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Scalable Top- k Spatio-Temporal Term Querying

Anders Skovsgaard Aarhus University. Darius Š idlauskas Aarhus University. Christian S. Jensen Aarhus University. Scalable Top- k Spatio-Temporal Term Querying. Fig. 2: Multiple Spatial Granularities. Fig. 1: Dynamic Summaries.

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Scalable Top- k Spatio-Temporal Term Querying

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  1. Anders Skovsgaard Aarhus University Darius ŠidlauskasAarhus University Christian S. JensenAarhus University Scalable Top-k Spatio-Temporal Term Querying Fig. 2: Multiple Spatial Granularities Fig. 1: Dynamic Summaries Fig. 5: Accuracy at Different Spatio-Temporal Granularities Stream: targeted-k m targeted-k Fig. 6: Average Number of Counters Maintained at Different Spatio-Temporal Granularities Active Summary Archived Summary Fig. 7: Large Scale Stream Processing Fig. 3: Multiple Temporal Granularities Fig. 4: Merging of Summaries Fig. 8: Top-k Query Processing MADALGO – Center for Massive Data Algorithmics, a Center of the Danish National Research Foundation

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