80d ago

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.


knowt logo

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.