What is data warehouse hierarchy?

Answer: Hierarchies are logical structures that use ordered levels as a means of organizing data. A hierarchy can be used to define data aggregation. Query tools use hierarchies to enable you to drill down into your data to view different levels of granularity. This is one of the key benefits of a data warehouse.

A concept hierarchy defines a sequence of mappings from a set of low-level concepts to higher-level, more general concepts. These mappings form a concept hierarchy for the dimension location, mapping a set of low-level concepts (i.e., cities) to higher-level, more general concepts (i.e., countries).

Subsequently, question is, what is a hierarchy table? A hierarchical database model is a data model in which the data are organized into a tree-like structure. The hierarchical database model mandates that each child record has only one parent, whereas each parent record can have one or more child records.

Likewise, what is meant by hierarchy generation?

Concept Hierarchy reduce the data by collecting and replacing low level concepts (such as numeric values for the attribute age) by higher level concepts (such as young, middle-aged, or senior). Concept hierarchy generation for numeric data is as follows: Binning (see sections before)

What are dimension hierarchies?

Hierarchical dimensions are those dimensions which have a parent/child relationship. In simple hierarchies there every child has a parent at the level above with no skipping of levels.

What is data warehouse architecture?

A data warehouse is the defacto source of business truth developed by combining data from multiple disparate sources. It supports analytical reporting, and both structured and ad hoc queries. All data warehouse architecture includes the following layers: Data Source Layer. Data Staging Layer.

What are OLAP operations?

OLAP is a category of software that allows users to analyze information from multiple database systems at the same time. These operations in relational databases are resource intensive. With OLAP data can be pre-calculated and pre-aggregated, making analysis faster. OLAP databases are divided into one or more cubes.

What is DMQL?

The Data Mining Query Language (DMQL) was proposed by Han, Fu, Wang, et al. for the DBMiner data mining system. The Data Mining Query Language is actually based on the Structured Query Language (SQL). The DMQL can work with databases and data warehouses as well. DMQL can be used to define data mining tasks.

What is multidimensional data model?

The multidimensional data model is designed to solve complex queries in real time. The multidimensional data model is composed of logical cubes, measures, dimensions, hierarchies, levels, and attributes. The simplicity of the model is inherent because it defines objects that represent real-world business entities.

What is data warehouse implementation?

Data Warehouse Implementation [Step by Step Guide] Data Warehouse design is the process of building a solution for data integration from many sources that supports analytical reporting and data analysis.

What do you mean by data warehousing?

A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process. Subject-Oriented: A data warehouse can be used to analyze a particular subject area. For example, “sales” can be a particular subject.

Why do we preprocess the data?

Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. Data preprocessing is a proven method of resolving such issues. Data preprocessing prepares raw data for further processing.

What is data cube in data mining?

A data cube refers is a three-dimensional (3D) (or higher) range of values that are generally used to explain the time sequence of an image’s data. It is a data abstraction to evaluate aggregated data from a variety of viewpoints. As such, data cubes can go far beyond 3-D to include many more dimensions.

What is data discretization?

Data discretization is defined as a process of converting continuous data attribute values into a finite set of intervals with minimal loss of information. Our results show that our method delivers on the average 31% less classification errors than many previously known discretization methods.

What is data Discretization and concept hierarchy generation?

Data Discretization & Concept hierarchy generation. Data discretization techniques can be used to reduce the number of values for a given continuous attribute by dividing the range of the attribute into intervals. A concept hierarchy for a given numerical attribute defines a discretization of the attribute.

What is data generalization?

Data Generalization is the process of creating successive layers of summary data in an evaluational database. It is a process of zooming out to get a broader view of a problem, trend or situation. It is also known as rolling-up data. But in modern data warehouses, data could come from other sources.

What is the opposite of a generalization relation in a concept hierarchy?

Specialization is opposite to Generalization. It is a top-down approach in which one higher level entity can be broken down into two lower level entity. In specialization, a higher level entity may not have any lower-level entity sets, it’s possible.

How concept hierarchies are useful in data mining?

etc. As one of the useful background knowledge, concept hierarchies organize data or concepts in hierarchical forms or in certain partial order, which are used for expressing knowledge in concise, high-level terms, and facilitating mining knowledge at multiple levels of abstraction.

What is data discretization in data mining?

Discretization is the process of putting values into buckets so that there are a limited number of possible states. The buckets themselves are treated as ordered and discrete values. You can discretize both numeric and string columns. There are several methods that you can use to discretize data.