Downtime refers to the period of time when an information system is not operational or available for use. Some systems have daily scheduled downtimes, during which maintenance and backup procedures are performed.
Examples of amount of downtime criteria for an information system:
- Provides 24-hour system availability with no scheduled daily downtime
- Does not have a history of prolonged or frequent unscheduled downtimes
- for open repository and comprehensive patient records, minimal downtime should be standard
- Most system changes should be able to be made without bringing complete system
Data are classified as “dirty” when the database contains errors that render the data inaccurate. This compromises the data integrity. Dirty data may result from human errors in entering data, such as misspelling a name or incorrectly entering an ID number. Dirty data may also result from viruses, worms, or other bugs installed into a system. Hackers may enter a system and alter or remove data. Hardware and software may fail, corrupting or destroying data.
Common anti-disaster protection methods include the following:
- automated backups (allows restoration of data, if, or when, they are lost),
- off-site media storage,
- data mirroring,
- server replication (Electronic vaulting sends backups over telecommunication links to secure storage facilities. This approach eliminates labor costs and the needs to physically transport tapes. It ensures continuity by providing a reliable secondary infrastructure, also improves data integrity and shortens recovery efforts.),
- remote data replication,
- a virtual tape library that emulates multiple tape drives,
- snapshots of data at prescribed intervals.
Data mining (also known as knowledge discovery and data mining (KDD), knowledge discovery in data, and knowledge discovery in databases), refers to sorting through data to identify patterns and relationships in large relational databases in order to extract and utilize data. Data mining allows data to be extracted and transformed, stored in a data base, accessed, and analyzed by software programs. Additionally, the data retrieved can be presented in various forms, such as in graphs or tables. The types of relationships that are commonly found in data mining include classes, clusters, associations, and sequential patterns.
Data mining focuses on producing a solution that generates useful forecasting through a four-phase process:
- problem identification
- exploration of the data
- pattern discovery
- knowledge deployment, or application of knowledge to new data to forecast or generate predictions.