Zobrazeno 1 - 10
of 550
pro vyhledávání: '"Haas, Peter J."'
Autor:
Mai, Anh L., Wang, Pengyu, Abouzied, Azza, Brucato, Matteo, Haas, Peter J., Meliou, Alexandra
A package query returns a package - a multiset of tuples - that maximizes or minimizes a linear objective function subject to linear constraints, thereby enabling in-database decision support. Prior work has established the equivalence of package que
Externí odkaz:
http://arxiv.org/abs/2307.02860
Business users perform data analysis to inform decisions for improving business processes and outcomes despite having limited formal technical training. While earlier work has focused on data analysts' and data scientists' practices and challenges, l
Externí odkaz:
http://arxiv.org/abs/2212.13643
The fundamental goal of business data analysis is to improve business decisions using data. Business users often make decisions to achieve key performance indicators (KPIs) such as increasing customer retention or sales, or decreasing costs. To disco
Externí odkaz:
http://arxiv.org/abs/2109.06160
Probability proportional to size (PPS) sampling schemes with a target sample size aim to produce a sample comprising a specified number $n$ of items while ensuring that each item in the population appears in the sample with a probability proportional
Externí odkaz:
http://arxiv.org/abs/2105.10809
Autor:
Wolf, Arnold Jacob
Publikováno v:
The Journal of Religion, 2006 Oct . 86(4), 704-705.
Externí odkaz:
https://www.jstor.org/stable/10.1086/509688
Publikováno v:
SIGMOD 2020
We provide methods for in-database support of decision making under uncertainty. Many important decision problems correspond to selecting a package (bag of tuples in a relational database) that jointly satisfy a set of constraints while minimizing so
Externí odkaz:
http://arxiv.org/abs/2103.06784
To maintain the accuracy of supervised learning models in the presence of evolving data streams, we provide temporally-biased sampling schemes that weight recent data most heavily, with inclusion probabilities for a given data item decaying over time
Externí odkaz:
http://arxiv.org/abs/1906.05677
Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key assumption that the training data is distributed identically to the t
Externí odkaz:
http://arxiv.org/abs/1808.08294
To maintain the accuracy of supervised learning models in the presence of evolving data streams, we provide temporally-biased sampling schemes that weight recent data most heavily, with inclusion probabilities for a given data item decaying exponenti
Externí odkaz:
http://arxiv.org/abs/1801.09709