Data Processing Algorithm: Difference between revisions
Jump to navigation
Jump to search
m (Text replacement - "<B>Counter-Examples:</B>" to "<B>Counter-Example(s):</B>") |
m (Text replacement - "<B>Examples:</B>" to "<B>Example(s):</B>") |
||
Line 17: | Line 17: | ||
** It can range from being a [[Single-Pass Algorithm]] to being a [[Multi-Pass Algorithm]], depending on its [[iteration requirement]]. | ** It can range from being a [[Single-Pass Algorithm]] to being a [[Multi-Pass Algorithm]], depending on its [[iteration requirement]]. | ||
** ... | ** ... | ||
* <B> | * <B>Example(s):</B> | ||
** [[Data Transformation Algorithm]]s, such as: | ** [[Data Transformation Algorithm]]s, such as: | ||
*** [[Format Conversion Algorithm]]s, such as: | *** [[Format Conversion Algorithm]]s, such as: |
Revision as of 23:04, 28 December 2024
A Data Processing Algorithm is an algorithm that can be implemented into a data processing system (to transform input data into output data through systematic operations).
- Context:
- It can (typically) perform Core Processing Functions, such as:
- It can execute data transformations through sequential steps.
- It can handle data flows through processing pipelines.
- It can maintain data integrity through validation checks.
- It can (typically) require Processing Elements, such as:
- It can need input validation for data quality.
- It can involve intermediate storage for partial results.
- It can demand output verification for result accuracy.
- It can (often) address Processing Challenges, such as:
- It can handle data volume through scalable operations.
- It can manage processing speed through optimization techniques.
- It can ensure result consistency through error handling.
- It can range from being a Pre-Processing Algorithm to being a Post-Processing Algorithm, depending on its processing stage.
- It can range from being a Batch Processing Algorithm to being a Stream Processing Algorithm, depending on its data handling.
- It can range from being a Single-Pass Algorithm to being a Multi-Pass Algorithm, depending on its iteration requirement.
- ...
- It can (typically) perform Core Processing Functions, such as:
- Example(s):
- Data Transformation Algorithms, such as:
- Format Conversion Algorithms, such as:
- Data Type Conversion for format standardization.
- Encoding Transformation for character set changes.
- Data Structure Algorithms, such as:
- Format Conversion Algorithms, such as:
- Data Cleaning Algorithms, such as:
- Error Detection Algorithms, such as:
- Data Correction Algorithms, such as:
- Data Analysis Algorithms, such as:
- Data Integration Algorithms, such as:
- Data Merger Algorithms, such as:
- Data Synchronization Algorithms, such as:
- ...
- Data Transformation Algorithms, such as:
- Counter-Example(s):
- Random Number Generation Algorithms, which create rather than process data.
- Data Storage Algorithms, which preserve rather than transform data.
- Data Collection Algorithms, which gather rather than process data.
- Data Transmission Algorithms, which move rather than modify data.
- See: Data Pipeline, Processing System, Data Transformation, Algorithm Optimization, Data Quality.