
LineageAware Temporal Windows: Supporting Set Operations in TemporalProbabilistic Databases
In temporalprobabilistic (TP) databases, the combination of the tempora...
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MemoryEfficient Groupby Aggregates over MultiWay Joins
Aggregate computation in relational databases has long been done using t...
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CacheEfficient SweepingBased Interval Joins for Extended Allen Relation Predicates (Extended Version)
We develop a family of efficient planesweeping interval join algorithms...
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An Integrated FirstOrder Theory of Points and Intervals over Linear Orders (Part I)
There are two natural and wellstudied approaches to temporal ontology a...
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Consistency, Acyclicity, and Positive Semirings
In several different settings, one comes across situations in which the ...
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Joint Recognition and Segmentation of Actions via Probabilistic Integration of SpatioTemporal Fisher Vectors
We propose a hierarchical approach to multiaction recognition that perf...
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A NearOptimal Parallel Algorithm for Joining Binary Relations
We present a constantround algorithm in the massively parallel computat...
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Generalized LineageAware Temporal Windows: Supporting Outer and Anti Joins in TemporalProbabilistic Databases
The result of a temporalprobabilistic (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. 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 p have nonzero probabilities to be true and input tuples from n have nonzero probabilities to be false, respectively. For the computation of TP joins with negation, we introduce generalized lineageaware 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 realworld datasets.
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