Xem 1-11 trên 11 kết quả Hierarchical retrieval
  • 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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  • [ Team LiB ] Recipe 3.14 Using the Shape Language to Retrieve Hierarchical Data Problem You want to use the Shape language with ADO.NET to retrieve hierarchical data from a SQL Server.

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  • Data Model: A set of concepts to describe the structure of a database, and certain constraints that the database should obey. Data Model Operations: Operations for specifying database retrievals and updates by referring to the concepts of the data model. Operations on the data model may include basic operations and user-defined operations.

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  • [ Team LiB ] Recipe 2.1 Retrieving Hierarchical Data into a DataSet Problem You want to fill a DataSet with parent and related child data, even if the DataSet already has a schema that includes the relationship. Solution There are several techniques you can use to load parent and child data into a DataSet.

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Hierarchical Fuzzy Feature Similarity Combination for Presentation Slide Retrieval

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  • Appendix E - Hierarchical model. This chapter presents the following content: Basic concepts, tree-structure diagrams, data-retrieval facility, update facility, virtual records, mapping of hierarchies to files, the IMS database system.

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  • The book presents several advances in different research areas related to data storage, from the design of a hierarchical memory subsystem in embedded signal processing systems for data-intensive applications, through data representation in flash memories, data recording and retrieval in conventional optical data storage systems and the more recent holographic systems, to applications in medicine requiring massive image databases.

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  • The relational database uses the concept of linked two-dimensional tables consisting of rows and columns, as shown in Figure 1-2. Unlike the hierarchical approach, no predetermined relationship exists between distinct tables. This means that the data needed to link together the different areas of the network or hierarchical model need not be defined. Because relational users don’t need to understand the representation of data in storage to retrieve it (many such users created ad hoc queries against the data), ease of use helped popularize the relational model....

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  • This Handbook has been designed to assist students as well as professionals in the behavioral and biological sciences to understand and apply the principles and methods of experimental research. The Handbook is comprehensive but is written in a concise, outline format. This allows a considerable amount of material to be presented in an explicit, compact form that facilitates the perception and understanding of the hierarchical and parallel relationships among concepts. As a result, learning, retrieving, and applying the information should be greatly enhanced....

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  • Probabilistic topic models have recently gained much popularity in informational retrieval and related areas. Via such models, one can project high-dimensional objects such as text documents into a low dimensional space where their latent semantics are captured and modeled; can integrate multiple sources of information—to ”share statistical strength” among components of a hierarchical probabilistic model; and can structurally display and classify the otherwise unstructured object collections. ...

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  • The main aim of the MIMA (Mining Information for Management and Acquisition) Search System is to achieve ‘structuring knowledge’ to accelerate knowledge exploitation in the domains of science and technology. This system integrates natural language processing including ontology development, information retrieval, visualization, and database technology. The ‘structuring knowledge’ that we define indicates 1) knowledge storage, 2) (hierarchical) classification of knowledge, 3) analysis of knowledge, 4) visualization of knowledge.

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