Historical Overview of Database Applications
1.7.1 Early Database Applications
Early systems were used in large organizations (corporations, hospitals, universities).
Records maintained for similar structures, e.g., student records in universities.
Intermixing of conceptual relationships with physical record storage led to inefficiencies.
Major issues included lack of flexibility and difficulty in reorganization due to system constraints.
1.7.2 Relational Databases
Proposed to separate physical data storage from conceptual representation.
Introduced high-level query languages, improving flexibility and speed of database organization.
Initial versions were slow but improved with advancements in storage and indexing techniques.
Relational databases became dominant due to their adaptability across various computer systems.
1.7.3 Object-Oriented Databases (OODBs)
Emerged in the 1980s to store complex structured objects.
Offered more general data structures and incorporated object-oriented programming paradigms.
However, complexity and lack of standardization limited widespread adoption.
Some object-oriented concepts were integrated into relational DBMSs leading to object-relational DBMS (ORDBMS).
1.7.4 XML and E-Commerce
The World Wide Web enabled dynamic extraction of data for e-commerce applications (e.g., product prices, flight information).
XML (eXtended Markup Language) standardized data interchange between databases and web pages.
1.7.5 Database Extensions for New Applications
Database systems expanded to support scientific, multimedia, and data mining applications, among others.
Specific examples include:
Scientific data for experiments (e.g., human genome mapping)
Image and video storage (e.g., medical imaging, personal photos)
Data mining and analysis for fraud detection and pattern recognition.
Spatial applications for geographical information systems.
Time series applications for tracking economic data.
1.7.6 Big Data and NOSQL Databases
Surge in data volume from social media and e-commerce led to the need for new database systems.
Requirements for fast search and retrieval of nontraditional data types.
NOSQL (Not Only SQL) databases emerged to manage large datasets, accommodating various storage strategies depending on application needs.
Notes on Database Applications History and DBMS Benefits
Historical Overview of Database Applications
1.7.1 Early Database Applications
Early systems were used in large organizations (corporations, hospitals, universities).
Records maintained for similar structures, e.g., student records in universities.
Intermixing of conceptual relationships with physical record storage led to inefficiencies.
Major issues included lack of flexibility and difficulty in reorganization due to system constraints.
1.7.2 Relational Databases
Proposed to separate physical data storage from conceptual representation.
Introduced high-level query languages, improving flexibility and speed of database organization.
Initial versions were slow but improved with advancements in storage and indexing techniques.
Relational databases became dominant due to their adaptability across various computer systems.
1.7.3 Object-Oriented Databases (OODBs)
Emerged in the 1980s to store complex structured objects.
Offered more general data structures and incorporated object-oriented programming paradigms.
However, complexity and lack of standardization limited widespread adoption.
Some object-oriented concepts were integrated into relational DBMSs leading to object-relational DBMS (ORDBMS).
1.7.4 XML and E-Commerce
The World Wide Web enabled dynamic extraction of data for e-commerce applications (e.g., product prices, flight information).
XML (eXtended Markup Language) standardized data interchange between databases and web pages.
1.7.5 Database Extensions for New Applications
Database systems expanded to support scientific, multimedia, and data mining applications, among others.
Specific examples include:
Scientific data for experiments (e.g., human genome mapping)
Image and video storage (e.g., medical imaging, personal photos)
Data mining and analysis for fraud detection and pattern recognition.
Spatial applications for geographical information systems.
Time series applications for tracking economic data.
1.7.6 Big Data and NOSQL Databases
Surge in data volume from social media and e-commerce led to the need for new database systems.
Requirements for fast search and retrieval of nontraditional data types.
NOSQL (Not Only SQL) databases emerged to manage large datasets, accommodating various storage strategies depending on application needs.