Missing Value Imputation for Multi-attribute Sensor Data Streams via Message Propagation
Summary: Introduces MPIN, a message-propagation imputation network that recovers multi-attribute missing values in sliding windows under weak stream assumptions, with theoretical justification. Embeds MPIN in a continuous (data- and model-update) framework for efficient, accurate online imputation, outperforming prior methods. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiao Li (Roskilde University)
- 2. Huan Li (Zhejiang University)
- 3. Hua Lu (Roskilde University)
- 4. Christian S. Jensen (Aalborg University)
- 5. Varun Pandey (Technical University of Berlin)
- 6. Volker Markl (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
BibTeX Citation
@article{li_vldb24,
title = {{Missing Value Imputation for Multi-attribute Sensor Data Streams via Message Propagation}},
author = {Li, Xiao and Li, Huan and Lu, Hua and Jensen, Christian S. and Pandey, Varun and Markl, Volker},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {3},
pages = {345--358},
doi = {10.14778/3632093.3632100},
url = {https://doi.org/10.14778/3632093.3632100},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,573 | ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data | 2024 | VLDB | 5.1571823e-05 |
| 11,065 | DeXOR: Enabling xor in Decimal Space for Streaming Lossless Compression of Floating-point Data | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,278 | Streaming Pattern Discovery in Multiple Time-Series | 2005 | VLDB | 0.00011231952 |
| 2,125 | Discovery of Genuine Functional Dependencies from Relational Data with Missing Values | 2018 | VLDB | 9.0033119e-05 |
| 2,208 | Multi-Dimensional Regression Analysis of Time-Series Data Streams | 2002 | VLDB | 8.8382452e-05 |
| 2,239 | Query Optimization for Dynamic Imputation | 2017 | VLDB | 8.7745792e-05 |
| 2,547 | NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing | 2019 | VLDB | 8.3160201e-05 |
| 2,589 | Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series | 2020 | VLDB | 8.2507594e-05 |
| 4,922 | Clustering by Pattern Similarity in Large Data Sets | 2002 | SIGMOD | 6.3523165e-05 |
| 5,071 | Data Collection and Quality Challenges for Deep Learning | 2020 | VLDB | 6.2879908e-05 |
| 6,551 | ORBITS: Online Recovery of Missing Values in Multiple Time Series Streams | 2021 | VLDB | 5.750302e-05 |
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