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Linear Search, Binary Search, Linked List, Stack, Queue, General Tree, Binary Tree, Binary Search Tree, Graph
Different Database Structures:
The relationships among the many individual records in databases are based on one of several logical data structures or models.  DBMS is designed to provide end users with quick, easy access to information stored in databases.  Five fundamental database structures are:
Early mainframe DBMS packages used the hierarchical structure, in which:
- Relationships between records form a hierarchy or tree-like structure.
- Records are dependent and arranged in multilevel structures, consisting of one root record and any number of subordinate levels.
-Relationships among the records are one-to-many since each data element is related only to one element above it.
- Data element or record at the highest level of the hierarchy is called the root element.  Any data element can be accessed by moving progressively downward from the root and along the branches of the tree until the desired record is located.
Network Structure: 
- Can represent more complex logical relationships, and is still used by many mainframe DBMS packages.
- Allows many-to-many relationships among records.  That is, the network model can access a data element by following one of several paths because any data element or record can be related to any number of other data elements.
Relational Structure:
- Most popular of the three database structures.
- Used by most microcomputer DBMS packages, as well as many minicomputer and mainframe systems.
- Data elements within the database are stored in the form of simple tables.  Tables are related if they contain common fields.
- DBMS packages based on the relational model can link data elements from various tables to provide information to users.
Multidimensional Structure:
- Is a variation of the relational model that uses multidimensional structures to store data and relationships between data.
- A major benefit of multidimensional databases is that they are a compact and easy-to-understand way to visualize and manipulate data elements that have many interrelationships. 
- Multidimensional databases have become the most popular database structure for the analytical databases that support online analytical processing (OLAP) applications.  
Object-Oriented Structure:
- Is considered to be one of the key technologies of a new generation of multimedia web-based applications.
- In an object-oriented structure, an object consists of data values describing the attributes of an entity plus the operations that can be performed upon the data.  This encapsulation capability allows the object-oriented model to better handle more complex types of data (graphics, voice, text) than other database structures.
- Supports inheritance, that is, new objects can be automatically created by replicating some or all of the characteristics of one or more parent objects.
-Object-capabilities and inheritance have made object-oriented database management systems (OODMS) popular in computer-aided design (CAD) applications.  Designers can develop product designs, store them as objects in an object-oriented database, and replicate and modify them to create new product designs. Multimedia web-based applications for the Internet and corporate intranets and extranets have become a major application area for object technology.

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Benefits and Limitations of Database Management:

The database management approach provides managerial end users with several important benefits such as:
1. Reduce the duplication of data
2. Integrate data so that they can be accessed by multiple programs and users.
3. Programs are not dependent on the format of the data and the type of secondary storage hardware being used.
4. Users are provided with an inquiry/response and reporting capability that allows them to easily obtain the information they need without having to write computer programs.
5. Computer programming is simplified because programs are not dependent on either the logical format of the data or their physical storage location.
6. Integrity and security of the data stored in databases can be increased since access to data and modification of the database are controlled by database management system software, a data dictionary, and a database administrator function.

The limitations of database management arise from:
1. It's increased technological complexity.
2. Development a large database and installing a DBMS can be difficult and expensive.
3. More hardware capability is required, since storage requirements for the organization's data, overhead control data, and the DBMS programs are greater.
4. Longer processing times may result from high-volume transaction processing applications since an extra layer of software (the DBMS) exists between application programs and the operating system.
5. If an organization relies on centralized databases, their vulnerability to errors, fraud, & failures is increased.
6. If an organization relies on distributed databases problems of the inconsistency of data can arise. 
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Types of Databases Management System: 
Continuing developments in information technology and its business applications have resulted in the evolution of several major types of databases. Six major conceptual categories of databases that may be found in computer-using organizations include:
Operational Databases - These databases store detailed data needed to support the operations of the entire organization.  They are also called subject area databases (SADB), transaction databases, and production databases.  Examples are customer databases, personnel databases, inventory databases, and other databases containing data generated by business operations.
Analytical Databases - These databases store data and information extracted from selected operational and external databases.  They consist of summarized data and information most needed by the organization's managers and other end users.  Analytical databases are also called management databases or information databases.  They may also be called multidimensional databases since they frequently use a multidimensional database structure to organize data. These are the databases accessed by the online analytical processing (OLAP) systems, decision support systems, and executive information systems in order to support managerial decision making.
Data Warehouse Databases - A data warehouse stores data from current and previous years that has been extracted from the various operational and management databases of an organization.  It is a central source of data that has been screened, edited, standardized, and integrated so it can be used by managers and other end-user professionals from throughout an organization.  Data warehouses may be subdivided into data marts which hold specific subsets of data from the warehouse.  A major use of data warehouse databases is data mining.  In data mining, the data in a data warehouse are processed to identify key factors and trends in historical patterns of business activity can be used to help managers make decisions about strategic changes in business operations to gain competitive advantages in the marketplace.

Distributed Databases - Many organizations replicate and distribute copies or parts of databases to network servers at a variety of sites.  These distributed databases can reside on network servers on the World Wide Web, on corporate intranets or extranets, or on other company networks.  Distributed databases may be copies of operational or analytical databases, hypermedia or discussion databases, or any other type of database.  Replication and distribution of databases are done to improve database performance and security.
End User Databases - These databases consist of a variety of data files developed by end users at their workstations.  End users may have their own electronic copies of documents they downloaded from the WEB, generated themselves, or received by electronic mail.
External Databases - Access to external, privately owned online databases or data banks is available for a fee to end users and organizations from commercial online services, and with or without charge from many sources on the Internet, especially the WEB. 

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Introduction to Data Structures
At the heart of virtually every computer program are its algorithms and its data structures.  It is hard to separate these two items, for data structures are meaningless without algorithms to create and manipulate them, and algorithms are usually trivial unless there are data structures on which to operate.

This category concentrates on four of the most basic structures:  stacks, queues, binary search trees, and priority queries.  Questions will cover these data structures and implicit algorithms, not on implementation language details.  A stack is usually used to save information that will need to be processed later.  Items are processed in a “Last-in, first-out” (LIFO) order.  A queue is usually used to process items in the order in which requests are generated; a new item is not processed until all items currently in the queue are processed.  This is also known as “first-in, first-out” (FIFO) order.  A binary search tree is used when one is storing a set of items and needs to be able to efficiently process the operations of insertion, deletion and query (i.e. find out if a particular item is part of the set and if not, which item in the set is close to the item in question).  A priority queue is used like a binary search tree, except one cannot delete an arbitrary item, nor can one make an arbitrary query.  One can only delete the smallest element of the set, and can only find out what is the smallest element of the set. 

A stack supports two operations:  PUSH and POP.  A command of the form “PUSH(A)” puts the key A at the top of the stack; the command “POP (X)” removes the top item from the stack and stores its value into variable X.  If the stack was empty (because nothing had ever been pushed on it, or if all elements have been popped off of it), then X is given the special value of NIL.  An analogy to this is a stack of books on a desk: a new book is placed on the top of the stack (pushed) and a book is removed from the top also (popped).  Some textbooks call this data structure a “push-down stack” or a “LIFO stack”.

Queues operate just like stacks, except items are removed from the bottom instead of the top.  A good physical analogy of this is the way a train conductor or newspaper boy uses a coin machine to give change: new coins are added to the tops of the piles, and change is given from the bottom of each.  Some textbooks refer to this data structure as a “FIFO stack”.
Consider the following sequence of 14 operations:

If these operations are applied to a stack, then the values of the pops are E, R, I, M, A and NIL.  After all of the operations, there are three items still on the stack: the N is at the top (it will be the next to be popped if nothing else is pushed before the pop command), and C is at the bottom.  If instead of using a stack we used a queue, then the values popped would be A, M, E, R, I and NIL.  There would be three items still in the queue: N at the top and C on the bottom.  Since items are removed from the bottom of a queue, C would be the next item to be popped regardless of any additional pushes.  
Linear Search, Binary Search, Linked List, Stack, Queue, General Tree, Binary Tree, Binary Search Tree, Graph

The data type is a way to classify various types of data such as integer, string, etc. which determines the values that can be used with the corresponding type of data, the type of operations that can be performed on the corresponding type of data.The data in the data structures are processed by certain operations. The particular data structure is chosen largely depends on the frequency of the operation that needs to be performed on the data structure.