Olap, oltp and data mining

Jan 22, 2012Download as ppt, pdf11 likes7,796 views

Data warehouses are repositories that provide access to data for complex analysis and knowledge discovery. They involve extracting, transforming, and loading data from source systems and storing it in a multidimensional database. Online transaction processing (OLTP) is used for traditional database operations like inserts and updates, while online analytical processing (OLAP) enables analysis of complex warehouse data. The goal of data mining is knowledge discovery through predicting, classifying, identifying patterns, or optimizing from stored warehouse data using techniques like classification, clustering, and regression.

OLAP, OLTP and Data Mining Title Data Warehouse DW Diagram OLTP OLAP Data Mining Data Mining goal Data Mining Elements Data Mining Application DW VS Data Mining Thanks

Data Warehouses Repository is a key data warehouse component Data warehouses provide access to data for complex analysis, knowledge discovery, and decision making. Data warehousing more generally as a collection of decision support technologies , aimed at enabling the knowledge worker (executive, manager, analyst) to make better and faster decisions.

Extract, Transform and Load Pulling data out of the source system and placing it into a data warehouse Cleaning Filtering Splitting a column into multiple columns Joining together. loading the data into a data warehouse

On-line Transaction Processing Use in Traditional databases Includes insertions, updates, and deletions, while also supporting information query requirements

On-line Analytical Processing To describe the analysis of complex data from the data warehouse ROLAP (relational OLAP) and MOLAP (multidimensional OLAP) functions

Knowledge Discovery Process The knowledge discovery process comprises six phases Data selection, Data cleansing, Enrichment, Data transformation or encoding, Data mining, Reporting and display of the discovered information.

Data Mining Data Mining as a Part of the Knowledge Discovery Process Used for knowledge discovery, the process of searching data for the new knowledge.

Data mining consists of five major elements: Extract, transform, and load transaction data onto the data warehouse system. Store and manage the data in a multidimensional database system. Provide data access to business analysts and information technology professionals. Analyse the data by application software. Present the data in a useful format, such as a graph or table.

Goal of Data Mining Prediction Identification Classification( combinations of parameters ) Optimization( Goal of data mining may be to optimize the use of limited resources such as time, space, money, or materials and to maximize output variables such as sales or profits under a given set of constraints)

Applications of Data Mining Marketing Finance Manufacturing Health Care Many people only need read-access to data, but still need a very rapid access to a larger volume of data than can conveniently be downloaded to the desktop. Data comes from multiple databases Such types of functionality provide:- Data warehousing, on-line analytical processing (OLAP), and data mining

DW VS DM Data warehousing can be seen as a process that requires a variety of activities to precede it; Data mining may be thought as an activity that draws knowledge from an existing data warehouse.

Hadoop is a framework that allows storing and processing of big data in a distributed environment. It consists of HDFS for storage of large amounts of data across clusters and YARN for managing resources and parallel processing of the stored data. HDFS stores data in blocks across data nodes and replicates the blocks for reliability. YARN schedules tasks across resource managers and node managers. Hadoop provides solutions for challenges of big data like storing large and varied data types, and processing the data faster by moving computation to the data rather than moving data to a central processor.

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Online analytical processing (OLAP) allows users to easily extract and analyze data from different perspectives. It originated in the 1970s and was formalized in 1993, with OLAP cubes organizing numeric facts by dimensions to enable fast analysis. OLAP provides operations like roll-up, drill-down, slice, and dice to analyze aggregated data across multiple systems. It offers advantages over relational databases for consistent reporting and analysis.

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OLTP systems emphasize short, frequent transactions with a focus on data integrity and query speed. OLAP systems handle fewer but more complex queries involving data aggregation. OLTP uses a normalized schema for transactional data while OLAP uses a multidimensional schema for aggregated historical data. A data warehouse stores a copy of transaction data from operational systems structured for querying and reporting, and is used for knowledge discovery, consolidated reporting, and data mining. It differs from operational systems in being subject-oriented, larger in size, containing historical rather than current data, and optimized for complex queries rather than transactions.

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The document defines and describes key concepts related to data warehousing. It provides definitions of data warehousing, data warehouse features including being subject-oriented, integrated, and time-variant. It discusses why data warehousing is needed, using scenarios of companies wanting consolidated sales reports. The 3-tier architecture of extraction/transformation, data warehouse storage, and retrieval is covered. Data marts are defined as subsets of the data warehouse. Finally, the document contrasts databases with data warehouses and describes OLAP operations.

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Olap, oltp and data mining

2. Groups Members

3. OLAP, OLTP and Data Mining Title Data Warehouse DW Diagram OLTP OLAP Data Mining Data Mining goal Data Mining Elements Data Mining Application DW VS Data Mining Thanks

4. Data Warehouses Repository is a key data warehouse component Data warehouses provide access to data for complex analysis, knowledge discovery, and decision making. Data warehousing more generally as a collection of decision support technologies , aimed at enabling the knowledge worker (executive, manager, analyst) to make better and faster decisions.

6. Extract, Transform and Load Pulling data out of the source system and placing it into a data warehouse Cleaning Filtering Splitting a column into multiple columns Joining together. loading the data into a data warehouse

8. On-line Transaction Processing Use in Traditional databases Includes insertions, updates, and deletions, while also supporting information query requirements

9. On-line Analytical Processing To describe the analysis of complex data from the data warehouse ROLAP (relational OLAP) and MOLAP (multidimensional OLAP) functions

10.

11. Knowledge Discovery Process The knowledge discovery process comprises six phases Data selection, Data cleansing, Enrichment, Data transformation or encoding, Data mining, Reporting and display of the discovered information.

12. Data Mining Data Mining as a Part of the Knowledge Discovery Process Used for knowledge discovery, the process of searching data for the new knowledge.

13. Data mining consists of five major elements: Extract, transform, and load transaction data onto the data warehouse system. Store and manage the data in a multidimensional database system. Provide data access to business analysts and information technology professionals. Analyse the data by application software. Present the data in a useful format, such as a graph or table.

14. Goal of Data Mining Prediction Identification Classification( combinations of parameters ) Optimization( Goal of data mining may be to optimize the use of limited resources such as time, space, money, or materials and to maximize output variables such as sales or profits under a given set of constraints)

15. Applications of Data Mining Marketing Finance Manufacturing Health Care Many people only need read-access to data, but still need a very rapid access to a larger volume of data than can conveniently be downloaded to the desktop. Data comes from multiple databases Such types of functionality provide:- Data warehousing, on-line analytical processing (OLAP), and data mining

16. DW VS DM Data warehousing can be seen as a process that requires a variety of activities to precede it; Data mining may be thought as an activity that draws knowledge from an existing data warehouse.

17.

Editor's Notes

#3: Groups name
#4: Title
#5: Data Warehouses
#9: OLTP
#10: OLAP,
#13: Data Mining
#18: Thank you

Olap, oltp and data mining

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