Conceptual Logical And Physical Database Design Pdf

conceptual logical and physical database design pdf

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A conceptual schema is a high-level description of informational needs underlying the design of a database.

The design of a database is generally divided into three phases: Conceptual design.

Types of Data Models: Conceptual, Logical & Physical

This chapter explains how to create a logical design for a data warehousing environment and includes the following topics:. Your organization has decided to build a data warehouse. You have defined the business requirements and agreed upon the scope of your application, and created a conceptual design. Now you need to translate your requirements into a system deliverable.

To do so, you create the logical and physical design for the data warehouse. You then define:. The logical design is more conceptual and abstract than the physical design. In the logical design, you look at the logical relationships among the objects. Orient your design toward the needs of the end users.

End users typically want to perform analysis and look at aggregated data, rather than at individual transactions. However, end users might not know what they need until they see it. In addition, a well-planned design allows for growth and changes as the needs of users change and evolve.

By beginning with the logical design, you focus on the information requirements and save the implementation details for later. A logical design is conceptual and abstract.

You do not deal with the physical implementation details yet. You deal only with defining the types of information that you need. One technique you can use to model your organization's logical information requirements is entity-relationship modeling.

Entity-relationship modeling involves identifying the things of importance entities , the properties of these things attributes , and how they are related to one another relationships. The process of logical design involves arranging data into a series of logical relationships called entities and attributes. An entity represents a chunk of information.

In relational databases, an entity often maps to a table. An attribute is a component of an entity that helps define the uniqueness of the entity.

In relational databases, an attribute maps to a column. To ensure that your data is consistent, you must use unique identifiers. A unique identifier is something you add to tables so that you can differentiate between the same item when it appears in different places. In a physical design, this is usually a primary key. While entity-relationship diagramming has traditionally been associated with highly normalized models such as OLTP applications, the technique is still useful for data warehouse design in the fo rm of dimensional modeling.

In dimensional modeling, instead of seeking to discover atomic units of information such as entities and attributes and all of the relationships between them, you identify which information belongs to a central fact table and which information belongs to its associated dimension tables. You identify business subjects or fields of data, define relationships between business subjects, and name the attributes for each subject. Your logical design should result in 1 a set of entities and attributes corresponding to fact tables and dimension tables and 2 a model of operational data from your source into subject-oriented information in your target data warehouse schema.

You can create the logical design using a pen and paper, or you can use a design tool such as Oracle Warehouse Builder specifically designed to support modeling the ETL process. A schema is a collection of database objects, including tables, views, indexes, and synonyms. You can arrange schema objects in the schema models designed for data warehousing in a variety of ways.

Most data warehouses use a dimensional model. The model of your source data and the requirements of your users help you design the data warehouse schema. You can sometimes get the source model from your company's enterprise data model and reverse-engineer the logical data model for the data warehouse from this. The physical implementation of the logical data warehouse model may require some changes to adapt it to your system parameters—size of computer, number of users, storage capacity, type of network, and software.

The star schema is the simplest data warehouse schema. It is called a star schema because the diagram resembles a star, with points radiating from a center. The center of the star consists of one or more fact tables and the points of the star are the dimension tables, as shown in Figure The most natural way to model a data warehouse is as a star schema, where only one join establishes the relationship between the fact table and any one of the dimension tables.

A star schema optimizes performance by keeping queries simple and providing fast response time. All the information about each level is stored in one row. Some schemas in data warehousing environments use third normal form rather than star schemas.

Another schema that is sometimes useful is the snowflake schema, which is a star schema with normalized dimensions in a tree structure. Another alternative is provided by OLAP, which supports dimensional data types such as cubes and dimensions within Oracle Database. Fact tables and dimension tables are the two types of objects commonly used in dimensional data warehouse schemas.

Fact tables are the large tables in your data warehouse schema that store business measurements. Fact tables typically contain facts and foreign keys to the dimension tables. Fact tables represent data, usually numeric and additive, that can be analyzed and examined.

Examples include sales , cost , and profit. Dimension tables, also k nown as lookup or reference tables, contain the relatively static data in the data warehouse. Dimension tables store the information you normally use to contain queries. Dimension tables are usually textual and descriptive and you can use them as the row headers of the result set.

Examples are customers or products. A fact table typically has two types of columns: those that contain numeric facts often called measurements , and those that are foreign keys to dimension tables. A fact table contains either detail-level facts or facts that have been aggregated. Fact tables that contain aggregated facts are often called summary tables. A fact table usually contains facts with the same level of aggregation. Though most facts are additive, they can also be semi-additive or non-additive.

Additive facts can be aggregated by simple arithmetical addition. A common example of this is sales. Non-additive facts cannot be added at all. An example of this is averages. Semi-additive facts can be aggregated along some of the dimensions and not along others. An example of this is inventory levels, where you cannot tell what a level means simply by looking at it.

You must define a fact table for each star schema. From a modeling standpoint, the primary key of the fact table is usually a composite key that is made up of all of its foreign keys. A dimension is a structure, often composed of one or more hierarchies, that categorizes data.

Dimensional attributes help to describe the dimensional value. They are normally descriptive, textual values. Several distinct dimensions, combined with facts, enable you to answer business questions.

Commonly used dimensions are customers, products, and time. Dimension data is typically collected at the lowest level of detail and then aggregated into higher level totals that are more useful for analysis.

These natural rollups or aggregations within a dimension table are called hierarchies. Hierarchies are logical structures that use ordered levels to organize data. A hierarchy can be used to define data aggregation. For example, in a time dimension, a hierarchy might aggregate data from the month level to the quarter level to the year level. A hierarchy can also be used to define a navigational drill path and to establish a family structure.

Within a hierarchy, each level is logically connected to the levels above and below it. Data values at lower levels aggregate into the data values at higher levels. A dimension can be composed of more than one hierarchy. For example, in the product dimension, there might be two hierarchies—one for product categories and one for product suppliers.

Dimension hierarchies also group levels from general to granular. Q uery tools use hierarchies to enable you to drill down into your data to view different levels of granularity. This is one of the key benefits of a data warehouse. When designing hierarchies, you must consider the relationships in business structures. For example, a divisional multilevel sales organization. Hierarchies impose a family structure on dimension values. For a particular level value, a value at the next higher level is its parent, and values at the next lower level are its children.

These familial relationships enable analysts to access data quickly. A level represents a position in a hierarchy. For example, a time dimension might have a hierarchy that represents data at the month, quarter, and year levels. Levels range from general to specific, with the root level as the highest or most general level. The levels in a dimension are organized into one or more hierarchies. Level relationships specify top-to-bottom ordering of levels from most general the root to most specific information.

They define the parent-child relationship between the levels in a hierarchy. Hierarchies are also essential components in enabling more complex rewrites. For example, the database can aggregate an existing sales revenue on a quarterly base to a yearly aggregation when the dimensional dependencies between quarter and year are known.

Conceptual, Logical and Physical Data Model

Conceptual, logical and physical model or ERD are three different ways of modeling data in a domain. While they all contain entities and relationships, they differ in the purposes they are created for and audiences they are meant to target. A general understanding to the three models is that, business analyst uses conceptual and logical model for modeling the data required and produced by system from a business angle, while database designer refines the early design to produce the physical model for presenting physical database structure ready for database construction. With Visual Paradigm , you can draw the three types of model, plus to progress through models through the use of Model Transitor. Conceptual ERD models information gathered from business requirements.

There are three different types of data models: conceptual, logical and physical, and each has a specific purpose. But with the different types of data models, an organization benefits from using all three, depending on the information it wishes to convey and the use cases it wants to satisfy. The conceptual data model should be used to organize and define concepts and rules. Typically, business stakeholders and data architects will create such a model to convey what a system contains. In contrast, the logical data models and physical data models are concerned with how such systems should be implemented. Like the conceptual data model, the logical data model is also used by data architects, but also will be used by business analysts, with the purpose of developing a database management system DBMS -agnostic technical map of rules and structures. The physical data model is used to demonstrate the implementation of a system s using a specific DBMS and is typically used by database analysts DBAs and developers.

This chapter explains how to create a logical design for a data warehousing environment and includes the following topics:. Your organization has decided to build a data warehouse. You have defined the business requirements and agreed upon the scope of your application, and created a conceptual design. Now you need to translate your requirements into a system deliverable. To do so, you create the logical and physical design for the data warehouse. You then define:.

Conceptual, Logical, and Physical design of Persistent Data using UML. The database needs a structure definition to be able to store data and to recognize the.

Data model

A data model or datamodel [1] [2] [3] [4] [5] is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner. The term data model can refer to two distinct but closely related concepts. Sometimes it refers to an abstract formalization of the objects and relationships found in a particular application domain: for example the customers, products, and orders found in a manufacturing organization.

This article follows on from Database Design Phase 1: Analysis. The design phase is where the requirements identified in the previous phase are used as the basis to develop the new system. Another way of putting it is that the business understanding of the data structures is converted to a technical understanding.

Data modeling data modelling is the process of creating a data model for the data to be stored in a database. This data model is a conceptual representation of Data objects, the associations between different data objects, and the rules. Data modeling helps in the visual representation of data and enforces business rules, regulatory compliances, and government policies on the data. Data Models ensure consistency in naming conventions, default values, semantics, security while ensuring quality of the data. The Data Model is defined as an abstract model that organizes data description, data semantics, and consistency constraints of data.

Data Modelling: Conceptual, Logical, Physical Data Model Types

Logical Model

Хейл в ужасе тотчас понял свою ошибку. Стратмор находится на верхней площадке, у меня за спиной. Отчаянным движением он развернул Сьюзан так, чтобы она оказалась выше его, и начал спускаться. Достигнув нижней ступеньки, он вгляделся в лестничную площадку наверху и крикнул: - Назад, коммандер. Назад, или я сломаю… Рукоятка револьвера, разрезая воздух, с силой опустилась ему на затылок. Сьюзан высвободилась из рук обмякшего Хейла, не понимая, что произошло.

Если потребуется, заплатите за это кольцо хоть десять тысяч долларов. Я верну вам деньги, - сказал ему Стратмор. В этом нет необходимости, - ответил на это Беккер. Он так или иначе собирался вернуть деньги. Он поехал в Испанию не ради денег.

Сьюзан буквально онемела, когда эта страшная правда дошла до ее сознания. Северная Дакота - это Грег Хейл. Глаза ее не отрывались от экрана. Мозг лихорадочно искал какое-то другое объяснение, но не находил. Перед ее глазами было внезапно появившееся доказательство: Танкадо использовал меняющуюся последовательность для создания функции меняющегося открытого текста, а Хейл вступил с ним в сговор с целью свалить Агентство национальной безопасности. - Это н-не… - заикаясь, произнесла она вслух, - невероятно.

Conceptual schema

Сьюзан, не поднимая глаз, поджала ноги и продолжала следить за монитором. Хейл хмыкнул. Сьюзан уже привыкла к агрессивному поведению Хейла.

Он был известен среди сотрудников, он пользовался репутацией патриота и идеалиста… честного человека в мире, сотканном из лжи. За годы, прошедшие после появления в АНБ Сьюзан, Стратмор поднялся с поста начальника Отдела развития криптографии до второй по важности позиции во всем агентстве. Теперь только один человек в АНБ был по должности выше коммандера Стратмора - директор Лиланд Фонтейн, мифический правитель Дворца головоломок, которого никто никогда не видел, лишь изредка слышал, но перед которым все дрожали от страха.

Что он не мог разобрать, но все-таки кое-как прочитал первые буквы, В них не было никакого смысла. И это вопрос национальной безопасности. Беккер вошел в телефонную будку и начал набирать номер Стратмора.

 Ein Ring! - повторил Беккер, но дверь закрылась перед его носом. Он долго стоял в роскошно убранном коридоре, глядя на копию Сальватора Дали на стене.




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Elpidio A.


A data model helps design the database at the conceptual, physical and logical levels. Data Model structure helps to define the relational.

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Conceptual, logical and physical model are three different ways of modeling Physical ERD represents the actual design blueprint of a relational database.