Early Database Applications Using Hierarchical and Network Systems
Many early database applications maintained records in large organizations such as corporations, universities, hospitals, and banks. In many of these applications, there were large numbers of records of similar structure.
For example, in a university application, similar information would be kept for each student, each course, each grade record, and so on. There were also many types of records and many interrelationships among them
One of the main problems with early database systems was the intermixing of conceptual relationships with the physical storage and placement of records on disk. Hence, these systems did not provide sufficient data abstraction and program-data independence capabilities. For example, the grade records of a particular student could be physically stored next to the student record. Although this provided very efficient access for the original queries and transactions that the database was designed to handle, it did not provide enough flexibility to access records efficiently when new queries and transactions were identified. In particular, new queries that required a different storage organization for efficient processing were quite difficult to implement efficiently. It was also laborious to reorganize the database when changes were made to the application’s requirements.
Another shortcoming of early systems was that they provided only programming language interfaces. This made it time-consuming and expensive to implement new queries and transactions, since new programs had to be written, tested, and debugged. Most of these database systems were implemented on large and expensive mainframe computers starting in the mid-1960s and continuing through the 1970s and 1980s. The main types of early systems were based on three main paradigms: hierarchical systems, network model–based systems, and inverted file systems
Providing Data Abstraction and Application Flexibility with Relational Databases
Relational databases were originally proposed to separate the physical storage of data from its conceptual representation and to provide a mathematical foundation for data representation and querying. The relational data model also introduced high-level query languages that provided an alternative to programming language interfaces, making it much faster to write new queries
Relational systems were initially targeted to the same applications as earlier systems, and provided flexibility to develop new queries quickly and to reorganize the database as requirements changed. Hence, data abstraction and program-data independence were much improved when compared to earlier systems
Early experimental relational systems developed in the late 1970s and the commercial relational database management systems (RDBMS) introduced in the early 1980s were quite slow, since they did not use physical storage pointers or record placement to access related data records. With the development of new storage and indexing techniques and better query processing and optimization, their performance improved. Eventually, relational databases became the dominant type of database system for traditional database applications. Relational databases now exist on almost all types of computers, from small personal computers to large server
Object-Oriented Applications and the Need for More Complex Databases
The emergence of object-oriented programming languages in the 1980s and the need to store and share complex, structured objects led to the development of object-oriented databases (OODBs). Initially, OODBs were considered a competitor to relational databases, since they provided more general data structures. They also incorporated many of the useful object-oriented paradigms, such as abstract data types, encapsulation of operations, inheritance, and object identity. However, the complexity of the model and the lack of an early standard contributed to their limited use. They are now mainly used in specialized applications, such as engineering design, multimedia publishing, and manufacturing systems. Despite expectations that they will make a big impact, their overall penetration into the database products market remains low. In addition, many object-oriented concepts were incorporated into the newer versions of relational DBMSs, leading to object-relational database management systems, known as ORDBMSs
Interchanging Data on the Web for E-Commerce Using XML
The World Wide Web provides a large network of interconnected computers. Users can create static Web pages using a Web publishing language, such as HyperText Markup Language (HTML), and store these documents on Web servers where other users (clients) can access them and view them through Web browsers. Documents can be linked through hyperlinks, which are pointers to other documents. Starting in the 1990s, electronic commerce (e-commerce) emerged as a major application on the Web. Much of the critical information on e-commerce Web pages is dynamically extracted data from DBMSs, such as flight information, product prices, and product availability. A variety of techniques were developed to allow the interchange of dynamically extracted data on the Web for display on Web pages. The eXtended Markup Language (XML) is one standard for interchanging data among various types of databases and Web pages. XML combines concepts from the models used in document systems with database modeling concepts. Chapter 13 is devoted to an overview of XML
Extending Database Capabilities for New Applications
The success of database systems in traditional applications encouraged developers of other types of applications to attempt to use them. Such applications traditionally used their own specialized software and file and data structures. Database systems now offer extensions to better support the specialized requirements for some of these applications. The following are some examples of these applications
- Scientific applications that store large amounts of data resulting from scientific experiments in areas such as high-energy physics, the mapping of the human genome, and the discovery of protein structures
- Storage and retrieval of images, including scanned news or personal photographs, satellite photographic images, and images from medical procedures such as x-rays and MRI (magnetic resonance imaging) tests
- Storage and retrieval of videos, such as movies, and video clips from news or personal digital cameras
- Data mining applications that analyze large amounts of data to search for the occurrences of specific patterns or relationships, and for identifying unusual patterns in areas such as credit card fraud detection
- Spatial applications that store and analyze spatial locations of data, such as weather information, maps used in geographical information systems, and automobile navigational systems
- Time series applications that store information such as economic data at regular points in time, such as daily sales and monthly gross national product figures
Emergence of Big Data Storage Systems and NOSQL Databases
In the first decade of the twenty-first century, the proliferation of applications and platforms such as social media Web sites, large e-commerce companies, Web search indexes, and cloud storage/backup led to a surge in the amount of data stored on large databases and massive servers. New types of database systems were necessary to manage these huge databases—systems that would provide fast search and retrieval as well as reliable and safe storage of nontraditional types of data, such as social media posts and tweets. Some of the requirements of these new systems were not compatible with SQL relational DBMSs (SQL is the standard data model and language for relational databases). The term NOSQL is generally interpreted as Not Only SQL, meaning that in systems than manage large amounts of data, some of the data is stored using SQL systems, whereas other data would be stored using NOSQL, depending on the application requirements