Model-based approximate querying in sensor networks

By Amol Deshpande, Carlos Guestrin, Samuel Madden, Joseph M Hellerstein, W. Hong. 2005

140 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Water Science and Technology, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Virginia Smith
2. Ameet Talwalkar
3. Tian Li
4. Anit Kumar Sahu
5. Maziar Sanjabi
6. Aryan Mokhtari
7. Shusen Wang
8. Luping Wang

In Signal Processing

1. Ameet Talwalkar

Citations in the corpus

• Federated Multi-Task Learning (2017, background): “Computing SQL-like queries across distributed, low-powered nodes is a decades-long area of research that has been explored under the purview of query processing in sensor networks, computing at the edge, and fog computing [31, 11, 32, 7, 17, 14].”
• Federated Learning: Challenges, Methods, and Future Directions (2019, background): “es across distributed, low-powered devices is a decades-long area of research that has been explored under the purview of query processing in sensor networks, computing at the edge, and fog computing [12, 29, 40, 49, 74]. Recent works have also considered training machine learning models centrally b”