Zobrazeno 1 - 10
of 223
pro vyhledávání: '"Naughton, Jeffrey"'
Autor:
Wu, Xi, Zhu, Zichen, Yu, Xiangyao, Deep, Shaleen, Viglas, Stratis, Cieslewicz, John, Jha, Somesh, Naughton, Jeffrey F.
A range of data insight analytical tasks involves analyzing a large set of tables of different schemas, possibly induced by various groupings, to find salient patterns. This paper presents Multi-Relational Algebra, an extension of the classic Relatio
Externí odkaz:
http://arxiv.org/abs/2311.04824
Autor:
Wu, Xi, Deep, Shaleen, Benassi, Joe, Li, Fengan, Zhang, Yaqi, Jang, Uyeong, Foster, James, Kim, Stella, Sun, Yujing, Nguyen, Long, Viglas, Stratis, Jha, Somesh, Cieslewicz, John, Naughton, Jeffrey F.
Many data insight questions can be viewed as searching in a large space of tables and finding important ones, where the notion of importance is defined in some adhoc user defined manner. This paper presents Holistic Cube Analysis (HoCA), a framework
Externí odkaz:
http://arxiv.org/abs/2302.00120
This work studies the problem of constructing a representative workload from a given input analytical query workload where the former serves as an approximation with guarantees of the latter. We discuss our work in the context of workload analysis an
Externí odkaz:
http://arxiv.org/abs/2011.05549
Database query processing requires algorithms for duplicate removal, grouping, and aggregation. Three algorithms exist: in-stream aggregation is most efficient by far but requires sorted input; sort-based aggregation relies on external merge sort; an
Externí odkaz:
http://arxiv.org/abs/2010.00152
Interactive time responses are a crucial requirement for users analyzing large amounts of data. Such analytical queries are typically run in a distributed setting, with data being sharded across thousands of nodes for high throughput. However, provid
Externí odkaz:
http://arxiv.org/abs/2002.01531
Autor:
Naughton, Jeffrey R.
Thesis advisor: Michael J. Naughton
Thesis advisor: Michael J. Burns
Recent progress in the study of the brain has been greatly facilitated by the development of new measurement tools capable of minimally-invasive, robust coupling to neuron
Thesis advisor: Michael J. Burns
Recent progress in the study of the brain has been greatly facilitated by the development of new measurement tools capable of minimally-invasive, robust coupling to neuron
Externí odkaz:
http://hdl.handle.net/2345/bc-ir:108037
Autor:
Li, Fengan, Chen, Lingjiao, Zeng, Yijing, Kumar, Arun, Naughton, Jeffrey F., Patel, Jignesh M., Wu, Xi
Data compression is a popular technique for improving the efficiency of data processing workloads such as SQL queries and more recently, machine learning (ML) with classical batch gradient methods. But the efficacy of such ideas for mini-batch stocha
Externí odkaz:
http://arxiv.org/abs/1702.06943
Providing machine learning (ML) over relational data is a mainstream requirement for data analytics systems. While almost all the ML tools require the input data to be presented as a single table, many datasets are multi-table, which forces data scie
Externí odkaz:
http://arxiv.org/abs/1612.07448
While significant progress has been made separately on analytics systems for scalable stochastic gradient descent (SGD) and private SGD, none of the major scalable analytics frameworks have incorporated differentially private SGD. There are two inter
Externí odkaz:
http://arxiv.org/abs/1606.04722
Despite of decades of work, query optimizers still make mistakes on "difficult" queries because of bad cardinality estimates, often due to the interaction of multiple predicates and correlations in the data. In this paper, we propose a low-cost post-
Externí odkaz:
http://arxiv.org/abs/1601.05748