Data structure tuning

Xem 1-20 trên 25 kết quả Data structure tuning
  • To integrate molecular features from multiple high-throughput platforms in prediction, a regression model that penalizes features from all platforms equally is commonly used. However, data from different platforms are likely to differ in effect sizes, the proportion of predictive features, and correlations structures. Subtle but important features may be missed by shrinking all features equally.

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  • We propose a robust method of automatically constructing a bilingual word sense dictionary from readily available monolingual ontologies by using estimation-maximization, without any annotated training data or manual tuning. We demonstrate our method on the English FrameNet and Chinese HowNet structures. Owing to the robustness of EM iterations in improving translation likelihoods, our word sense translation accuracies are very high, at 82% on average, for the 11 most ambiguous words in the English FrameNet with 5 senses or more....

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  • We evaluate the accuracy of an unlexicalized statistical parser, trained on 4K treebanked sentences from balanced data and tested on the PARC DepBank. We demonstrate that a parser which is competitive in accuracy (without sacrificing processing speed) can be quickly tuned without reliance on large in-domain manuallyconstructed treebanks. This makes it more practical to use statistical parsers in applications that need access to aspects of predicate-argument structure.

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  • Recently, there is a growing interest in working with tree-structured data in different applications and domains such as computational biology and natural language processing. Moreover, many applications in computational linguistics require the computation of similarities over pair of syntactic or semantic trees. In this context, Tree Edit Distance (TED) has been widely used for many years. However, one of the main constraints of this method is to tune the cost of edit operations, which makes it difficult or sometimes very challenging in dealing with complex problems. ...

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  • 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.

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  • After completing this lesson, you should be able to do the following: Interpret the concept of a hierarchical query Create a tree-structured report Format hierarchical data Exclude branches from the tree structure

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  • After completing this lesson, you should be able to do the following: Compare and evaluate the different storage structures Examine different data access methods Implement different partitioning methods

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  • Classification of Indexes An index is a tree structure that allows direct access to a row in a table. Indexes can be classified based on their logical design or on their physical implementation. The logical classification groups indexes from an application perspective, while the physical classification is derived from the way the indexes are stored. Single column and concatenated indexes A single column index has only one column in the index key—for example, an index on the employees number column of an employees table.

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  • Tablespaces and Data Files Databases, tablespaces, and data files are closely related, but they have important differences: An Oracle database consists of one or more logical storage units called tablespaces, which collectively store all of the database’s data. Each tablespace in an Oracle database consists of one or more files called data files, which are physical structures that conform with the operating system in which Oracle is running. A database’s data is collectively stored in the data files that constitute each tablespace of the database.

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  • After completing this lesson, you should be able to do the following: Monitor and size the redo log buffer Monitor and size the Java pool Control the amount of Java session memory used by a session

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  • Built-In Database Objects In addition to creating the database files, several other structures are created. Data dictionary: Contains descriptions of the objects in the database Dynamic performance tables: Contains information used by the database administrator (DBA) to monitor and tune the database and instance PL/SQL packages: Program units adding functionality to the database. These packages are created when the catproc.sql script is run after the CREATE DATABASE command. PL/SQL packages will not be discussed within the scope of this course.

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  • Overview of Primary Components The Oracle architecture includes a number of primary components, which are discussed further in this lesson. Oracle server: There are several files, processes, and memory structures in an Oracle server; however, not all of them are used when processing a SQL statement. Some are used to improve the performance of the database, to ensure that the database can be recovered in the event of a software or hardware error, or to perform other tasks necessary to maintain the database. The Oracle server consists of an Oracle instance and an Oracle database.

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  • Course Objectives This course is the first in a series of courses that cover the core database administrator tasks. The tasks covered in this course are: Outlining the Oracle architecture Planning and creating databases Managing the memory, process, physical, and logical structures Managing database users by controlling and monitoring their actions Using the Globalization Support features

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  • Overview The recovery catalog is a schema that is created in a separate database. It contains the RMAN metadata obtained from the target database control file. RMAN propagates information about the database structure, archived redo logs, backup sets, and datafile copies into the recovery catalog from the control file of the target database. You should use a catalog when you have multiple target databases to manage. RMAN stores, uses, and maintains the information in the recovery catalog. The recovery catalog is maintained by RMAN when you do the following: 1.

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  • Workshop Methodology Group-Oriented and Interactive Structure The workshop is structured to allow individuals to work in groups to perform database backup, restore, and recovery operations. Each group is encouraged to share its approach to resolving database failures with other groups in the class. Intensive Hands-On Diagnosis and Problem Resolution The intent is to provide you with as much hands-on experience as possible to diagnose and work through backup and recovery scenarios.

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  • Overview The Oracle server uses many memory components, background processes, and file structures for its backup and recovery mechanism. This lesson reviews the concepts presented in the Oracle9i Database Administration Fundamentals I course, with an emphasis on backup and recovery requirements. Oracle Instance An Oracle instance consists of memory areas (mainly System Global Area [SGA]) and background processes, namely PMON, SMON, DBWn, LGWR, and CKPT. An instance is created during the nomount stage of the database startup after the parameter file has been read.

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  • Client-Server Application Connection Oracle Net enables a network connection between a client and an Oracle database server. Oracle Net is a software component that resides on both the client and on the Oracle database server. It is layered on top of the network protocol. When a connection is initiated from a client to the Oracle database server, data is passed down a stack on the client, over the network, and up a similar stack to the Oracle database server.

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  • Indexes are important. Not only that, they are vastly important. No single structure aids in retrieving data from a database more than an index. Indexes represent both how data is stored and the access paths by which data can be retrieved from your database. Without indexes, a database is an unordered mess minus the roadmap to find the information you seek. Throughout my experience with customers, one of the most common resolutions that I provide for performance tuning and application outages is to add indexes to their databases.

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  • Data design is an essential part of the application development cycle. By analogy, building an application is like building a house. Having the right tools is important, but we need a solid foundation: the data structure. However, producing a good data structure can be a daunting challenge; the quest for a perfect data structure can lead us to new territories where many methods are available. Which one is the best? How can we keep our focus on the goal to achieve, without losing our time?

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  • The TrueNet® Category 6 system is a fully integrated family of precisely tuned components each designed to operate at optimum performance with the other. TrueNet Category 5e systems are unmatched in data throughput and are supported by the most comprehensive and thorough warranty in the industry: the TrueNet Zero Bit-Error warranty. The warranty guarantees that the structured cabling system will remain error free for a full 5 years and includes a 20-year electrical performance and free of defect warranty....

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