A database can be a set of flat files stored on computer tape or disk or it could consist of database tables that are managed by a Database Management System (DBMS). There are different types of DBMS products: relational, network and hierarchical, multidimensional, object. The most widely commonly used type of DBMS today is the Relational Database Management Systems (RDBMS). Database management systems (DBMS) are designed to use one of five database structures to provide simplistic access to information stored in databases.
The five database structures are: the hierarchical model, he network model, the relational model, the multidimensional model, and the object model. Inverted lists and other methods are also used. A given database management system may provide one or more of the five models. The optimal structure depends on the natural organization of the application’s data, and on the application’s requirements, which include transaction rate (speed), reliability, maintainability, scalability, and cost. Hierarchical Model The hierarchical data model organizes data in a tree structure.
There is a hierarchy of parent and child data segments.
This structure implies that a record can have repeating information, generally in the child data segments. Data in a series of records, which have a set of field values attached to it. It collects all the instances of a specific record together as a record type. These record types are the equivalent of tables in the relational model, and with the individual records being the equivalent of rows. To create links between these record types, the hierarchical model uses Parent Child Relationships. These are a 1:N mapping between record types. This is done by using trees, like set theory used in the relational model, “borrowed” from maths.
For example, an organization might store information about an employee, such as name, employee number, department, salary. The organization might also store information about an employee’s children, such as name and date of birth. The employee and children data forms a hierarchy, where the employee data represents the parent segment and the children data represents the child segment. If an employee has three children, then there would be three child segments associated with one employee segment. In a hierarchical database the parent-child relationship is one to many.
This restricts a child segment to having only one parent segment. Hierarchical DBMSs were popular from the late 1960s, with the introduction of IBM’s Information Management System (IMS) DBMS, through the 1970s. The hierarchical structure was used in early mainframe DBMS. Records’ relationships form a treelike model. This structure is simple but nonflexible because the relationship is confined to a one-to-many relationship. IBM’s IMS system and the RDM Mobile are examples of a hierarchical database system with multiple hierarchies over the same data. RDM Mobile is a newly designed embedded database for a mobile computer system.
The hierarchical structure is used primarily today for storing geographic information and file systems. Network Model The popularity of the network data model coincided with the popularity of the hierarchical data model. Some data were more naturally modeled with more than one parent per child. So, the network model permitted the modeling of many-to-many relationships in data. In 1971, the Conference on Data Systems Languages (CODASYL) formally defined the network model. The basic data modeling construct in the network model is the set construct. A set consists of an owner record type, a set name, and a member record type.
A member record type can have that role in more than one set, hence the multiparent concept is supported. An owner record type can also be a member or owner in another set. The data model is a simple network, and link and intersection record types (called junction records by IDMS) may exist, as well as sets between them . Thus, the complete network of relationships is represented by several pairwise sets; in each set some (one) record type is owner (at the tail of the network arrow) and one or more record types are members (at the head of the relationship arrow).
Usually, a set defines a 1:M relationship, although 1:1 is permitted. The CODASYL network model is based on mathematical set theory. The network structure consists of more complex relationships. Unlike the hierarchical structure, it can relate to many records and accesses them by following one of several paths. In other words, this structure allows for many-to-many relationships. Relational Model (RDBMS – relational database management system) A database based on the relational model developed by E. F. Codd. A relational database allows the definition of data structures, storage and retrieval operations and integrity constraints.
In such a database the data and relations between them are organised in tables. A table is a collection of records and each record in a table contains the same fields. Properties of Relational Tables: Values Are Atomic Each Row is Unique Column Values Are of the Same Kind The Sequence of Columns is Insignificant The Sequence of Rows is Insignificant Each Column Has a Unique Name Certain fields may be designated as keys, which means that searches for specific values of that field will use indexing to speed them up.
Where fields in two different tables take values from the same set, a join operation can be performed to select related records in the two tables by matching values in those fields. Often, but not always, the fields will have the same name in both tables. For example, an “orders” table might contain (customer-ID, product-code) pairs and a “products” table might contain (product-code, price) pairs so to calculate a given customer’s bill you would sum the prices of all products ordered by that customer by joining on the product-code fields of the two tables.
This can be extended to joining multiple tables on multiple fields. Because these relationships are only specified at retreival time, relational databases are classed as dynamic database management system. The RELATIONAL database model is based on the Relational Algebra. The relational structure is the most commonly used today. It is used by mainframe, midrange and microcomputer systems. It uses two-dimensional rows and columns to store data. The tables of records can be connected by common key values. While working for IBM, E. F. Codd designed this structure in 1970.
The model is not easy for the end user to run queries with because it may require a complex combination of many tables. Multidimensional structure The multidimensional structure is similar to the relational model. The dimensions of the cube-like model have data relating to elements in each cell. This structure gives a spreadsheet-like view of data. This structure is easy to maintain because records are stored as fundamental attributes – in the same way they are viewed – and the structure is easy to understand. Its high performance has made it the most popular database structure when it comes to enabling online analytical processing (OLAP).
Object/Relational Model Object/relational database management systems (ORDBMSs) add new object storage capabilities to the relational systems at the core of modern information systems. These new facilities integrate management of traditional fielded data, complex objects such as time-series and geospatial data and diverse binary media such as audio, video, images, and applets. By encapsulating methods with data structures, an ORDBMS server can execute comple x analytical and data manipulation operations to search and transform multimedia and other complex objects.
As an evolutionary technology, the object/relational (OR) approach has inherited the robust transaction- and performance-management features of it s relational ancestor and the flexibility of its object-oriented cousin. Database designers can work with familiar tabular structures and data definition languages (DDLs) while assimilating new object-management possibi lities. Query and procedural languages and call interfaces in ORDBMSs are familiar: SQL3, vendor procedural languages, and ODBC, JDBC, and proprie tary call interfaces are all extensions of RDBMS languages and interfaces.
And the leading vendors are, of course, quite well known: IBM, Inform ix, and Oracle. The object oriented structure has the ability to handle graphics, pictures, voice and text, types of data, without difficultly unlike the other database structures. This structure is popular for multimedia Web-based applications. It was designed to work with object-oriented programming languages such as Java. Object-Oriented Model Object DBMSs add database functionality to object programming languages. They bring much more than persistent storage of programming language objects.
Object DBMSs extend the semantics of the C++, Smalltalk and Java object programming languages to provide full-featured database programming capability, while retaining native language compatibility. A major benefit of this approach is the unification of the application and database development into a seamless data model and language environment. As a result, applications require less code, use more natural data modeling, and code bases are easier to maintain. Object developers can write complete database applications with a modest amount of additional effort.
The object-oriented database (OODB) paradigm is the combination of object-oriented programming language (OOPL) systems and persistent systems. The power of the OODB comes from the seamless treatment of both persistent data, as found in databases, and transient data, as found in executing programs. In contrast to a relational DBMS where a complex data structure must be flattened out to fit into tables or joined together from those tables to form the in-memory structure, object DBMSs have no performance overhead to store or retrieve a web or hierarchy of interrelated objects.
This one-to-one mapping of object programming language objects to database objects has two benefits over other storage approaches: it provides higher performance management of objects, and it enables better management of the complex interrelationships between objects. This makes object DBMSs better suited to support applications such as financial portfolio risk analysis systems, telecommunications service applications, world wide web document structures, design and manufacturing systems, and hospital patient record systems, which have complex relationships between data.