- Trending Categories
- Data Structure
- Operating System
- C Programming
- Social Studies
- Fashion Studies
- Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
How does data mining relate to information processing and online analytical processing?
There are three kinds of data warehouse applications such as information processing, analytical processing, and data mining.
Information processing − It provides querying, basic numerical analysis, and documenting using crosstabs, tables, charts, or graphs. A modern trend in data warehouse data processing is to make low-cost web-based accessing tools that it is integrated with web browsers.
Analytical processing − It provides basic OLAP operations, such as slice-and-dice, drilldown, roll-up, and pivoting. It usually works on historic information in both summarized and detailed forms. The major area of online analytical processing over information processing is the multidimensional information analysis of data warehouse data.
Data mining − It provides knowledge discovery by discovering hidden patterns and associations, making analytical models, implementing classification and prediction, and displaying the mining outcomes using visualization tools.
Information processing is based on queries, can discover useful data. It can answers to such queries reflect the data directly saved in databases or computable by aggregate services. They do not reflect sophisticated designs or predictability buried in the database. Thus, information processing is not data mining.
Online analytical processing comes a step nearer to data mining because it can change data summarized at several granularities from user-defined subsets of a data warehouse. The services of OLAP and data mining can be considered as disjoint −
OLAP is a data summarization/aggregation tool that supports easily data analysis, while data mining enables the automated discovery of implicit designs and interesting knowledge hidden in huge amounts of data.
OLAP tools are targeted toward simplifying and providing interactive data analysis, whereas the objective of data mining tools is to automate as much of the process as applicable, while enabling users to support the process. In this method, data mining goes one phase further traditional online analytical processing.
An alternative view of data mining can be adopted in which data mining covers both data definition and data modeling. Because OLAP systems can display general definition of information from data warehouses, OLAP services are essentially for user-directed data summarization and comparison (by drilling, pivoting, slicing, dicing, etc.).
Data mining is not limited to the analysis of data saved in data warehouses. It can explore data existing at more detailed granularities than the summarized records supported in a data warehouse.
It can also explore transactional, spatial, textual, and multimedia records that are complex to model with modern multidimensional database technology. In this context, data mining covers a wider spectrum than OLAP concerning data mining services and the complexity of the data managed.
- How do data warehousing and OLAP relate to data mining?
- Information Processing Theories of Intelligence
- Difference between Batch Processing and Stream Processing
- Parallel Data Processing in Java
- Image processing in Python?
- What is Parallel Processing?
- Difference between Computer Vision and Image Processing
- What are the interrupt stages and processing?
- Processing time with Pandas DataFrame
- Bit-processing group in 8051
- XML Processing Modules in Python
- Image Processing with NodeJS JIMP
- Text and File processing using sed Linux Commands
- What is query processing and optimization in DBMS?
- Client-side image processing with HTML