Zobrazeno 1 - 10
of 205
pro vyhledávání: '"McSherry, Frank"'
Autor:
Lattuada, Andrea, McSherry, Frank
Distributed data processing systems have advanced through models that expose more and more opportunities for concurrency within a computation. The scheduling of these increasingly sophisticated models has become the bottleneck for improved throughput
Externí odkaz:
http://arxiv.org/abs/2210.06113
Incremental view maintenance has been for a long time a central problem in database theory. Many solutions have been proposed for restricted classes of database languages, such as the relational algebra, or Datalog. These techniques do not naturally
Externí odkaz:
http://arxiv.org/abs/2203.16684
Autor:
McSherry, Frank J.
Publikováno v:
Vital Speeches of the Day. 9/1/42, Vol. 8 Issue 22, p701. 4p.
Current systems for data-parallel, incremental processing and view maintenance over high-rate streams isolate the execution of independent queries. This creates unwanted redundancy and overhead in the presence of concurrent incrementally maintained q
Externí odkaz:
http://arxiv.org/abs/1812.02639
Autor:
Hoffmann, Moritz, Lattuada, Andrea, McSherry, Frank, Kalavri, Vasiliki, Liagouris, John, Roscoe, Timothy
We design and implement Megaphone, a data migration mechanism for stateful distributed dataflow engines with latency objectives. When compared to existing migration mechanisms, Megaphone has the following differentiating characteristics: (i) migratio
Externí odkaz:
http://arxiv.org/abs/1812.01371
We study the problem of finding and monitoring fixed-size subgraphs in a continually changing large-scale graph. We present the first approach that (i) performs worst-case optimal computation and communication, (ii) maintains a total memory footprint
Externí odkaz:
http://arxiv.org/abs/1802.03760
We advance the approach initiated by Chawla et al. for sanitizing (census) data so as to preserve the privacy of respondents while simultaneously extracting "useful" statistical information. First, we extend the scope of their techniques to a broad a
Externí odkaz:
http://arxiv.org/abs/1207.1371
Publikováno v:
Calibrating Data to Sensitivity in Private Data Analysis Proserpio, Davide, Sharon Goldberg, and Frank McSherry. "Calibrating Data to Sensitivity in Private Data Analysis." Proceedings of the VLDB Endowment 7.8 (2014)
We present an approach to differentially private computation in which one does not scale up the magnitude of noise for challenging queries, but rather scales down the contributions of challenging records. While scaling down all records uniformly is e
Externí odkaz:
http://arxiv.org/abs/1203.3453
We present new theoretical results on differentially private data release useful with respect to any target class of counting queries, coupled with experimental results on a variety of real world data sets. Specifically, we study a simple combination
Externí odkaz:
http://arxiv.org/abs/1012.4763
Consider the following problem: given a metric space, some of whose points are "clients", open a set of at most $k$ facilities to minimize the average distance from the clients to these facilities. This is just the well-studied $k$-median problem, fo
Externí odkaz:
http://arxiv.org/abs/0903.4510