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Grid and Cloud Database Management

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Grid and Cloud Database Management, 9783642200441 (3642200443), Springer, 2011

Since the 1960s, database systems have been playing a relevant role in the information technology field. By the mid-1960s, several systems were also available for commercial purposes. Hierarchical and network database systems provided two different perspectives and data models to organize data collections. In 1970, E. Codd wrote a paper called A Relational Model of Data for Large Shared Data Banks, proposing a model relying on relational table structures. Relational databases became appealing for industries in the 1980s, and their wide adoption fostered new research and development activities toward advanced data models like object oriented or the extended relational. The online transaction processing (OLTP) support provided by the relational database systems was fundamental to make this data model successful. Even though the traditional operational systems were the best solution to manage transactions, new needs related to data analysis and decision support tasks led in the late 1980s to a new architectural model called data warehouse. It includes extraction transformation and loading (ETL) primitives and online analytical processing (OLAP) support to analyze data. From OLTP to OLAP, from transaction to analysis, from data to information, from the entity-relationship data model to a star/snowflake one, and from a customer-oriented perspective to a market-oriented one, data warehouses emerged as data repository architecture to perform data analysis and mining tasks. Relational, object-oriented, transactional, spatiotemporal, and multimedia data warehouses are some examples of database sources. Yet, the World Wide Web can be considered another fundamental and distributed data source (in the Web2.0 era it stores crucial information – from a market perspective – about user preferences, navigation, and access patterns).

Accessing and processing large amount of data distributed across several countries require a huge amount of computational power, storage, middleware services, specifications, and standards.

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