In ICDE, 2010.Bruno, N. Galindo-Legaria, C. ; Joshi, M. : Polynomial heuristics for query optimization. In: 26th IEEE International Conferences on Data Engineering (ICDE 2010).Bruno N.: Polynomial heuristics for query optimization. In: Proc. of Int. Conf. of Data Engineering. pp.589-...
Research on query optimization has traditionally focused on exhaustive enumeration of an exponential number of candidate plans. Alternatively, heuristics for query optimization are restricted in several ways, such as by either focusing on join predicates only, ignoring the availability of indexes, or in...
The standard approach to join-query optimization is cost based, which requires developing a cost model, assigning an estimated cost to each query-processing plan, and searching in the space of all plans for a plan of minimal cost. But as the number of joins increases, the size of the ...
Query optimization in XML structured-document databases Che, D., Aberer, K., Özsu, M.T.: Query optimization in XML structured-document databases. The VLDB Journal The International Journal on Very Large... D Che,K Aberer,M. Tamer Oezsu - 《Vldb Journal》 被引量: 112发表: 2006年 ...
In this paper, we implement a tabu search heuristic, a probabilistic tabu search heuristic, a simulated annealing heuristic, and a hybrid tabu search heuri... WC Chiang,C Chi - 《European Journal of Operational Research》 被引量: 122发表: 1998年 Query reformulation strategies for an intelligent...
Stream Query Optimization Chapter© 2019 Stateful Load Balancing for Parallel Stream Processing Chapter© 2018 Cost Optimization of Data Flows Based on Task Re-ordering Chapter© 2017 References Avnur, R., Hellerstein, J.M.: Eddies: Continuously adaptive query processing. In: Proceedings of ACM...
anevolutionary algorithmwill execute a mutation operation and crossover operation at a given rate for every iteration of the algorithm. In this case, there is little to be said about the sequences of perturbations which are generated by these types of algorithm. However, in the case of a sele...
optimization, we used the ADAM optimizer 2 with an initial learning rate of 1e-5 and mini-batch size of 64. For regularization, we applied a weight decay of 1e -8 on all network weights anddropout with probability 0.5 on the fully connected layers. We ran our tests for 20 epochs or ...
if(!inProgress) apt.StartCommand("Add country code"); apt.AddMetaDataKey(META_KeyName(wed_AddMetaDataCountry), code3 + country); if (!inProgress) apt.CommitCommand(); return true; } 2 changes: 1 addition & 1 deletion 2 src/WEDImportExport/WED_MetaDataDefaults.h Original file line...
may not be available in the C# bindings. 1. Identifying the reward function SelfTune's optimization algorithm(e.g., Bluefin) uses a reward to compute a gradient-ascent style update to the parameter values. This reward can be any health or utilization metric of the current state of the ...