Describe Multidimensional Data Cube

-

 Multidimensional data cube

It basically helps in storing large amounts of data by making use of a multi-dimensional array. It increases its efficiency by keeping an index of each dimension. Thus, dimensional is able to retrieve data fast.

 Multidimensional arrays are used to store data that assures a multidimensional view of the data. A multidimensional data cube helps in storing a large amount of data. Multidimensional data cube implements indexing to represent each dimension of a data cube which improves the accessing, retrieving, and storing data from the data cube.

             OR,

Centered on a structure where the cube is patterned as a multidimensional array, most OLAP products are created. Compared to other methods, these multidimensional OLAP (MOLAP) products typically provide a better performance, primarily because they can be indexed directly into the data cube structure to capture data subsets. The cube gets sparser as the number of dimensions is larger. That ensures that no aggregated data would be stored in multiple cells that represent unique note combinations. This in turn raises the storage needs, which can at times exceed undesirable thresholds, rendering the MOLAP solution untenable for massive, multi-dimensional data sets. Compression strategies may help, but their use may damage MOLAP's natural indexing.

             OR,

Multidimensional Data Cube: 


Most OLAP products are developed based on a structure where the cube is patterned as a multidimensional array. These multidimensional OLAP (MOLAP) products usually offer improved performance when compared to other approaches mainly because they can be indexed directly into the structure of the data cube to gather subsets of data. When the number of dimensions is greater, the cube becomes sparser. That means that several cells that represent particular attribute combinations will not contain any aggregated data. This in turn boosts the storage requirements, which may reach undesirable levels at times, making the MOLAP solution untenable for huge data sets with many dimensions. Compression techniques might help; however, their use can damage the natural indexing of MOLAP.

Comments

Post a Comment

Popular posts from this blog

Suppose that a data warehouse for Big-University consists of the following four dimensions: student, course, semester, and instructor, and two measures count and avg_grade. When at the lowest conceptual level (e.g., for a given student, course, semester, and instructor combination), the avg_grade measure stores the actual course grade of the student. At higher conceptual levels, avg_grade stores the average grade for the given combination. a) Draw a snowflake schema diagram for the data warehouse. b) Starting with the base cuboid [student, course, semester, instructor], what specific OLAP operations (e.g., roll-up from semester to year) should one perform in order to list the average grade of CS courses for each BigUniversity student. c) If each dimension has five levels (including all), such as “student < major < status < university < all”, how many cuboids will this cube contain (including the base and apex cuboids)?

-
Image
a) A Snowflake Shema is shown in figure below: b) The specific OLAP operations to be performed: 1. Roll-up on course from course_id to department. 2. Roll-up on a student from student_id to university. 3. Dice on course, student with department ="CS" and university = "biguniversity" 4. Drill-down on a student from the university to student_name. c) N = 4 dimensions The cube will contain 54 = 625 cuboids.
Read more

Suppose that a data warehouse consists of the four dimensions; date, spectator, location, and game, and the two measures, count and charge, where charge is the fee that a spectator pays when watching a game on a given date. Spectators may be students, adults, or seniors, with each category having its own charge rate. a) Draw a star schema diagram for the data b) Starting with the base cuboid [date; spectator; location; game], what specific OLAP operations should perform in order to list the total charge paid by student spectators at GM Place in 2004?

-
Image
  b) The specific OLAP operations to be performed are: 1. Roll-up on date from date id to year. 2. Roll-up on spectator from spectator id to status. 3. Roll-up on location from location id to location name. 4. Roll-up on the game from game id to all. 5. Dice with status= “students”, location name= “GM Place”, and year=2004
Read more

Discuss classification or taxonomy of virtualization at different levels.

-
Image
Taxonomy of virtualization Virtualization is mainly used to emulate the execution environment, storage, and networks. The execution environment is classified into two: – Process-level – implemented on top of an existing operating system.  – System-level – implemented directly on hardware and does not or minimum requirement of the existing operating system. OR,  Virtualization covers a wide range of emulation techniques that are applied to different areas of computing. A classification of these techniques helps us better understand their characteristics and use  V irtualization is mainly used to emulate  ● Execution Environments: To provide support for the execution of the programs eg. OS, and Application.  ○ Process Level: Implemented on top of an existing OS that has full control of the hardware  ○ System Level: Implemented directly on Hardware and do not require support from existing OS.  ● Storage: Storage vi...
Read more