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Databases for Planning and Manufacturing
Published in Ulrich Rembold, Robot Technology and Applications, 2020
Klaus R. Dittrich, Alfons Kemper, Peter C. Lockemann
Finally, database systems are constructed to provide data independence. With logical data independence, an application program is restricted to exactly the data it needs, and in the desired format (we say that the program is provided with a specific view of the database). In this way the program becomes immune to any changes to the database outside its own view. Consider the world model database, which certainly reflects just a small portion of the corporate database but is not affected by changes, say, to the production schedules. Physical data independence refers to the way data are physically stored and to the strategies by which they are accessed. For example, robot programs need not be changed or recompiled when new storage media are added to the database system configuration, or a new access technique is included to speed up retrieval.
Development of Process-Centric Application Systems
Published in Vivek Kale, Enterprise Process Management Systems, 2018
Views provide the following advantages: They simplify the user interface, because users can ignore the data that are not relevant to them.They favor logical data independence, because they allow for changing of the logical data structure of the DB without the need to perform corresponding changes to other rules.They make certain queries easier or more natural to define, since, by means of them, we can refer directly to the concepts instead of having to provide their definition.They provide a protection measure, because they prevent users from accessing data external to their view.
Are NoSQL Databases Affected by Schema?
Published in IETE Journal of Research, 2023
Neha Bansal, Shelly Sachdeva, Lalit K. Awasthi
In traditional relational databases, the data is fitted into a predefined schema, which is decided after normalisation, giving us the most effective schema for any dataset. Predefined schema provides data independence and user abstraction from the logical to the physical level. Still, the limitation is a) we may have to wrestle with logical schema changes while changing the database schema that becomes too complex after application development. b) with Big Data’s rise, this predefined schema provides limited support for various (semi-structured and unstructured) data. NoSQL databases alleviate the burden of formal schema definition by allowing schema flexibility and promoting redundancy. In NoSQL databases, similar information can be stored with many schemas, which supports data evolution due to the lack of restrictions imposed on the schema [7–9]. The question arises whether the schema matters in NoSQL databases because these databases do not require explicit schema declaration before data storage. And if it matters, what is its potential impact on application performance? Therefore, this paper seeks to answer why someone should take the time to define the schema by taking advantage of the schema flexibility feature rather than immediately storing the data. This paper has taken the insights of the work [10], where authors have analysed the impact of schema alternatives on document stores only, ignoring other types of NoSQL databases.