TY - GEN
T1 - Analytics over probabilistic unmerged duplicates
AU - Ioannou, Ekaterini
AU - Garofalakis, Minos N.
PY - 2014
Y1 - 2014
N2 - This paper introduces probabilistic databases with unmerged duplicates (DB ud ), i.e., databases containing probabilistic information about instances found to describe the same real-world objects. We discuss the need for efficiently querying such databases and for supporting practical query scenarios that require analytical or summarized information. We also sketch possible methodologies and techniques that would allow performing efficient processing of queries over such probabilistic databases, and especially without the need to materialize the (potentially, huge) collection of all possible deduplication worlds.
AB - This paper introduces probabilistic databases with unmerged duplicates (DB ud ), i.e., databases containing probabilistic information about instances found to describe the same real-world objects. We discuss the need for efficiently querying such databases and for supporting practical query scenarios that require analytical or summarized information. We also sketch possible methodologies and techniques that would allow performing efficient processing of queries over such probabilistic databases, and especially without the need to materialize the (potentially, huge) collection of all possible deduplication worlds.
U2 - 10.1007/978-3-319-11508-5_17
DO - 10.1007/978-3-319-11508-5_17
M3 - Conference contribution
SN - 9783319115078
T3 - Lecture Notes in Computer Science
SP - 203
EP - 208
BT - Proceedings of the International Conference on Scalable Uncertainty Management (SUM2014)
PB - Springer
CY - Cham
ER -