
Numerical Linear Algebra in the Sliding Window Model
We initiate the study of numerical linear algebra in the sliding window ...
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SWOOP: Topk Similarity Joins over Set Streams
We provide efficient support for applications that aim to continuously f...
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SubO(log n) OutofOrder SlidingWindow Aggregation
Slidingwindow aggregation summarizes the most recent information in a d...
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Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension
The spectrum of a matrix contains important structural information about...
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Approaching Optimal Duplicate Detection in a Sliding Window
Duplicate detection is the problem of identifying whether a given item h...
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SRASTER: Contraction Clustering for Evolving Data Streams
Contraction Clustering (RASTER) is a very fast algorithm for densitybas...
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StreamTable: An Area Proportional Visualization for Tables with Flowing Streams
Let M be an r× c table with each cell weighted by a nonzero positive num...
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Smoothness of Schatten Norms and SlidingWindow Matrix Streams
Large matrices are often accessed as a roworder stream. We consider the setting where rows are timesensitive (i.e. they expire), which can be described by the slidingwindow roworder model, and provide the first (1+ϵ)approximation of Schatten pnorms in this setting. Our main technical contribution is a proof that Schatten pnorms in roworder streams are smooth, and thus fit the smoothhistograms technique of Braverman and Ostrovsky (FOCS 2007) for slidingwindow streams.
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