Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Rusu, Florin"'
In this work, we define the problem of finding an optimal query plan as finding spanning trees with low costs. This approach empowers the utilization of a series of spanning tree algorithms, thereby enabling systematic exploration of the plan search
Externí odkaz:
http://arxiv.org/abs/2403.04026
The Join Order Benchmark (JOB) has become the de facto standard to assess the performance of relational database query optimizers due to its complexity and completeness. In order to compute the optimal execution plan -- join order -- existing solutio
Externí odkaz:
http://arxiv.org/abs/2111.00163
Motivated by extreme multi-label classification applications, we consider training deep learning models over sparse data in multi-GPU servers. The variance in the number of non-zero features across training batches and the intrinsic GPU heterogeneity
Externí odkaz:
http://arxiv.org/abs/2110.07029
Cost-based query optimization remains a critical task in relational databases even after decades of research and industrial development. Query optimizers rely on a large range of statistical synopses -- including attribute-level histograms and table-
Externí odkaz:
http://arxiv.org/abs/2102.02440
Autor:
Souto, Yania Molina, Pereira, Rafael, Zorrilla, Rocío, Chaves, Anderson, Tsan, Brian, Rusu, Florin, Ogasawara, Eduardo, Ziviani, Artur, Porto, Fabio
In this paper, we present a cost-based approach for the automatic selection and allocation of a disjoint ensemble of black-box predictors to answer predictive spatio-temporal queries. Our approach is divided into two parts -- offline and online. Duri
Externí odkaz:
http://arxiv.org/abs/2005.11093
Autor:
Ma, Yujing, Rusu, Florin
The widely-adopted practice is to train deep learning models with specialized hardware accelerators, e.g., GPUs or TPUs, due to their superior performance on linear algebra operations. However, this strategy does not employ effectively the extensive
Externí odkaz:
http://arxiv.org/abs/2004.08771
Autor:
Rusu, Florin, Huang, Zhiyi
In this study, we present the first results of a complete implementation of the LDBC SNB benchmark -- interactive short, interactive complex, and business intelligence -- in two native graph database systems---Neo4j and TigerGraph. In addition to tho
Externí odkaz:
http://arxiv.org/abs/1907.07405
Selectivity estimation remains a critical task in query optimization even after decades of research and industrial development. Optimizers rely on accurate selectivities when generating execution plans. They maintain a large range of statistical syno
Externí odkaz:
http://arxiv.org/abs/1901.01488
Autor:
Turkay, Cagatay, Pezzotti, Nicola, Binnig, Carsten, Strobelt, Hendrik, Hammer, Barbara, Keim, Daniel A., Fekete, Jean-Daniel, Palpanas, Themis, Wang, Yunhai, Rusu, Florin
Data science requires time-consuming iterative manual activities. In particular, activities such as data selection, preprocessing, transformation, and mining, highly depend on iterative trial-and-error processes that could be sped-up significantly by
Externí odkaz:
http://arxiv.org/abs/1812.08032