
2025 J jnl
SAIL: A Voyage to Symbolic Approximation Solutions for Time-Series Analysis.
Proc. VLDB Endow.
Fan Yang, John Paparrizos
2025 J jnl
SPARTAN: Data-Adaptive Symbolic Time-Series Approximation.
Proc. ACM Manag. Data
Fan Yang, John Paparrizos
2025 — conf
ShapX Engine: A Demonstration of Shapley Value Approximations.
SIGMOD Conference Companion
Suchit Gupte, John Paparrizos
2025 J jnl
TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection [E, A & B].
Proc. VLDB Endow.
Qinghua Liu, Seunghak Lee, John Paparrizos
2025 J jnl
Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods.
Proc. VLDB Endow.
John Paparrizos, Bogireddy Sai Prasanna Teja
2025 J jnl
Understanding the Black Box: A Deep Empirical Dive into Shapley Value Approximations for Tabular Data.
Proc. ACM Manag. Data
Suchit Gupte, John Paparrizos
2025 J jnl
VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly Detection.
CoRR
Paul Boniol, Ashwin K. Krishna, Marine Bruel, Qinghua Liu, Mingyi Huang, Themis Palpanas, Ruey S. Tsay, Aaron J. Elmore, Michael J. Franklin, John Paparrizos
2025 J jnl
VUS: effective and efficient accuracy measures for time-series anomaly detection.
VLDB J.
Paul Boniol, Ashwin K. Krishna, Marine Bruel, Qinghua Liu, Mingyi Huang, Themis Palpanas, Ruey S. Tsay, Aaron J. Elmore, Michael J. Franklin, John Paparrizos
2024 J jnl
A Survey on Time-Series Distance Measures.
CoRR
John Paparrizos, Haojun Li, Fan Yang, Kaize Wu, Jens E. d'Hondt, Odysseas Papapetrou
2024 A* conf
ADecimo: Model Selection for Time Series Anomaly Detection.
ICDE
Paul Boniol, Emmanouil Sylligardos, John Paparrizos, Panos E. Trahanias, Themis Palpanas
2024 A* conf
AdaEdge: A Dynamic Compression Selection Framework for Resource Constrained Devices.
ICDE
Chunwei Liu, John Paparrizos, Aaron J. Elmore
2024 A* conf
An Interactive Dive into Time-Series Anomaly Detection.
ICDE
Paul Boniol, John Paparrizos, Themis Palpanas
2024 — conf
Beyond the Dimensions: A Structured Evaluation of Multivariate Time Series Distance Measures.
ICDEW
Jens E. d'Hondt, Odysseas Papapetrou, John Paparrizos
2024 J jnl
Bridging the Gap: A Decade Review of Time-Series Clustering Methods.
CoRR
John Paparrizos, Fan Yang, Haojun Li
2024 J jnl
Dive into Time-Series Anomaly Detection: A Decade Review.
CoRR
Paul Boniol, Qinghua Liu, Mingyi Huang, Themis Palpanas, John Paparrizos
2024 A* conf
The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark.
NeurIPS
Qinghua Liu, John Paparrizos
2024 J jnl
Time-Series Anomaly Detection: Overview and New Trends.
Proc. VLDB Endow.
Qinghua Liu, Paul Boniol, Themis Palpanas, John Paparrizos
2023 J jnl
AMIR: Active Multimodal Interaction Recognition from Video and Network Traffic in Connected Environments.
Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.
Shinan Liu, Tarun Mangla, Ted Shaowang, Jinjin Zhao, John Paparrizos, Sanjay Krishnan, Nick Feamster
2023 J jnl
Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances.
Proc. VLDB Endow.
John Paparrizos, Kaize Wu, Aaron J. Elmore, Christos Faloutsos, Michael J. Franklin
2023 J jnl
Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series.
Proc. VLDB Endow.
Emmanouil Sylligardos, Paul Boniol, John Paparrizos, Panos E. Trahanias, Themis Palpanas
2023 B conf
New Trends in Time Series Anomaly Detection.
EDBT
Paul Boniol, John Paparrizos, Themis Palpanas
2023 J jnl
Odyssey: An Engine Enabling The Time-Series Clustering Journey.
Proc. VLDB Endow.
John Paparrizos, Sai Prasanna Teja Reddy
2023 J jnl
Querying Time-Series Data: A Comprehensive Comparison of Distance Measures.
IEEE Data Eng. Bull.
John Paparrizos, Chunwei Liu, Aaron J. Elmore, Michael J. Franklin
2022 A* conf
Fast Adaptive Similarity Search through Variance-Aware Quantization.
ICDE
John Paparrizos, Ikraduya Edian, Chunwei Liu, Aaron J. Elmore, Michael J. Franklin
2022 J jnl
TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection.
Proc. VLDB Endow.
John Paparrizos, Yuhao Kang, Paul Boniol, Ruey S. Tsay, Themis Palpanas, Michael J. Franklin
2022 J jnl
Theseus: Navigating the Labyrinth of Time-Series Anomaly Detection.
Proc. VLDB Endow.
Paul Boniol, John Paparrizos, Yuhao Kang, Themis Palpanas, Ruey S. Tsay, Aaron J. Elmore, Michael J. Franklin
2022 J jnl
Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection.
Proc. VLDB Endow.
John Paparrizos, Paul Boniol, Themis Palpanas, Ruey S. Tsay, Aaron J. Elmore, Michael J. Franklin
2021 J jnl
Decomposed Bounded Floats for Fast Compression and Queries.
Proc. VLDB Endow.
Chunwei Liu, Hao Jiang, John Paparrizos, Aaron J. Elmore
2021 — conf
Good to the Last Bit: Data-Driven Encoding with CodecDB.
SIGMOD Conference
Hao Jiang, Chunwei Liu, John Paparrizos, Andrew A. Chien, Jihong Ma, Aaron J. Elmore
2021 J jnl
SAND in Action: Subsequence Anomaly Detection for Streams.
Proc. VLDB Endow.
Paul Boniol, John Paparrizos, Themis Palpanas, Michael J. Franklin
2021 J jnl
SAND: Streaming Subsequence Anomaly Detection.
Proc. VLDB Endow.
Paul Boniol, John Paparrizos, Themis Palpanas, Michael J. Franklin
2021 A conf
VergeDB: A Database for IoT Analytics on Edge Devices.
CIDR
John Paparrizos, Chunwei Liu, Bruno Barbarioli, Johnny Hwang, Ikraduya Edian, Aaron J. Elmore, Michael J. Franklin, Sanjay Krishnan
2020 — conf
Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures.
SIGMOD Conference
John Paparrizos, Chunwei Liu, Aaron J. Elmore, Michael J. Franklin
2020 J jnl
PIDS: Attribute Decomposition for Improved Compression and Query Performance in Columnar Storage.
Proc. VLDB Endow.
Hao Jiang, Chunwei Liu, Qi Jin, John Paparrizos, Aaron J. Elmore
2019 J jnl
Artificial Intelligence in Resource-Constrained and Shared Environments.
ACM SIGOPS Oper. Syst. Rev.
Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin, John Paparrizos, Zechao Shang, Adam Dziedzic, Rui Liu
2019 A* conf
Band-limited Training and Inference for Convolutional Neural Networks.
ICML
Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin
2019 J jnl
Band-limited Training and Inference for Convolutional Neural Networks.
CoRR
Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin
2019 J jnl
GRAIL: Efficient Time-Series Representation Learning.
Proc. VLDB Endow.
John Paparrizos, Michael J. Franklin
2018 — —
Fast, Scalable, and Accurate Algorithms for Time-Series Analysis.
John Paparrizos
2017 J jnl
Fast and Accurate Time-Series Clustering.
ACM Trans. Database Syst.
John Paparrizos, Luis Gravano
2016 A* conf
Detecting Devastating Diseases in Search Logs.
KDD
John Paparrizos, Ryen W. White, Eric Horvitz
2016 J jnl
Predicting the impact of scientific concepts using full-text features.
J. Assoc. Inf. Sci. Technol.
Kathy McKeown, Hal Daumé III, Snigdha Chaturvedi, John Paparrizos, Kapil Thadani, Pablo Barrio, Or Biran, Suvarna Bothe, Michael Collins, Kenneth R. Fleischmann, Luis Gravano, Rahul Jha, Ben King, Kevin McInerney, Taesun Moon, Arvind Neelakantan, Diarmuid Ó Séaghdha, Dragomir R. Radev, Thomas Clay Templeton, Simone Teufel
2016 — conf
The Social Dynamics of Language Change in Online Networks.
SocInfo (1)
Rahul Goel, Sandeep Soni, Naman Goyal, John Paparrizos, Hanna M. Wallach, Fernando Diaz, Jacob Eisenstein
2016 J jnl
The Social Dynamics of Language Change in Online Networks.
CoRR
Rahul Goel, Sandeep Soni, Naman Goyal, John Paparrizos, Hanna M. Wallach, Fernando Diaz, Jacob Eisenstein
2016 J jnl
k-Shape: Efficient and Accurate Clustering of Time Series.
SIGMOD Rec.
John Paparrizos, Luis Gravano
2015 — conf
k-Shape: Efficient and Accurate Clustering of Time Series.
SIGMOD Conference
John Paparrizos, Luis Gravano