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» Using Recon for Data Cleaning
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CHI
2010
ACM
14 years 2 months ago
Cleanly: trashducation urban system
Half the world’s population is expected to live in urban areas by 2020. The high human density and changes in peoples’ consumption habits result in an everincreasing amount of...
Inbal Reif, Florian Alt, Juan David Hincapié...
IFL
2001
Springer
137views Formal Methods» more  IFL 2001»
13 years 12 months ago
A Generic Programming Extension for Clean
Abstract. Generic programming enables the programmer to define functions by induction on the structure of types. Defined once, such a generic function can be used to generate a s...
Artem Alimarine, Marinus J. Plasmeijer
PVLDB
2010
159views more  PVLDB 2010»
13 years 5 months ago
Explore or Exploit? Effective Strategies for Disambiguating Large Databases
Data ambiguity is inherent in applications such as data integration, location-based services, and sensor monitoring. In many situations, it is possible to “clean”, or remove, ...
Reynold Cheng, Eric Lo, Xuan Yang, Ming-Hay Luk, X...
INTERSPEECH
2010
13 years 2 months ago
What else is new than the hamming window? robust MFCCs for speaker recognition via multitapering
Usually the mel-frequency cepstral coefficients (MFCCs) are derived via Hamming windowed DFT spectrum. In this paper, we advocate to use a so-called multitaper method instead. Mul...
Tomi Kinnunen, Rahim Saeidi, Johan Sandberg, Maria...
SIGMOD
2011
ACM
205views Database» more  SIGMOD 2011»
12 years 10 months ago
Interaction between record matching and data repairing
Central to a data cleaning system are record matching and data repairing. Matching aims to identify tuples that refer to the same real-world object, and repairing is to make a dat...
Wenfei Fan, Jianzhong Li, Shuai Ma, Nan Tang, Weny...