Database Management Systems

• A database management system (DBMS) is a software package designed to create and maintain databases.
• What is a database? A database is a collection of related data (University Database, Credit Card Processing Systems, Airline Reservation Systems, Banking System, etc.)
• A database is a shared, integrated computer structure that stores a collection of: End-user data, that is, raw facts of interest to the end user.
• Efficient data management typically requires the use of a computer database.
• Metadata, or data about data, through which the end-user data are integrated and managed.

Database Management System (DBMS)

A is a collection of programs that manages the database structure and controls access to the data stored in the database. In a sense, a database resembles a very well-organized electronic filing cabinet in which powerful software, known as a database management system, helps manage the cabinet’s contents.

Role and Advantages

The DBMS serves as the intermediary between the user and the database. The database structure itself is stored as a collection of files, and the only way to access the data in those files is through the DBMS.

Role

The DBMS receives all application requests and translates them into the complex operations required to fulfill those requests. The DBMS hides much of the database’s internal complexity from the application programs and users.

The application program might be written by a programmer using a programming language such as Visual Basic.NET, Java, or C#, or it might be created through a DBMS utility program.

Advantages

• Having a DBMS between the end user’s applications and the database offers some important advantages. 

1. The DBMS enables the data in the database to be shared among multiple applications or users.
2. The DBMS integrates the many different users’ views of the data into a single all-encompassing data repository.

Improved data sharing

1. The DBMS helps create an environment in which end users have better access to more and better-managed data. Such access makes it possible for end users to respond quickly to changes in their environment
2. Concurrent accesses are scheduled by DBMS: users can think of the data as being accessed by one user at a time
3. The more users access the data, the greater the risks of data security breaches. Corporations invest considerable amounts of time, effort, and money to ensure that corporate data are used properly. A DBMS provides a framework for better enforcement of data privacy and security policies.

Better data integration

Wider access to well-managed data promotes an integrated view of the organization’s operations and a clearer view of the big picture. It becomes much easier to see how actions in one segment of the company affect other segments.

Minimized data inconsistency

1. Data inconsistency exists when different versions of the same data appear in different places.
2. The probability of data inconsistency is greatly reduced in a properly designed database.

Improved data access

1. The DBMS makes it possible to produce quick answers to ad hoc queries.
2. From a database perspective, a query is a specific request issued to the DBMS for data manipulation—for example, to read or update the data.
3. Simply put, a query is a question, and an ad hoc query is a spur-of-the-moment question. The DBMS sends back an answer (called the query result set) to the application.

Improved decision making

1. Better-managed data and improved data access make it possible to generate better-quality information, on which better decisions are based. The quality of the information generated depends on the quality of the underlying data.
2. Data quality is a comprehensive approach to promoting the accuracy, validity, and timeliness of the data. While the DBMS does not guarantee data quality, it provides a framework to facilitate data quality initiatives.

Increased end-user productivity

The availability of data, combined with the tools that transform data into usable information, empowers end users to make quick, informed decisions that can make the difference between success and failure in the global economy.

Reduced Application Development Time

DBMS supports many functions common to applications that access the database

These applications are likely to be more robust than applications developed from scratch because many important tasks are handled by DBMS instead of being implemented by the application

           Note: The advantages of using a DBMS are not limited to the few just listed. In fact, you will discover          many more advantages as you learn more about the technical details of databases and their proper                 design.

Types of Databases

A DBMS can support many different types of databases. Databases can be classified according to the number of users, the database location(s), and the expected type and extent of use.

Number of User:

1. A single-user database supports only one user at a time. In other words, if user A is using the database, users B and C must wait until user A is done. A single-user database that runs on a personal computer is called a desktop database.
2. A multi-user database supports multiple users at the same time. When the multiuser database supports a relatively small number of users (usually fewer than 50) or a specific department within an organization, it is called a workgroup database.
3. When the database is used by the entire organization and supports many users (more than 50, usually hundreds) across many departments, the database is known as an enterprise database.

Location:

1. A database that supports data located at a single site is called a centralized database.
2. A database that supports data distributed across several different sites is called a distributed database.

Time and Usage:

1. Based on how DBMS are used and on the time sensitivity of the information gathered from them:
• Operational Database (Transactional or production database
• Data warehouse
2. Transactions such as product or service sales, payments, and supply purchases reflect critical day-to-day operations. Such transactions must be recorded accurately and immediately
3. A database that is designed primarily to support a company’s day-to-day operations is classified as an operational database (sometimes referred to as a transactional or production database).
4. A data warehouse focuses primarily on storing data used to generate information required to make tactical or strategic decisions. Such decisions typically require extensive “data massaging” (data manipulation) to extract information to formulate pricing decisions, sales forecasts, market positioning, and so on.
5. Most decision support data are based on data obtained from operational databases over time and stored in data warehouses. Additionally, the data warehouse can store data derived from many sources. To make it easier to retrieve such data, the data warehouse structure is quite different from that of an operational or transactional database.

Need for a DBMS

Traditional File System provided by the Operating System is insufficient to meet the requirements of enterprise applications

scenario:

A company has a large collection (500 GB) of data on database. This data is accessed concurrently by several employees. Questions about the data must be answered quickly, changes made to the data by different users must be applied consistently, and access to certain parts of the data must be restricted.

When NOT to use a DBMS

1. High initial investment (DBMS is an expensive software package)
2. Applications use small amounts of data
3. Lack of resources (disk space, memory, etc.) to support a database
4. Single-user applications
5. Overhead for flexible querying, security, concurrent access & crash recovery is not required

Describing & Storing Data in a DBMS

• A data model is a collection of high-level data description constructs used to model the application domain
• Data model hides the low-level storage details
• Most commercial database systems are based on the relational data model
• It is easier to use a semantic data model to model an application domain. A well-known semantic data model is the Entity Relationship (ER) Model
• In relational data model, the main construct is a relation.
• A relation has fields that belong to it which contain the name & data type of each field
• A description of data in terms of a data model is called the schema.
• Every relation has a schema, which describes the name of the relation, name of each attribute (field or column), and the type of each column.
• e.g. Students(sid: string, name: string, login: string, age: integer, gpa: real)
• In addition to relational data model…
• Hierarchical model
• Network model
• Object oriented model
• Object relational model

Levels of Abstraction in a DBMS

• To illustrate the meaning of data abstraction, consider the example of automotive design.
• A car designer begins by drawing the concept of the car that is to be produced. Next, engineers design the details that help transfer the basic concept into a structure that can be produced. Finally, the engineering drawings are translated into production specifications to be used on the factory floor.
• As you can see, the process of producing the car begins at a high level of abstraction and proceeds to an ever-increasing level of detail. The factory floor process cannot proceed unless the engineering details are properly specified, and the engineering details cannot exist without the basic conceptual framework created by the designer.
  

DBMS is described at four levels of abstraction:

• External Model:
• The external model is the end users’ view of the data environment. The term end users refers to people who use the application programs to manipulate the data and generate information. End users usually operate in an environment in which an application has a specific business unit focus.
• Companies are generally divided into several business units, such as sales, finance, and marketing. Each business unit is subject to specific constraints and requirements, and each one uses a data subset of the overall data in the organization.
• Therefore, end users working within those business units view their data subsets as separate from or external to other units within the organization.
• A specific representation of an external view is known as an external schema.
• Because data are being modeled, ER diagrams will be used to represent the external views.
• Conceptual Model
• The conceptual model represents a global view of the entire database as viewed by the entire organization. That is, the conceptual model integrates all external views (entities, relationships, constraints, and processes) into a single global view of the data in the enterprise.
• Also known as a conceptual schema, it is the basis for the identification and high-level description of the main data objects (avoiding any database model–specific details).
• The most widely used conceptual model is the ER model.
• Generally, the term logical design is used to refer to the task of creating a conceptual data model that could be implemented in any DBMS.
• Internal Model
• Once a specific DBMS has been selected, the internal model maps the conceptual model to the DBMS.
• The internal model is the representation of the database as “seen” by the DBMS. In other words, the internal model requires the designer to match the conceptual model’s characteristics and constraints to those of the selected implementation model.
• An internal schema depicts a specific representation of an internal model, using the database constructs supported by the chosen database.
• Because the internal model depends on specific database software, it is said to be software-dependent. Therefore, a change in the DBMS software requires that the internal model be changed to fit the characteristics and requirements of the implementation database model.
• Physical Model
• The physical model describes the way data are saved on storage media such as disks or tapes. The physical model requires the definition of both the physical storage devices and the (physical) access methods required to reach the data within those storage devices, making it both software- and hardware- dependent.
• The storage structures used are dependent on the software (the DBMS and the operating system) and on the type of storage devices that the computer can handle.
• Describes storage details
• Summarizes how the relations described in the conceptual schema are actually stored on secondary storage devices such as disks and tapes
• Decide what file organizations used to store the relations
• Create indexes to speed up data retrieval operations

Queries In A DBMS…

• A DBMS provides a specialized language, called the query language in which queries can be posed
• Relational calculus is a formal query language based on mathematical logic
• Relational algebra is another formal query language, based on a collection of operators for manipulating relations, which is equivalent in power to the calculus

Database Design Process

Database design process can be divided into 6 major steps:

Requirements Analysis

This step answers the following question:

“What users want from the database?”

• what is going to be stored in the database
• what applications are going to be built on top the database
• what are the most frequently asked queries

Result:

A well-written concise document enumerating the user’s requirements

For example: a library database…

Data to be stored can be…

• Record of all books in the library
• Record of members of the library
• Students
• Faculty
• Other members
• Record members’ borrowing information

Some applications on top of the database can be…

• Renewal service (may be on-line)
• Borrowing-Lending service
• Resource reservation system (may be on-line)
• Resource request service (may be on-line)

Conceptual Database Design

The information gathered in the requirements analysis phase is used to create a high-level description of the data in a conceptual data model. (Semantic Data Model, e.g. E-R Diagram )

Logical Database Design

In this step, we determine the DBMS to implement the database & also the data model

We utilize the conceptual schema created in the previous step and convert it into a schema of a particular data model (e.g. Relational Database Schema)

Schema Refinement

The schema created by the logical database design phase is further refined for potential problems such as redundancies (e.g. Normalization)

Physical Database Design

In this step, performance criteria are taken into consideration and further enhancements to the schema & creation of indexes are considered

Security Design

In this step, different user groups and their roles are identified. Appropriate levels of access are then provided to the data ensuring that users have access to only the necessary data.