Declarative Support for Sensor Data Cleaning

By Shawn R. Jeffery, Gustavo Alonso, M. Franklin, W. Hong, Jennifer Widom. 2006

279 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems and Management, Management Science and Operations Research

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. Sanjay Krishnan
2. M. Franklin
3. Eugene Wu
4. Ramesh Govindan
5. Ken Goldberg
6. Jiannan Wang
7. Marc-André Zöller
8. Tova Milo

In Information Systems and Management

1. Jiannan Wang
2. Sanjay Krishnan
3. Ken Goldberg
4. M. Franklin
5. Tova Milo
6. Marco F. Huber
7. Eugene Wu
8. Yinjun Wu

In Management Science and Operations Research

1. Sanjay Krishnan
2. Eugene Wu
3. M. Franklin
4. Jiannan Wang
5. Ken Goldberg
6. Tova Milo
7. Tim Kraska
8. Marco F. Huber

Citations in the corpus

• Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “Incorporating domain knowledge during data cleaning heavily increases the data quality (Jeffery et al., 2006; Messaoud et al., 2011; Salvador et al., 2016).”
• Survey on Automated Machine Learning (2019, background): “domain knowledge, the data quality can be heavily increased (Jeffery et al., 2006; Messaoud et al., 2011; Salvador et al., 2016).”
• Issues in data fusion for healthcare monitoring (2008, background): “In [25], a framework for building sensor data cleaning is proposed for pervasive healthcare application.”
• Sensor faults: Detection methods and prevalence in real-world datasets (2010, methodology): “Two recent papers [Khoussainova et al. 2006; Jeffery et al. 2006] have pro­posed a declarative approach to erroneous data detection and cleaning.”
• ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016, background): “Since these two types of errors do not affect the schema or leave any obvious signs of corruption (e.g., NULL values), model training may seemingly succeed–albeit with an inaccurate result.”
• ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models (2016, background): “For example, battery-powered sensors can transmit unreliable measurements when battery levels are low [21].”
• Outlier Detection Techniques for Wireless Sensor Networks: A Survey (2008, unknown): “Such dynamicity and heterogeneity increase the complexity of designing an appropriate outlier detection technique for WSNs. • Large-scale deployment.”
• A sample-and-clean framework for fast and accurate query processing on dirty data (2014, methodology): “For example, outlier detection [31,35] and rule-based approaches [17,23] have been proposed to solve this problem.”