______ is a technique of breaking up database into logical units, which may be assigned for storage at the various sites in distributed database system.
Data fragmentation
Understanding Data Distribution in Distributed Database Systems
A distributed database system is a database that is spread across different locations or sites, connected by a network. Managing data across multiple sites requires specific techniques to ensure efficiency, availability, and transparency.
One fundamental technique in distributed database systems is breaking the database into smaller, manageable pieces. These pieces can then be stored at the various sites. This process allows data to be placed closer to where it is most frequently accessed, improving performance and potentially increasing availability if one site fails.
What is Data Fragmentation?
The technique specifically designed for breaking up a database into logical units that can be distributed and stored at different sites in a distributed database system is called Data fragmentation.
Data fragmentation divides a relation (table) into smaller relations or fragments. These fragments can then be stored at different sites where the data is most needed or where storage is available. Fragmentation is done in such a way that the original relation can be reconstructed from its fragments.
Types of Data Fragmentation
Data fragmentation can be primarily categorized into:
- Horizontal Fragmentation: Dividing a relation into subsets of rows. Each fragment contains a subset of the tuples (rows) of the original relation. This is typically based on a condition applied to the attribute values.
- Vertical Fragmentation: Dividing a relation into subsets of columns. Each fragment contains a subset of the attributes (columns) of the original relation, along with the primary key to allow reconstruction.
- Mixed Fragmentation: A combination of horizontal and vertical fragmentation. A relation is first fragmented horizontally, and then the resulting fragments are fragmented vertically (or vice versa).
Analyzing Other Options
Let's look at why the other options are not correct:
- Data mining: This is a process of discovering patterns and insights from large datasets. It is an analytical technique applied to data, not a method for breaking up a database for distribution.
- Data normalization: This is a database design technique used to reduce redundancy and improve data integrity in relational databases. It involves structuring tables and their relationships according to normalization rules (like 1NF, 2NF, 3NF, BCNF). While important for database design, it is not a technique for distributing data across sites.
- Data binding: This is a technique used in software development to establish a connection between the user interface (UI) and the underlying data. It is related to presenting and manipulating data in applications, not to how the database itself is structured or distributed across multiple physical locations.
Therefore, the technique that involves breaking up a database into logical units for storage at various sites in a distributed database system is Data fragmentation.
| Technique | Description | Relevance to Distributed Databases |
|---|---|---|
| Data Fragmentation | Breaking a database (relation) into smaller pieces (fragments) based on rows (horizontal) or columns (vertical). | Directly used for distributing data storage across multiple sites. |
| Data Mining | Analyzing large datasets to discover patterns and relationships. | Used for data analysis, not distribution. |
| Data Normalization | Structuring database tables to reduce redundancy and improve integrity. | Database design principle, not a distribution method. |
| Data Binding | Connecting UI elements to data sources in applications. | Software development technique, not database distribution. |
Conclusion on Database Distribution Technique
Based on the definitions and analysis, Data fragmentation is the correct technique for breaking up a database into logical units assigned for storage at various sites in a distributed database system.
Revision Table: Distributed Database Concepts
| Concept | Brief Explanation |
|---|---|
| Distributed Database System | A database where data is stored across multiple sites interconnected by a network. |
| Data Fragmentation | Dividing a database into smaller parts (fragments) for distributed storage. |
| Horizontal Fragmentation | Splitting a table into subsets of rows. |
| Vertical Fragmentation | Splitting a table into subsets of columns (with primary key). |
| Distribution Transparency | The user or application does not need to know where the data is physically located. |
Additional Information: Database Distribution Transparency
A key goal in distributed database systems is achieving distribution transparency. This means that users and applications should be able to interact with the database as if it were a single, centralized database, without being aware of the fragmentation or replication of data or the physical location of data fragments.
Data fragmentation, while enabling distribution, introduces complexity. Distribution transparency hides this complexity from the user. It can be achieved through various levels:
- Fragmentation Transparency: Users are unaware that the data is fragmented. Queries are written as if the data were in a single, non-fragmented table. The distributed database management system (DDBMS) handles the mapping of queries to the appropriate fragments.
- Location Transparency: Users are unaware of the physical location of the data fragments. They do not need to specify the site where the data resides.
- Replication Transparency: If data is replicated (copies of data fragments are stored at multiple sites), users are unaware of the replication. The DDBMS manages the consistency of replicated copies.
Data fragmentation is a necessary step towards building a distributed database system, and achieving transparency is crucial for its usability.
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