John's

Papers in Referred Journals and Conferences

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TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection

Qinghua Liu, Seunghak Lee, and John Paparrizos

Proceedings of the VLDB Endowment (PVLDB 2025) Journal, Volume 18, pages 4364-4379

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Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods

John Paparrizos and Teja Bogireddy

Proceedings of the VLDB Endowment (PVLDB 2025) Journal, Volume 18, pages 4380-4395

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Beyond Compression: A Comprehensive Evaluation of Lossless Floating-Point Compression

Kaisei Hishida, Chunwei Liu, John Paparrizos and Aaron Elmore

Proceedings of the VLDB Endowment (PVLDB 2025) Journal, Volume 18, pages 4396-4409

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BURST: Rendering Clustering Techniques Suitable for Evolving Streams

Apostolos Giannoulidis, Anastasios Gounaris, and John Paparrizos

Proceedings of the VLDB Endowment (PVLDB 2025) Journal, Volume 18, pages 4054-4063

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SPARTAN: Data-Adaptive Symbolic Time-Series Approximation

Fan Yang and John Paparrizos

Proceedings of the ACM on Management of Data (PACMMOD) Journal, Volume 3, Issue 3, Article 220, pages 1-30

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Understanding the Black Box: A Deep Empirical Dive into Shapley Value Approximations for Feature Explanations

Suchit Gupte and John Paparrizos

Proceedings of the ACM on Management of Data (PACMMOD) Journal, Volume 3, Issue 3, Article 232, pages 1-31

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A Structured Study of Multivariate Time-Series Distance Measures

Jens d'Hondt, Haojun Li, Fan Yang, Odysseas Papapetrou and John Paparrizos

Proceedings of the ACM on Management of Data (PACMMOD) Journal, Volume 3, Issue 3, Article 121, pages 1-29

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Advances in Time-Series Anomaly Detection: Algorithms, Benchmarks, and Evaluation Measures

John Paparrizos, Qinghua Liu, Paul Boniol, and Themis Palpanas

2025 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD 2025), pages 1-11

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VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly Detection

Paul Boniol, Ashwin K Krishna, Marine Bruel, Qinghua Liu, Mingyi Huang, Themis Palpanas, Ruey S Tsay, Aaron Elmore, Michael J Franklin, and John Paparrizos

The VLDB Journal (VLDBJ 2025), Volume 34, Issue 3, pages 1-32

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The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark

Qinghua Liu and John Paparrizos

Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pages 108231-108261

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AdaEdge: A Dynamic Compression Selection Framework for Resource Constrained Devices

Chunwei Liu, John Paparrizos, and Aaron Elmore

40th IEEE International Conference on Data Engineering (IEEE ICDE 2024), pages 1506-1519

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Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances

John Paparrizos, Kaize Wu, Aaron Elmore, Christos Faloutsos, and Michael Franklin

Proceedings of the VLDB Endowment (PVLDB 2023) Journal, Volume 16, pages 2019–2032

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Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series

Emmanouil Sylligardos, Paul Boniol, John Paparrizos, Panos Trahanias, and Themis Palpanas

Proceedings of the VLDB Endowment (PVLDB 2023) Journal, Volume 16, pages 3418–3432

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AMIR: Active Multimodal Interaction Recognition from Video and Network Traffic

Shinan Liu, Tarun Mangla, Ted Shaowang, Jinjin Zhao, John Paparrizos, Sanjay Krishnan, Nick Feamster

ACM International Conference on Pervasive and Ubiquitous Computing (ACM UbiComp 2023), pages 1-26

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TSB‑UAD: An End‑to‑End Benchmark Suite for Univariate Time‑Series Anomaly Detection

John Paparrizos, Yuhao Kang, Paul Boniol, Ruey Tsay, Themis Palpanas, and Michael Franklin

Proceedings of the VLDB Endowment (PVLDB 2022) Journal, Volume 15, pages 1697–1711

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Volume Under the Surface: A New Accuracy Evaluation Measure for Time‑Series Anomaly Detection

John Paparrizos, Paul Boniol, Themis Palpanas, Ruey Tsay, Aaron Elmore, and Michael J. Franklin

Proceedings of the VLDB Endowment (PVLDB 2022) Journal, Volume 15, pages 2774‑2787

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Fast Adaptive Similarity Search through Variance‑Aware Quantization

John Paparrizos, Ikraduya Edian, Chunwei Liu, Aaron Elmore, and Michael J. Franklin

38th IEEE International Conference on Data Engineering (IEEE ICDE 2022), pages 2969‑2983

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VergeDB: A Database for IoT Analytics on Edge Devices

John Paparrizos, Chunwei Liu, Bruno Barbarioli, Johnny Hwang, Ikraduya Edian, Aaron J. Elmore, et al.

11th Conference on Innovative Data Systems Research (CIDR 2021), pages 1-8

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Good to the Last Bit: Data‑Driven Encoding with CodecDB

Hao Jiang, Chunwei Liu, John Paparrizos, Andrew Chien, Jihong Ma, and Aaron Elmore

2021 ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 2021), pages 843‑856

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Decomposed Bounded Floats for Fast Compression and Queries

Chunwei Liu, Hao Jiang, John Paparrizos, and Aaron Elmore

Proceedings of the VLDB Endowment (PVLDB 2021) Journal, Volume 14, pages 2586‑2598

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SAND: Streaming Subsequence Anomaly Detection

Paul Boniol, John Paparrizos, Themis Palpanas, and Michael Franklin

Proceedings of the VLDB Endowment (PVLDB 2021) Journal, Volume 14, pages 1717‑1729

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Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures

John Paparrizos Chunwei Liu, Aaron J. Elmore, and Michael J. Franklin

2020 ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 2020), pages 1887‑1905

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PIDS: Attribute Decomposition for Improved Compression and Query Performance in Columnar Storage

Hao Jiang, Chunwei Liu, John Paparrizos, and Aaron J. Elmore

Proceedings of the VLDB Endowment (PVLDB 2020) Journal, Volume 13, pages 925‑938

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GRAIL: Efficient Time-Series Representation Learning

John Paparrizos and Michael Franklin

Proceedings of the VLDB Endowment (PVLDB 2019) Journal, Volume 12, pages 1762‑1777

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Band-limited Training and Inference for Convolutional Neural Networks

Adam Dziedzic1, John Paparrizos1, Sanjay Krishnan, Aaron Elmore, and Michael Franklin

1. Alphabetical order; Equal contribution

36th International Conference on Machine Learning (ICML 2019), pages 1745‑1754

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Fast, Scalable, and Accurate Algorithms for Time-Series Analysis

John Paparrizos

Ph.D. Dissertation, Columbia University

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Fast and Accurate Time-Series Clustering

John Paparrizos and Luis Gravano

ACM Transactions on Database Systems (ACM TODS 2017) Journal, Volume 42, pages 1-49

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Screening for Pancreatic Adenocarcinoma using Signals from Web Search Logs

John Paparrizos, Ryen W. White, and Eric Horvitz

Journal of Oncology Practice (JOP 2016), Volume 12, pages 737‑744

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Predicting the Impact of Scientific Concepts Using Full Text Features

Kathy McKeown,1 Hal Daume,1 Snigdha Chaturvedi,2 John Paparrizos,2 Kapil Thadani,2 et al.

1. Lead PIs 2. Lead student authors in alphabetic order

Journal of the American Society for Information Science and Technology (JASIST 2016), Volume 67, pages 2684‑2696

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The Social Dynamics of Language Chance in Online Networks

Rahul Goel, Sandeep Soni, Naman Goyal, John Paparrizos, Hanna Wallach, et al.

International Conference on Social Informatics (SocInfo 2016), pages 41‑57

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Detecting Devastating Diseases in Search Logs

John Paparrizos, Ryen W. White, and Eric Horvitz

2016 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD 2016), pages 559‑568

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k-Shape: Efficient and Accurate Clustering of Time Series

John Paparrizos and Luis Gravano

2015 ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 2015), pages 1855‑1870