A subjective interestingness measure for multidimensional BI queries is defined by modeling user belief as a probability distribution learned via random walk from past interactions, cube schema, and other users' activities.
In 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, April 16-19
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.DB 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Subjective Interestingness measure for Business Intelligence explorations
A subjective interestingness measure for multidimensional BI queries is defined by modeling user belief as a probability distribution learned via random walk from past interactions, cube schema, and other users' activities.