MCDB: a monte carlo approach to managing uncertain data

By Ravi Jampani, Fei Xu, Mingxi Wu, Luis Leopoldo Perez, C. Jermaine, Peter J. Haas. SIGMOD Conference, 2008

312 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ded1ab521c9839145040cb45c1f0c353536de8d5

How researchers see this paper

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

In Artificial Intelligence

1. Christopher R'e
2. Alexander J. Ratner
3. Ce Zhang
4. Christopher De Sa
5. Sen Wu
6. D. Narayanan
7. Peter Bailis
8. B. Recht

In Genetics

1. Samuel Madden
2. Christopher De Sa
3. Christopher R'e

In Signal Processing

1. Christopher R'e
2. Alexander J. Ratner
3. Christopher De Sa
4. Ce Zhang
5. Sen Wu
6. Samuel Madden
7. D. Narayanan
8. Peter Bailis

Citations in the corpus

• DeepDive: Declarative Knowledge Base Construction (2016, methodology): “We choose to study two popular classes of approximate inference techniques: sampling-based materialization (inspired by sampling-based probabilistic databases such as MCDB [18]) and variational-based materialization (inspired by techniques for approximating graphical models [41]).”
• MacroBase: Prioritizing Attention in Fast Data (2016, methodology): “In designing a specialized engine, we were inspired by several past projects, including Gigascope (specialized for network monitoring) [28], WaveScope (specialized for signal processing) [36], MCDB (specialized for Monte Carlo-based operators) [52], and Bismarck (providing extensible aggregation for”
• Towards a unified architecture for in-RDBMS analytics (2012, methodology): “So far, this arms race has centered around bringing individual statistical data mining techniques into an RDBMS, notably Support Vector Machines [35], Monte Carlo sampling [27, 51], Conditional Random Fields [25, 49], and Graphical Models [43, 50].”
• BayesDB: A probabilistic programming system for querying the probable implications of data (2015, background): “Systems such as SimSQL (Cai et al., 2013) and its ancestor, MCDB (Jampani et al., 2008), provide SQL operators for efficient Monte Carlo sampling.”
• MacroBase: Analytic Monitoring for the Internet of Things (2016, background): “nspired by several past projects, including Gigascope (specialized for network monitoring) [28], WaveScope (specialized for signal processing) [36], MCDB (specialized for Monte Carlo-based operators) [52], and Bismarck (providing extensible aggregation for gradient-based optimization) [33]. In addit”