Data dictionaries

Fostel J, Choi D, Zwickl C, Morrison N, Rashid A, Hasan A, et al. Chemical Effects in Biological Systems—Data Dictionary (CEBS-DD) a compendium of terms for the capture and integration of biological study design description, conventional phenotypes and omics data. Toxicol Sci 2005 88 585-601. 

R92070 User s Guide and Data Dictionary for Kenai Lakes Investigation Project 570990002 Your Drinking Water from Sonrce to Tap EPA Regulations and Guidance... 

Database schemas are centrally stored and controlled. Data definitions (schema) are stored in the centralized data dictionary. The user s view(s) of the database is defined and stored in the same data dictionary. Programs are given access to individual data fields, records, sets and areas of the database on a need-to-know basis. The database administrator creates and maintains integrity of the database schemas. The benefits of this approach are ... 

D. Database integrity is maintained in a multi-user environment through the centralized data dictionary. 

Data structures and content must be dehned. Actual data to be loaded into tables, hies, and databases mnst be specihed by reference to their sources. Data dictionaries should be used to describe different data types. Specihc data requirements include ... 

The master "data dictionary" table, which describes all the objects in the database, as well as some parameters that are specific to the database (exact match criteria, version of the database, etc.). This is sometimes referred to as "metadata" or "data about data."... 

Details on system screen designs, report layouts, data dictionary with data flow diagram, system configurations, system security, file design, system limitations and memory requirements are laid out by system developers and are usually formally inspected with members of the development team. Major outputs of this phase are the internal design documents and prototypes. The design documents are based on the ERS and can be used as a source for the technical support documentation. 

Microsoft, Oracle, and Linux are software platforms that provide scalable environments. Microsoft Access is a widely used data entry platform where tables with pull-down entries can be created. The data storage element at the back-end requires Microsoft SQL or a comparable system to provide reliable and secure back-up and data storage. The systems should allow for dynamic error detection notification at data entry, construction of computer forms that mimic hardcopy forms, and data lookup capabilities for subject information. A web-based data entry format allows for integration of geographically separate sites. An inventory system for the repository should be incorporated into the electronic database. A data dictionary should be part of the protocol (7). 

During the analysis phase of the SDLC, data are represented in the DFDs in the form of data flows and data stores. The data flows and data stores are depicted as DFD components in a processing context that will enable the system to do what is required. To produce a system, however, it is necessary to focus on the data, independent of the processes. Conceptual data modeling is used to examine the data at rest in the DFD to provide more precise knowledge about the data, such as definitions, structure, and relationships within the data. Withoirt this knowledge, httle is actually known about how the system will have to manipulate the data in the processes. Two conceptual data modeling tools are commonly used entity relationship diagrams (ERDs), and data dictionaries (DDs). Each of these tools provides critical information to the development effort. ERDs and DDs also provide important documentation of the system. 

The ERD is an excellent tool to help understand and explore the conceptual data model. It is less useful, however, for documentation purposes. To address the documentation requirements, a data dictionary (DD) is used. The DD is a repository of aU data definitions for all the data in the system. A DD entry is required for each data flow and data store on every DFD and for each entity in the ERDs. Developing a DD entry typically begins with naming the data structure being described, either by flow name, file name, or entity type. The list of attributes follow, with a description of the attribute, the attribute type, and the maximum size of the attribute, in characters. The attribute type is specified (e.g., integer, text, date, or binary). In the case of a data file, the average size of the file is indicated. 

There are many compelling reasons to use CASE tools. CASE tools can greatly shorten development times without sacrificing system quality. The tools usually enforce consistency across different phases by providing automated consistency checking between DFDs, ERDs, and the data dictionary to ensure that the attributes and data structures have been named and defined consistently across all of them. This, in turn, can increase the productivity of the SDLC team because a data store defined in a DFD can be automatically inserted as an entry in the data dictionary. This, in turn, can increase the consistency of the development effort across projects. 

Useful documentation about the database structure and architecture is provided through the various schemas, which represent explicit data declarations. These declarations represent data about data. The central repository in which these declarations are kept is called a data dictionary or data directory, and is often represented by the symbol DD. The data directory is a central repository for the definitions of the schemas and the mappings between schemas. Generally, a data dictionary can be queried in the same manner as can the database, thereby enhancing the abdity of the DBMS user to pose questions about the avadability and structure of data. It is often possible to query a data directory with a high-level, or fourth-generation, query language. 

For very large system designs, it is necessary that the data dictionary development process be automated. A typical data dictionary for a large system may include several thousand entries. It is physically impossible to manually maintain a dictionary of this size or to retain consistent and unambiguous terms for each data element or composite of data elements. Therefore, automated tools are needed for efficient and effective development and maintenance of a data dictionary. These are provided in contemporary DBMSs. 

A distributed database management system will generally look much like replicated versions of a more conventional single-location database management system. We can thus imagine replicated versions of Figure 7. For simplicity, we show only the physical database, the database access interface mechanism, and the data dictionary for each database. Figure 10 indicates one possible conceptual... 

Many support facilities will typically be provided with a DBMS to enable achievement of these purposes. These include data dictionaries to aid in internal housekeeping and information query, retrieval, and report generation fadlities to support external use needs. 

Upper-Extremity Checklist, 1143-1144 major activities of, 1770 Data/control flow diagrams (DFD/CFDs), 173 Data definition languages (DDLs), 119 Data dictionaries (DDs), 102-103, 119 Data Encryption Standard (DES), 733 Data flow diagrams (DEDs), 99-101 Data gloves, 1125... 

Information systems (IS) (Continued) data dictionaries, I02-I03 data flow diagrams, 99-101 entity relationship diagrams, 102, 103 feasibility analysis, 98-99 Gantt charts, 103-104 joint application deployment, 105 PERT diagrams, 104 rapid application deployment, 104, 105 Structured English, use of, 100-102 transorganizational, 69-70 as element of electronic commerce, 69-70 and types of knowledge, 67 value of, 67... 

The monitoring system, the database server is first transferred to the system hardware configuration table and data dictionary, loading hardware... 

Logic diagrams Data dictionary Data flow diagrams... 

The behavioral model is based on the environmental model and consists of hierarchical data flow diagrams, supplemented by functional descriptions (equations), assrrmptions and optionally a list (data-dictionary) of all the variables and constants. 

Every function is described by one equation. In order to avoid redundancy, functional descriptions are only necessary at the lowest level where the function is worked out completely. If necessary, one could use a data dictionary, which shows all the variables and constants with a description, values and units, which is common for information systems. 

The definition of a data SGroup (set of atoms and bonds) can be either tied directly to a chemical SGroup, or to an arbitrary set of atoms and bonds (except that once a bond is included, the two atoms of which it is composed are automatically included). Each data SGroup has associated with it a field and a value of data. The data fields to be allowed are defined by a database administrator as a part of the data dictionary. In the MACCS-II implementation, the program allows the same data field control as for data attached to the structure (text, numeric, and formatted). 

An HView defines a logical view of the contents of a set of (possibly) disparate databases. This logical view provides a data dictionary that is used by both the workstation and DSHost software to access the hierarchy. Thus, the simple HView definition summarised at the bottom of Figure 9 is all that is required to give access to all fields in each of the defined segments. 

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