This book is about tuning Oracle databases. Three areas of Oracle Database tuning are data model tuning, SQL code tuning, and physical and configuration tuning. The author began his career as an applications developer, not as a systems or network administrator. As a result, this book is written from an applications rather than an operating system perspective.
This book starts by setting a clear foundation for what Core Data is and how it works and then takes you step-by-step through how to extract the results you need from this powerful framework. You’ll learn what the components of Core Data are and how they interact, how to design your data model, how to filter your results, how to tune performance, how to migrate your data across data model versions, and many other topics around and between these that will separate your apps from the crowd.
Tuning of SQL code is generally cheaper than changing the data model. Physical and configuration tuning involves a search for bottlenecks that often points to SQL code or data model issues. Building an appropriate data model and writing properly performing SQL code can give 100%+ performance improvement. Physical and configuration tuning often gives at most a 25% performance increase.
Frequency distribution models tuned to words and other linguistic events can predict the number of distinct types and their frequency distribution in samples of arbitrary sizes. We conduct, for the ﬁrst time, a rigorous evaluation of these models based on cross-validation and separation of training and test data. Our experiments reveal that the prediction accuracy of the models is marred by serious overﬁtting problems, due to violations of the random sampling assumption in corpus data. We then propose a simple pre-processing method to alleviate such non-randomness problems. ...
This Training Kit is designed for information technology (IT) professionals who support
or plan to support data warehouses, extract-transform-load (ETL) processes, data quality
improvements, and master data management. It is designed for IT professionals who also
plan to take the Microsoft Certified Technology Specialist (MCTS) exam 70-463. The authors
assume that you have a solid, foundation-level understanding of Microsoft SQL Server 2012
and the Transact-SQL language, and that you understand basic relational modeling concepts.
The universal UDF SAS_JOB represents a complex multi-step process that calls into all the SAS In-Database
subsystems that can reside in the DBMS: formats (TKFORMAT subsystem), data transformation and model scoring
(TKFUNCTIONS and TSPL subsystems), and analytics (TKSCIENCE subsystem).
Both SAS clients and DBMS clients can use the integrated SAS servers. A SAS client communicates directly with the SAS
servers deployed on the DBMS head node. The SAS client can execute SAS jobs inside the DBMS by sending commands
to the SAS servers that are running on the DBMS head nodes.
After completing this lesson, you should be able to
do the following:
Create roles to ease setup and maintenance of the security model
Use the GRANT and REVOKE statements to grant and revoke object privileges
Create and access database links
Data-driven systems for natural language processing have the advantage that they can easily be ported to any language or domain for which appropriate training data can be found. However, many data-driven systems require careful tuning in order to achieve optimal performance, which may require specialized knowledge of the system. We present MaltOptimizer, a tool developed to facilitate optimization of parsers developed using MaltParser, a data-driven dependency parser generator.
We estimate the parameters of a phrasebased statistical machine translation system from monolingual corpora instead of a bilingual parallel corpus. We extend existing research on bilingual lexicon induction to estimate both lexical and phrasal translation probabilities for MT-scale phrasetables. We propose a novel algorithm to estimate reordering probabilities from monolingual data. We report translation results for an end-to-end translation system using these monolingual features alone.
A measurement of the underlying event (UE) activity
in proton–proton collisions at a center-of-mass energy
of 7 TeV is performed using Drell–Yan events in a data sample
corresponding to an integrated luminosity of 2.2 fb−1,
collected by the CMS experiment at the LHC. The activity
measured in the muonic final state (qq → μ
−) is corrected
to the particle level and compared with the predictions
of various Monte Carlo generators and hadronization
Identifying background (context) information in scientiﬁc articles can help scholars understand major contributions in their research area more easily. In this paper, we propose a general framework based on probabilistic inference to extract such context information from scientiﬁc papers. We model the sentences in an article and their lexical similarities as a Markov Random Field tuned to detect the patterns that context data create, and employ a Belief Propagation mechanism to detect likely context sentences. ...
We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks.
RightScale takes much of the risk out of choosing a cloud
computing system by offering a free edition of its myCloud
platform for developing and testing private cloud infrastructures.
The open-source model is almost as revolutionary as the
technology. The company makes its profit from services, once
the cloud is up and running. Its specialty is fine-tuning servers to
handle different types of data seamlessly while providing strategies
that create as little downtime as possible.
Solar power systems are not without risks too.