- DBML

Advanced Databases: Their Needs and Importance

Advanced Databases are becoming more rampant, advantageous and applicable to real life as developers of these databases strive to make that happen. In this article, I give an overview of several advanced databases and explain why they are important

Here I cite three such kinds of databases:

1. Distributed Databases

A distributed database is a database with one common schema whose parts are physically distributed via a network. For a user, a distributed database appears like a central database i.e. it is invisible to users where each data item is actually located. However, the database management system (DBMS) must periodically synchronize the scattered databases to make sure that they have all consistent data.

Advantages:

  1. Reflects organizational structure: database fragments are located in the departments they relate to.
  2. Local autonomy: a department can control the data about them (as they are the ones familiar with it)
  3. Improved availability: a fault in one database system will affect one fragment instead of the entire database.
  4. Improved performance: data is located near the site of greatest demand; the database systems themselves are parallelized, allowing load on the databases to be balanced among servers. (A high load on one module of the database won’t affect other modules of the database in a distributed database)
  5. Ergonomics: It costs less to create a network of smaller computers with the power of a single large computer.
  6. Modularity: Systems can be modified, added and removed from the distributed database without affecting other modules (systems).

2. Data Warehouses

A data warehouse (DW) is a subject-oriented, integrated, non-volatile and time-variant collection of data in support of management’s decisions. (Inmon’s definition).

Explanation:

  • Subject-oriented: The system focus is not on the applications required by the different departments of a company (e.g. econometrics and finance, medical research and biotechnology, data mining, engineering etc) but on subject areas, those that relate to all departments like customers, products, profits etc. Traditional database systems are developed for the different applications and data warehouses for the subject areas.
  • Integration: Data from various sources is represented in the data warehouse. Different sources often use different conventions in which their data is represented. It must be unified to be represented in a single format in the data warehouse. E.g., Application A uses “m” and “f” to denote gender. Application B uses “1” and “0” and application C uses “male” and “female”. One of the conventions can be used for the data warehouse; others can be converted.
  • Non-volatility: Data that have migrated into the DW are not changed or deleted.
  • Time-variance: DW data is stored in a way to allow comparisons of data loaded at different times (e.g. a company’s profits of last year versus the profits of the year before that). DW is like a series of snapshots of the data of its different sources, taken at different times, over a long period of time (typically 5-10 years).

The purpose of most databases is to present current, not historical data. Data in traditional databases is not always associated with a time whereas data in a DW always is.

Advantages:

  1. Because DW is subject-oriented, it deals with subject areas like customers, products and profits relating to all departments of a company but not to different applications relating to different departments.
  2. It converts non-homogeneous data to homogeneous data.
  3. Data do not require to be updated or deleted. It can be stored redundantly.
  4. It can present historical data over a period of 5-10 years. So it can be used for the purpose of analysis of data.

3. Multimedia Databases

Multimedia databases store multimedia such as images, audio and video. The database functionality becomes important when the number of multimedia objects stored is large.

Advantages:

  1. The database supports large objects since multimedia data such as videos can occupy up to a few gigabytes of storage.
  2. Similarity-based retrieval can be utilized in many multimedia database applications. For example, in a database that stores fingerprint images, a query fingerprint is provided, and the fingerprint(s) in the database that are similar to the query finger print are retrieved.
  3. The retrieval of some types of data such as audio and video has the requirement that data delivery must proceed at a guaranteed steady rate. This is a good upside as for example, if audio data are not supplied in time, there will be gaps in the sound. If data are supplied too fast, system buffers may overflow resulting in loss of data.


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