4 research outputs found
Generalized Lineage-Aware Temporal Windows: Supporting Outer and Anti Joins in Temporal-Probabilistic Databases
The result of a temporal-probabilistic (TP) join with negation includes, at
each time point, the probability with which a tuple of a positive relation
matches none of the tuples in a negative relation , for a
given join condition . TP outer and anti joins thus resemble the
characteristics of relational outer and anti joins also in the case when there
exist time points at which input tuples from have non-zero
probabilities to be and input tuples from have non-zero
probabilities to be , respectively. For the computation of TP joins with
negation, we introduce generalized lineage-aware temporal windows, a mechanism
that binds an output interval to the lineages of all the matching valid tuples
of each input relation. We group the windows of two TP relations into three
disjoint sets based on the way attributes, lineage expressions and intervals
are produced. We compute all windows in an incremental manner, and we show that
pipelined computations allow for the direct integration of our approach into
PostgreSQL. We thereby alleviate the prevalent redundancies in the interval
computations of existing approaches, which is proven by an extensive
experimental evaluation with real-world datasets
Outer and Anti Joins in Temporal-Probabilistic Databases
The result of a temporal-probabilistic (TP) join with negation includes, at each time point, the probability with which a tuple of a positive relation p matches none of the tuples in a negative relation n, for a given join condition θ. For the computation of TP joins with negation, we introduce generalized lineage-aware temporal windows, a mechanism that binds an interval to the lineages of all the matching valid tuples of each input relation. We compute these windows in an incremental manner, and we show that pipelined computations allow for the direct integration of our approach into PostgreSQL. We thereby alleviate the prevalent redundancies in the interval computations of existing approaches, which is proven by an extensive experimental evaluation with real-world datasets