Reproducibility Award
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"type" : "award",
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"source" : "UB",
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"individual" : "YES"
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},
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{
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"description" :
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"The SIGMOD 2019 paper titled \"Uncertainty Annotated Databases - A Lightweight Approach for Approximating Certain Answers\" received the 2020 SIGMOD Reproducability Award",
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"year" : 2020,
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"type" : "best-paper",
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"source" : "SIGMOD",
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"individual" : "NO"
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}
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],
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"chairs" : [
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---
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title: UADBs win Reproducibility Award
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author: Oliver Kennedy
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---
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Probabilistic and Incomplete databases are a principled way to handle data that
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isn't perfect (and really, who's data is perfect). Unfortunately, pretty much
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every PDB and IDB developed to date is insanely slower than their deterministic
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counterparts (to say nothing of how complex and finicky they are to use
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correctly). That's why, in collaboration with IIT, for the past five years,
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we've been working towards a more user-friendly approach to incomplete data
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management. Instead of trying to give people perfect answers, we just help
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them keep track of *what* is uncertain through annotations and provenance
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trickery. In other words, we're developing an Uncertainty Annotated Database
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System (or UADB).
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Thanks in large part to the heroic efforts of [Su Feng](http://cs.iit.edu/~dbgroup/members/sfeng.html),
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our [latest UADB paper](https://odin.cse.buffalo.edu/papers/2019/SIGMOD-UADBs.pdf)
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received the [SIGMOD 2020 Reproducibility Award](http://db-reproducibility.seas.harvard.edu/).
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