This document discusses how the Unified Modeling Language (UML) and UML Profile for Data Modeling can be used to map object-oriented application models to relational data models. Key mappings include:
- Packages map to database schemas
- Classes map to tables
- Attributes map to columns
- Associations between classes map to relationships between tables, such as one-to-one, one-to-many, and many-to-many relationships.
- Generalization relationships between classes map to identifying relationships between tables.
Schema Integration, View Integration and Database Integration, ER Model & Dia...Mobarok Hossen
What is ER Model & Diagrams?
How can you design ER Model & Diagram?
What is Object-Oriented Model?
What is Schema Integration? how can you Schema Integrate?
What is View Integration? how can you View Integrate?
What is Database Integration? how can you Database Integrate?
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This RDBMS (Relational Database Management System) contains 9 Units and each unit contains 40 to 50 slides in it.
Contents…
• Define a Database Management System
• Describe the types of data models
• Create an entity-relationship model
• List the types of relationships between entities
• Define a Relational Database Management System
• Describe the operators that work on relations
• Identify tips of logical database design
• Map an ER diagram to a table
• Describe data redundancy
• Describe the first, second, and third normal forms
• Describe the Boyce-Codd Normal Form
• Appreciate the need for denormalization.
Schema Integration, View Integration and Database Integration, ER Model & Dia...Mobarok Hossen
What is ER Model & Diagrams?
How can you design ER Model & Diagram?
What is Object-Oriented Model?
What is Schema Integration? how can you Schema Integrate?
What is View Integration? how can you View Integrate?
What is Database Integration? how can you Database Integrate?
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This RDBMS (Relational Database Management System) contains 9 Units and each unit contains 40 to 50 slides in it.
Contents…
• Define a Database Management System
• Describe the types of data models
• Create an entity-relationship model
• List the types of relationships between entities
• Define a Relational Database Management System
• Describe the operators that work on relations
• Identify tips of logical database design
• Map an ER diagram to a table
• Describe data redundancy
• Describe the first, second, and third normal forms
• Describe the Boyce-Codd Normal Form
• Appreciate the need for denormalization.
LESSON CONTENT:
- Advanced Spreadsheet Skills
- Microsoft Excel: Concept and Features
- Parts and Functions
- Eloctronic Formula
Remider:
- All sets for educational purposes only.
Tableau software is a basic requirement for any business to gain insight into the development of the company. It allows any non-technical user to easily create or develop the customized dashboards that facilitate insight into a broad spectrum of information. It is a must know interactive business intelligence tool in the field of data visualization.
LESSON CONTENT:
- Advanced Spreadsheet Skills
- Microsoft Excel: Concept and Features
- Parts and Functions
- Eloctronic Formula
Remider:
- All sets for educational purposes only.
Tableau software is a basic requirement for any business to gain insight into the development of the company. It allows any non-technical user to easily create or develop the customized dashboards that facilitate insight into a broad spectrum of information. It is a must know interactive business intelligence tool in the field of data visualization.
Презентация об этапах психосексуального развития ребенка, об особенностях физиологических и психологических изменений. Поможет родителям, педагогам, психологам больше понимать состояние ребенка и способствовать его гармоничному развития
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This reading introduces you to data modeling and different types of data models. Data models help keep data consistent and enable people to map out how data is organized. A basic understanding makes it easier for analysts and other stakeholders to make sense of their data and use it in the right ways.
Important note: As a junior data analyst, you won't be asked to design a data model. But you might come across existing data models your organization already has in place.
3. Physical data modeling depicts how a database operates. A physical data model defines all entities and attributes used; for example, it includes table names, column names, and data types for the database.
3. Physical data modeling depicts how a database operates. A physical data model defines all entities and attributes used; for example, it includes table names, column names, and data types for the database.
Student POST Database processing models showcase the logical s.docxorlandov3
Student POST:
Database processing models showcase the logical structure of a database. The most commonly used model is the Relational database model that sorts the data in a table that consist of rows and columns. The column holds the attributes of the entity and rows hold the data of a particular instance of the entities. The major advantage of the Relational model is that it is in the table form and hence easier for users to understand, manage and work with the data. And, with the primary key and foreign key concepts, the data can be uniquely identified, stored in different entities and retrieved effectively with the relationships. The other advantage is that with the relational model, SQL language can be used to work with the data which is simple to understand and most widely used. The disadvantage of relational model could be the financial cost that is higher in comparison as the specific software needs to be in place and the regular maintenance needs to be performed that requires highly skilled manpower. And, the complexity of the database can be further increased when the volume of the data keep in increasing. Also, there is the limitation in the length of fields stored as different data types in relational model (Joseph & Paul, 2009).
The other processing model is the Object-oriented model that depicts database as the collection of objects. The advantage of this model is that it is compatible to work with complex data sets with the use of Object IDs and object-oriented programming. It’s disadvantage is that object databases are not commonly used and the complexity can hamper the performance of database. The other type of database model is the Entity-Relationship model which is mostly used for the conceptual design of database. It pictures the entities, several attributes that falls within the domain of that entity and the cardinality of relationship between them. It’s advantage is that the E-R diagram is easily understandable by the users at the first glance and thus can effectively work with the data in no time and can point out the discrepancies in the data. The other advantage is that it can be easily converted to other models if required by the business. The disadvantage of Entity-Relationship is that the industry standard notations for the diagram is not defined and thus can create confusion to the users. This model is only suitable for high-level database design (S.J.D.,2020).
2Nd Student POST :
Database models or commonly referred to as schemas help represent the structure of a database and its format which is run by a DBMS. Database model uses vary depending on user specifications.
Types of database models
1.
Network model
This network model uses a structure similar to that of a hierarchical model. The model permits multiple parents, which is a tree-like structure model. This model emphasizes two basic concepts; records and sets. Records hold file hierarchy and sets define the many-to-many relationship .
Before you take a gander at particular images, its vital to compre.pdfaquacare2008
Before you take a gander at particular images, it\'s vital to comprehend the different levels of
ERDs. There are a few approaches to model substance relationship outlines. The most abnormal
state sort is a calculated information show; the following most astounding is the coherent
information model, and the least level (and in this manner most point by point) sort is the
physical information model. Counsel the graph beneath to see which components are secured in
every information model.
ER graph images and importance
Calculated DATA MODEL
This ER model builds up an expansive perspective of what ought to be incorporated into the
model set. Applied information models:
Incorporate essential substances and the relationship between them.
Try not to determine properties.
Try not to determine essential keys.
Calculated ERDs can be utilized as the establishment for intelligent information models. They
may likewise be utilized to frame shared trait connections between ER models as a premise for
information model coordination.
Legitimate DATA MODEL
This model contains more detail than the theoretical ER model, without respect to how data will
be physically actualized in the database. Legitimate information models:
Incorporate all substances and connections between them.
Indicate properties for every element.
Indicate essential key for every substance.
Indicate outside keys, which recognize the relationship between various substances.
Include standardization, which is the way toward expelling excess in a table so that the table is
simpler to adjust. Standardization ordinarily happens by partitioning an element table into two or
more tables and characterizing connections between the tables.
PHYSICAL DATA MODEL
The physical information model speaks to the way toward adding data to the database. This
model demonstrates every table structure, including section name, segment information sort,
segment limitations, essential key, outside key, and connections between tables. Physical
information models:
Determine all tables and segments.
Incorporate remote keys to distinguish connections between tables.
May incorporate denormalization, contingent upon client necessities.
May be essentially not quite the same as the intelligent information model.
Will vary contingent upon which DBMS (database administration framework) is utilized.
Applied ERD Symbols
These images are for the most part utilized for reasonable information models, albeit a few
perspectives may overflow into legitimate information models. They can be found in the UML
Entity Relationship and Entity Relationship shape libary of Lucidchart. In the event that you
don\'t see the shape you require, utilize a picture record (Lucidchart bolsters .PNG, .JPG, or
.SVG import) or make your own particular with our current shapes and styling alternatives.
Elements
Elements are items or ideas that speak to critical information. They are normally things, e.g.
client, manager, area, or advancement.
Solid elements exi.
Informatica Data Modelling : Importance of Conceptual ModelsZaranTech LLC
50-55 hours Training + Assignments + Actual Project Based Case Studies
All attendees will receive,
Assignment after each module, Video recording of every session
Notes and study material for examples covered.
Access to the Training Blog & Repository of Materials
The Data Warehouse (DW) is considered as a collection of integrated, detailed, historical data, collected from different sources . DW is used to collect data designed to support management decision making. There are so many approaches in designing a data warehouse both in conceptual and logical design phases. The conceptual design approaches are dimensional fact model, multidimensional E/R model, starER model and object-oriented multidimensional model. And the logical design approaches are flat schema, star schema, fact constellation schema, galaxy schema and snowflake schema. In this paper we have focused on comparison of Dimensional Modelling AND E-R modelling in the Data Warehouse. Dimensional Modelling (DM) is most popular technique in data warehousing. In DM a model of tables and relations is used to optimize decision support query performance in relational databases. And conventional E-R models are used to remove redundancy in the data model, facilitate retrieval of individual records having certain critical identifiers, and optimize On-line Transaction Processing (OLTP) performance.
Purpose of the data base system, data abstraction, data model, data independence, data definition
language, data manipulation language, data base manager, data base administrator, data base users,
overall structure.
ER Models, entities, mapping constrains, keys, E-R diagram, reduction E-R diagrams to tables,
generatio, aggregation, design of an E-R data base scheme.
Oracle RDBMS, architecture, kernel, system global area (SGA), data base writer, log writer, process
monitor, archiver, database files, control files, redo log files, oracle utilities.
SQL: commands and data types, data definition language commands, data manipulation commands,
data query language commands, transaction language control commands, data control language
commands.
Joins, equi-joins, non-equi-joins, self joins, other joins, aggregate functions, math functions, string
functions, group by clause, data function and concepts of null values, sub-querries, views.
PL/SQL, basics of pl/sql, data types, control structures, database access with PL/SQL, data base
connections, transaction management, data base locking, cursor management.
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2. Table of Contents
SOFTWARE DEVELOPMENT FOR DATABASE APPLICATION ..............................................1
THE APPLICATION MODEL.............................................................................................................1
THE DATA MODEL .............................................................................................................................1
THE UML PROFILE FOR DATA MODELING............................................................................................1
MAPPING AND DEPENDENCIES BETWEEN APPLICATION AND DATA MODELS............1
COMPONENT TO DATABASE ..................................................................................................................1
PACKAGE TO SCHEMA ...........................................................................................................................2
CLASSES TO TABLES..............................................................................................................................2
ATTRIBUTES TO COLUMNS ....................................................................................................................3
ASSOCIATIONS TO RELATIONSHIPS........................................................................................................4
SUMMARY...........................................................................................................................................10
3. Mapping Object to Data Models with the UML
1
Software Development for Database Application
The development of a database application involves a close working relationship between the software
developers and the database development team. The most successful projects are marked by a shared
vision and clear communication of project details. Software developers deal with object oriented software
development and use the logical class model to represent the main view at the application, while the
Database team designs, models, builds and optimizes the database. The areas of interface and overlap
between these two distinct responsibilities often represent the most challenging aspect of database
application development. This white paper discusses how the UML and the UML Profile for Data
Modeling can help resolve this challenge.
The Application Model
The application model uses the class view of the application. Classes tagged as persistent describe the
logical data model.
The application model describes the application layer dealing with the database. It contains classes used as
the layer interface to the application and persistent classes used as the interface to the database.
The Data Model
The data model describes the physical implementation of the database. This is the model of the internal
database structure.
Persistent classes from the application model map to the data model.
The UML Profile for Data Modeling
The UML profile for Data Modeling contains modeling constructs necessary to accurately model a
database. These constructs are used to specify detailed information about the database and database
modeling. See the Rational White Paper, “The UML profile for Data Modeling” for more details.
Mapping and dependencies between Application and Data Models
The physical data model must map to the database. This is a simple one to one mapping, which is used to
forward or reverse engineer the database structures.
The mapping between the application model and the database model is more complex. As the data model
can change because of normalization or de-normalization, the application model can change as well.
Therefore, this mapping must be able to describe any and every possible relationship between the
application model and the data model.
Component to Database
The database itself is the physical layer of data storage. It has no mapping into the application model.
Instead, the application has interfaces to the database.
The database is associated to the application. The type of the association is a dependency.
4. Mapping Object to Data Models with the UML
2
The dependency between database and component is part of a software design and must be modeled
manually.
Package to Schema
The persistent view of the application is most commonly modeled in a persistent layer, represented by a
package.
This package maps to a Schema. The mapping is used for forward and reverse engineering. The mapping,
which can be used on a class diagram, is a dependency.
The dependency between package and schema is part of a software design and must be modeled manually.
Classes to Tables
Persistent classes can be mapped to tables. The default mapping is a 1:1 mapping although classes using
associations will be in some cases mapped to more than one table. See the “Association to Relationships”
section below for details on the mapping of classes associated to another classes.
When a class is mapped to a table all of the necessary transformations will be done. See the “Attributes to
Columns” section below.
The Item Class, below, is an example of a persistent table, which will be mapped to a database.
5. Mapping Object to Data Models with the UML
3
The corresponding table is the Item table.
The mapping assigns the table to the class although, as will be seen later, the mapping is additionally
provided on the detailed level of a column.
The mapping does not require the special design of a class or a table. The forward and reverse engineering
itself provides the ability to generate primary keys and primary key constraints.
Attributes to Columns
Attributes of persistent classes map to columns of a table. Mapping of attributes must consider the datatype
conversion between application datatype and database datatype. Although SQL-92 defines standard
datatypes for the database, most vendors implement additional datatypes or change the name of standard
datatypes.
The Item class also provides an example of the attribute to column conversion.
The data types used are Long for the id, String for the description and double for the price. In the case of an
Oracle 8.x database this mapping is done as follows.
6. Mapping Object to Data Models with the UML
4
The Long is mapped to NUMBER(10)1
, the String to VARCHAR2, and the Double to NUMBER(20).
See the Rational Rose Data Modeler Online Help for a complete list of mapping for every database.
Associations to Relationships
In a persistent data class model any of the provided association types can be used. To make it even more
complex, all of the possible cardinalities of the class roles in the association must be supported.
It is difficult to use relationships in a data model, because the relational data model understands only
identifying and non-identifying relationships and the1:N cardinality. The 1:1 cardinality must be forced
through constraints.
Following are some examples of the mapping between the persistent class model and the relational data
model.
1:1 association maps to a non-identifying relationship
In the object-oriented design often a 1:1 association represents the relationship between two independent
objects (in the example, Item and Picture). Each Item has to have a Picture, whereas the Picture has to be
assigned to the Item. The association is unidirectional.
The mapping to a data model uses two tables and a non-identifying relationship.
1
The length is specified in the detail specification of the column.
7. Mapping Object to Data Models with the UML
5
The foreign key of the table ItemPicture (item_id) uses the primary key of the Item table to build the
relationship. The foreign key constraint must be generated.
Because of the basic functionality of the relational data model the relationship is always bi-directional.
1:N association maps to a non-identifying relationship
The 1:N association is used as the association between the Item and the OrderedItem in this example. Every
instance of an ordered item has to have an association to exact one instance of the Item – only existing
Items can be ordered.
An instance of an Item may or may not be associated with every OrderedItem – not every Item must be
ordered.
A non-identifying relationship is used to specify the relationship between the tables.
A foreign key on the column item_id is generated to build the relationship. As with every foreign key a
foreign key constraint is also generated.
M:N association maps to 3 tables
The object-oriented design allows m to n (many-to-many) associations between classes. Many Employees
in the example care about one Customer. One Employee cares about many Customers.
8. Mapping Object to Data Models with the UML
6
As the relational data model does not allow m to n relationships, an additional table, called an associate
table, must be created. The associatetable splits the m to n relationship into two 1 to n relationships, using
identifying relationships.
The primary keys of both basic tables are used as primary and foreign keys in the relationship table. The
corresponding primary and foreign key constraints are generated.
Indexes can be built to improve the performance of database access.
Aggregation by reference maps to a non-identifying relationship
An optional aggregation by reference, as in the case of the BillingAddress, maps to a non-identifying
relationship. An Address can be the billing address of a CorporateCustomer.
9. Mapping Object to Data Models with the UML
7
The data model uses a non-identifying relationship to represent this type of relation.
The CorporateBillingAddress is used as a foreign key for the CorporateCustomer table and may be null.
The foreign key constraint must be generated.
Aggregation by value (composite aggregation) maps to an identifying relationship
A non-optional aggregation (by value) is used in this example to represent the Address as a part of the
Customer, resulting in two separate objects which act as one.
The corresponding representation in the data model is the identifying relationship.
10. Mapping Object to Data Models with the UML
8
The primary key of the Customer table migrates to the Address as foreign and primary key. A composite
primary constraint and a foreign key constraint are built for the address table based on the relationship.
Generalization maps to identifying relationship
Generalization specifies “a kind of” relation between two tables. CorporateCustomer is a kind of Customer.
The corresponding data model specifies two tables and an identifying relationship.
The primary key of the base table migrates to the child table as a primary and foreign key. The primary and
foreign key constraints are specified.
One class can map to several tables
In the process of normalization the data model is often split into more tables to reduce data redundancy.
The Address table, for example, is quite redundant because of the zip code definition.
The data analyst will in most cases split one table into two – the Address table and the Zip_codes table.
11. Mapping Object to Data Models with the UML
9
A non-identifying relationship is used to specify the relationship between the tables. The primary key of the
outsourced table is used as foreign keys in the Address table.
The foreign key constraints must be generated.
Multiple classes can map to one table
Because of performance and data accessibility the data model is often de-normalized. This results in the
mapping from multiple classes to one table.
In the example the PrivateCustomer just adds some attributes to the Customer. The attributes are never used
with other types of customers, but the data analyst could decide to make the columns within the
PrivateCustomer nullable.
As a result just one table of Customer is used in the data model. This table can be further refined for other
types of customers, but it already contains all of the columns of the PrivateCustomer. A data modeler may
add additional columns as well. For example, to map the PrivateCustomer, a column called cust_type could
be created.
In most cases, merging tables requires additional checks at the application logic to qualify data, or the
definition of additional views on a table for different accessibility.
12. Mapping Object to Data Models with the UML
10
In most cases, he data analyst makes decisions about merging tables based on optimizing the database for
data access.
Summary
Mapping object to data models is not easy. The object-relational mapping must be updated continuously as
the requirements, object and data model change.
There are several levels of mapping – from the database, schema, up to table and column. The examples in
this white paper are not complete. There are additional types of associations and additional mapping
examples.
Tracking of object-relational mapping is the key to success when building database applications.
13. IBM software integrated solutions
IBM Rational supports a wealth of other offerings from IBM software. IBM
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