485 research outputs found
MAP inference via Block-Coordinate Frank-Wolfe Algorithm
We present a new proximal bundle method for Maximum-A-Posteriori (MAP)
inference in structured energy minimization problems. The method optimizes a
Lagrangean relaxation of the original energy minimization problem using a multi
plane block-coordinate Frank-Wolfe method that takes advantage of the specific
structure of the Lagrangean decomposition. We show empirically that our method
outperforms state-of-the-art Lagrangean decomposition based algorithms on some
challenging Markov Random Field, multi-label discrete tomography and graph
matching problems
Evaluation of Sql Performance Tuning Features in Oracle Database Software
Timely access to data is one of the most important requirements of database management systems. Having access to data in acceptable time is crucial for efficient decision making. Tuning inefficient SQL is one of the most important elements of enhancing performance of databases. With growing repositories and complexity of underlying data management systems, maintaining decent levels of performance and tuning has become a complicated task. DBMS providers acknowledge this tendency and developed tools and features that simplify the process. DBAs and developers have to make use of these tools in the attempt to provide their companies with stable and efficient systems. Performance tuning functions differ from platform to platform. Oracle is the main DBMS provider in the world, and this study focuses on the tools provided in all releases of their software. A thorough literature analysis is performed in order to gain understanding of the functionality and assessment of each tool is performed. It also provides insight into factual utilization of tools by gathering responses through the use of an online survey and an analysis of the results
Adaptive model selection method for a conditionally Gaussian semimartingale regression in continuous time
This paper considers the problem of robust adaptive efficient estimating of a
periodic function in a continuous time regression model with the dependent
noises given by a general square integrable semimartingale with a conditionally
Gaussian distribution. An example of such noise is the non-Gaussian
Ornstein-Uhlenbeck-Levy processes. An adaptive model selection procedure, based
on the improved weighted least square estimates, is proposed. Under some
conditions on the noise distribution, sharp oracle inequality for the robust
risk has been proved and the robust efficiency of the model selection procedure
has been established. The numerical analysis results are given.Comment: 50 pages, 2 figures. arXiv admin note: text overlap with
arXiv:1710.03111, arXiv:1712.0645
Performance Tuning of Database Systems Using a Context-aware Approach
Database system performance problems have a cascading effect into all aspects of an enterprise application. Database vendors and application developers provide guidelines, best practices and even initial database settings for good
performance. But database performance tuning is not a one-off task. Database administrators have to keep a constant eye on the database performance as the tuning work carried out earlier could be invalidated due to multitude of reasons. Before engaging in a performance tuning endeavor, a database administrator must prioritize which tuning tasks to carry out first. This prioritization is done based on which tuning action would yield highest performance benefit. However, this prediction may not always be accurate. Experiment-based performance tuning methodologies have been introduced as an alternative to prediction-based performance tuning approaches. Experimenting on a representative system similar to the production one allows a database administrator to accurately gauge the performance gain for a particular tuning task. In this paper we propose a novel approach to experiment-based performance tuning with the use of a context-aware application model. Using a proof-of-concept implementation we show how it could be used to automate the detection of performance changes, experiment creation and evaluate the performance tuning outcomes for mixed workload types through database configuration parameter changes
Non parametric finite translation mixtures with dependent regime
In this paper we consider non parametric finite translation mixtures. We
prove that all the parameters of the model are identifiable as soon as the
matrix that defines the joint distribution of two consecutive latent variables
is non singular and the translation parameters are distinct. Under this
assumption, we provide a consistent estimator of the number of populations, of
the translation parameters and of the distribution of two consecutive latent
variables, which we prove to be asymptotically normally distributed under mild
dependency assumptions. We propose a non parametric estimator of the unknown
translated density. In case the latent variables form a Markov chain (Hidden
Markov models), we prove an oracle inequality leading to the fact that this
estimator is minimax adaptive over regularity classes of densities
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