- Trending Categories
- Data Structure
- Operating System
- C Programming
- 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 query operations can be improved by cascading the operations?
It is the process that manages the queries and speeds them up by directing queries to the most effective data source. This process also ensures that all the system resources are used most effectively, usually by scheduling the execution of queries. The query management process monitors the actual query profiles that are used to determine which aggregations to generate.
This process services at all times that the data warehouse is created accessible to end-users. There are no major consecutive steps within this process, rather there are a set of facilities that are constantly in operations.
OLAP is an element of software technology that authorizes analysts, managers, and executives to gain insight into data through fast, consistent, interactive access in a wide variety of possible views of data that has been changed from raw data to reflect the real dimensionality of the enterprise as learned by the clients.
OLAP servers present business users with multidimensional information from data warehouses or data marts, without concerns how or where the data are saved. The physical structure and execution of OLAP servers should consider data storage issues.
Several OLAP data cube operations continue to materialize these multiple views, enabling interactive querying and analysis of the data at hand. Therefore, OLAP supports a convenient environment for interactive data analysis.
It provides online analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data generalization and data mining. There are several data mining functions, including association, classification, prediction, and clustering can be integrated with OLAP operations to build up interactive mining of knowledge at various levels of abstraction.
The objective of materializing cuboids and making OLAP index architecture is to speed up query processing in data cubes. Given materialized views, query processing must proceed as follows:
Determine which operations should be performed on the available cuboids − This contains transforming some selection, projection, roll-up (group-by), and drill-down operations represented in the query into the corresponding SQL and/or OLAP operations.
Determine to which materialized cuboid(s) the relevant operations must be used − This includes recognizing some materialized cuboids that can probably be used to solve the query, pruning the following set utilizing knowledge of “dominance” relationships among the cuboids, calculating the values of using the remaining materialized cuboids and selecting the cuboid with the minimum cost.
- How can Unicode operations be performed in Tensorflow using Python?
- Check if array sum can be made K by three operations on it in Python
- Set Operations
- Explain the performance ratios by using cash flow from operations
- What are the FTP Operations?
- Operating System Operations
- Python Boolean Operations
- What are the operations on process?
- Database Operations in C#
- Database operations in Java
- Different Operations on Processes
- String Operations in Python
- Meldable Priority Queue Operations
- Diac Operations and Applications