Zobrazeno 1 - 10
of 332
pro vyhledávání: '"Ullman, Jeffrey"'
Autor:
Cussen, Daniel, Ullman, Jeffrey D.
Matrix multiplication consumes a large fraction of the time taken in many machine-learning algorithms. Thus, accelerator chips that perform matrix multiplication faster than conventional processors or even GPU's are of increasing interest. In this pa
Externí odkaz:
http://arxiv.org/abs/2307.01415
Autor:
Ullman, Jeffrey Layton
The endogenous hormones estrone and 17ò-estradiol support vertebrate growth and development, but slight increases above ambient concentrations may paradoxically induce endocrine disruption, leading to increased frequencies of reproductive diso
Externí odkaz:
http://hdl.handle.net/1969.1/5924
Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This paper continues along with the emerging trend in secure data processing that recognizes that the entire dataset may
Externí odkaz:
http://arxiv.org/abs/2005.06154
We propose the algorithms for performing multiway joins using a new type of coarse grain reconfigurable hardware accelerator~-- ``Plasticine''~-- that, compared with other accelerators, emphasizes high compute capability and high on-chip communicatio
Externí odkaz:
http://arxiv.org/abs/1905.13376
Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This paper continues along the emerging trend in secure data processing that recognizes that the entire dataset may not
Externí odkaz:
http://arxiv.org/abs/1812.09233
Autor:
AHO, ALFRED1, ULLMAN, JEFFREY2
Publikováno v:
Communications of the ACM. Feb2022, Vol. 65 Issue 2, p76-91. 16p. 3 Diagrams, 2 Charts.
In recent years, an increasing amount of data is collected in different and often, not cooperative, databases. The problem of privacy-preserving, distributed calculations over separated databases and, a relative to it, issue of private data release w
Externí odkaz:
http://arxiv.org/abs/1605.06143
Autor:
Ullman, Jeffrey D., Ullman, Jonathan
A common form of MapReduce application involves discovering relationships between certain pairs of inputs. Similarity joins serve as a good example of this type of problem, which we call a "some-pairs" problem. In the framework of Afrati et al. (VLDB
Externí odkaz:
http://arxiv.org/abs/1602.01443
In this paper, we investigate the problem of computing a multiway join in one round of MapReduce when the data may be skewed. We optimize on communication cost, i.e., the amount of data that is transferred from the mappers to the reducers. We identif
Externí odkaz:
http://arxiv.org/abs/1512.03921
We consider the problem of computing the data-cube marginals of a fixed order $k$ (i.e., all marginals that aggregate over $k$ dimensions), using a single round of MapReduce. The focus is on the relationship between the reducer size (number of inputs
Externí odkaz:
http://arxiv.org/abs/1509.08855