• Tidak ada hasil yang ditemukan

PDF Faculty of Egineering Data Mining & Warehouseing Lecture-03

N/A
N/A
Info
Unduh - Bebas
Protected

Academic year: 2025

Membagikan "PDF Faculty of Egineering Data Mining & Warehouseing Lecture-03"

Copied!
10
0
0

Memuat.... (Lihat teks lengkap sekarang)

Unduh sekarang ( 10 Halaman )

Teks penuh

(1)

FACULTY OF EGINEERING

DATA MINING & WAREHOUSEING LECTURE -03

MR. DHIRENDRA

ASSISTANT PROFESSOR

RAMA UNIVERSITY

(2)

OUTLINE

 METADATA

 METADATA REPOSITORY

 DATA CUBE

 DATA MART

 VIRTUAL WAREHOUSE

 MCQ

 REFERENCES

(3)

DATA WAREHOUSING

Process of constructing and using a data warehouse.

constructed by integrating data from multiple heterogeneous sources

that support analytical reporting, structured and/or ad hoc queries, and decision making.

involves data cleaning, data integration, and data consolidations.

(4)

METADATA

• simply defined as data about data.

• used to represent other data is known as metadata.

• For example, the index of a book serves as a metadata for the contents in the book.

• metadata is the summarized data that leads us to the detailed data.

• Metadata is a road-map to data warehouse.

• Metadata in data warehouse defines the warehouse objects.

• Metadata acts as a directory. This directory helps the decision support system to locate the contents of a data warehouse.

(5)

METADATA REPOSITORY

• Business metadata

− It contains the data ownership information, business definition, and changing

policies.

• Operational metadata

− It includes currency of data and data lineage. Currency of data refers to the data being active, archived, or purged. Lineage of data means history of data migrated and

transformation applied on it.

• Data for mapping from operational environment to data warehouse

− It metadata includes source databases and their contents, data extraction, data partition, cleaning, transformation rules, data refresh and purging rules.

• The algorithms for summarization

− It includes dimension algorithms, data on granularity, aggregation, summarizing, etc.
(6)

DATA CUBE

• helps us represent data in multiple dimensions.

• It is defined by dimensions and facts.

• dimensions are the entities with respect to which an enterprise preserves the records.

• Illustration of Data Cube

(7)

DATA MART

• contain a subset of organization-wide data that is valuable to specific groups of people in an organization.

• data mart contains only those data that is specific to a particular group.

• For example, the marketing data mart may contain only data related to items, customers, and sales. Data marts are confined to subjects.

Points to Remember About Data Marts

• The life cycle of data marts may be complex in the long run, if their planning and design are not organization-wide.

• Data marts are small in size.

• Data marts are customized by department.

• The source of a data mart is departmentally structured data warehouse.

• Data marts are flexible.

(8)

VIRTUAL WAREHOUSE

• The view over an operational data warehouse is known as virtual warehouse.

• easy to build a virtual warehouse.

• Building a virtual warehouse requires excess capacity on operational database servers.

(9)

Multiple Choice Question

1. ________________defines the structure of the data held in operational databases and used by operational applications.

a) User-level metadata.

b) Data warehouse metadata.

c) Operational metadata.

d) Data mining metadata.

2.. ________________ is held in the catalog of the warehouse database system.

a) Application level metadata.

b) Algorithmic level metadata.

c) Departmental level metadata.

d) Core warehouse metadata.

3. _________maps the core warehouse metadata to business concepts, familiar and useful to end users.

a) Application level metadata.

b) User level metadata.

c) Enduser level metadata.

d) Core level metadata.

4. ______consists of formal definitions, such as a COBOL layout or a database schema.

a) Classical metadata.

b) Transformation metadata.

c) Historical metadata.

d) Structural metadata.

5. _____________consists of information in the enterprise that is not in classical form.

a) Mushy metadata.

b) Differential metadata.

c) Data warehouse.

d) Data mining.

(10)

REFERENCES

• https://www.tutorialspoint.com/dwh/dwh_overview.htm

• http://myweb.sabanciuniv.edu/rdehkharghani/files/2016/02/The-Morgan- Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline- Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition- Morgan-Kaufmann-2011.pdf

DATA MINING BOOK WRITTEN BY Micheline Kamber

• https://www.javatpoint.com/three-tier-data-warehouse-architecture

Referensi

Unduh sekarang ( PDF - 10 Halaman - 504.38 KB )

Dokumen terkait

FACULTY OF EGINEERING DATA MINING & WAREHOUSEING

This widely used data mining technique is a process that includes data preparation and selection, data cleansing, incorporating prior knowledge on data sets and interpreting accurate

FACULTY OF EGINEERING DATA MINING & WAREHOUSEING

• In order to ensure consistency of data across all access tools, the data should not be directly populated from the data warehouse, rather each tool must have its own data mart...

PDF Faculty of Egineering Data Mining & Warehouseing Lecture-11

This standard representation of OLAP data makes it easy for any reporting and analysis tool or application - including sophisticated business intelligence solutions, SQL-based

PDF Faculty of Egineering Data Mining & Warehouseing Lecture-28

Requirements of Clustering in Data Mining The following points throw light on why clustering is required in data mining − •Scalability − We need highly scalable clustering algorithms

FACULTY OF EGINEERING DATA MINING & WAREHOUSEING

DATA WAREHOUSING - OVERVIEW Term "Data Warehouse" was first coined by Bill Inmon in 1990Concept of Hard Computing a data warehouse is a subject oriented, integrated, time-variant,

FACULTY OF EGINEERING DATA MINING & WAREHOUSEING

Basic Data Mining Tasks  Classification maps data into predefined groups or classes  Supervised learning  Pattern recognition  Prediction  Regression is used to map a data

PDF Faculty of Egineering Data Mining & Warehouseing Lecture-18

Data Mining The Explosive Growth of Data: from terabytes to petabytes  Data collection and data availability o Automated data collection tools, database systems, Web, computerized

PDF Faculty of Egineering Data Mining & Warehouseing Lecture-21

Prediction Following are the examples of cases where the data analysis task is Prediction− Suppose the marketing manager needs to predict how much a given customer will spend during a