Chapter 13

Time-Variant

OPERATIONAL DATABASE: Data are recorded as current transactions. DATA WAREHOUSE: Data are recorded with a historic perspective in mind. Therefore, a time dimension is added to facilitate data analysis and various time comparisons.

Subject-oriented

OPERATIONAL DATABASE: Data are stored with a functional, or process, orientation. DATA WAREHOUSE: Data are stored with a subject orientation that facilitates multiple views of the data and facilitates decision making.

Nonvolatile

OPERATIONAL DATABASE: Data updates are frequent and common. DATA WAREHOUSE: Data cannot be changed. Data are added only periodically from historical systems. Once the data are properly stored, no changes are allowed. Therefore, the data environment is relatively static.

Integrated

OPERATIONAL DATABASE: Similar data can have different representations or meanings. DATA WAREHOUSE: Provide a unified view of all data elements with a common definition and representation for all business units.

Data Store

Optimized for decision support and is generally represented by a data warehouse or data mart. Contains two main types of data: business data and business model data.

Portals

Provide a unified, single point of entry for info. distribution. Web-based technology that uses a web browser to integrate data from multiple sources into a single web page.

Relational Online Analytical Processing (ROLAP)

Provides OLAP functionality by using relational databases and familiar relational query tools to store and analyze multidimensional data. Adds: Multidimensional data schema support within the RDBMS. Data access language and query performance optimized for multidimensional data. Support for very large databases (VLDBs)

Key Performance Indicators (KPI)

Quantifiable measurements (numeric or scale based) that assess the company's effectiveness or success in reaching its strategic and operational goals. Used for year-to-year measurements of profits, finance like earnings per share, HR for applicants and job openings, or even Education in graduation rates.

Online Analytical Processing (OLAP)

Create an advanced data analysis environment that supports decision making, business modeling, and operations research. The use multidimensional data analysis techniques. They provide advanced database support. The provide easy-to-use end-user interfaces. The support the client/server architecture.

Dashboards and business activity monitoring

Dashboards use web-based technology to present key business performance indicators or info. in a single integrated view, generally using graphics in a clear, concise, and easy to understand manner.

ETL tools

Data extraction, transformation, and loading (ETL) tools collect, filter, integrate, and aggregate operational data to be saved into a data store optimized for decision support.

Drill down

Decomposing data into more atomic components for summarized data.

Data Warehouse rule 1

The data warehouse and operational environments are separated

Data Warehouse rule 12

The data warehouse contains a chargeback mechanism for resource usage that enforces optimal use of the data by end users.

Data Warehouse rule 8

The data warehouse contains data with several levels of detail: current, old, lightly summarized, and highly summarized data.

Data Warehouse rule 3

The data warehouse contains historical data over a long time

Data Warehouse rule 2

The data warehouse data are integrated

Data Warehouse rule 6

The data warehouse data are mainly read-only with periodic batch updates from operational data. No online updates are allowed.

Data Warehouse rule 4

The data warehouse data are snapshot data captured at a given point in time

Data Warehouse rule 5

The data warehouse data are subject oriented

Data Warehouse rule 7

The data warehouse development lifecycle differs from classical systems development. The data warehouse development is data-driven, while the classic approach is process-driven.

Data Warehouse rule 10

The data warehouse environment has a system that traces data sources, transformations, and storage.

Multidimensional Online Analytical Processing (MOLAP)

Extends OLAP functionality to multidimensional database management systems (MDBMSs)

Data Warehouse rule 9

The data warehouse environment is characterized by read-only transactions to very large data sets. The operational environment is characterized by numerous update transactions to a few data entities at a time.

Data Warehouse rule 11

The data warehouse's metadata are a critical component of this environment. The metadata identify and define all data elements. The metadata provide the source, transformation, integration, storage, usage, relationships, and history of each data element.

Very Large Database (VLDBs)

A DBMS must be able to support one of these. To support one of these, DBMS is required to use advance hardware such as multiple disk arrays, and support multiple-processor technologies such as a symmetric multiprocessor (SMP) or a massively parallel processor (MPP)

Master Data Management (MDM)

A collection of concepts, techniques, and processes for the proper identification, definition, and management of data elements within an organization.

Sparsity

A measurement of the density of the data held in the data cube and is computed by dividing the total number of actual values in the cube by the total number of cells in the cube.

Governance

A method or process of government in business.

Data Mart

A small, single-subject data warehouse subset that provides decision support to a small group of people.

Business Intelligence (BI)

A term used to describe a comprehensive, cohesive, and integrated set of tools and processes used to capture, collect, integrate, store, and analyze data with the purpose of generating and presenting information used to support business decision making. CREATING INTELLIGENCE ABOUT A BUSINESS.

Roll up

Aggregating data to a higher level.

Decision Support System (DSS)

An arrangement of computerized tools used to assist managerial decision making within a business.

Data Warehouse

An integrated, subject-oriented, time-variant, nonvolatile collection of data that provides support for decision making.

Data Cube

MDBMS end users visualize the stored data as a three-dimensional cube. The location of each data value in the data cube is a function of the x-, y-, and z-axes in a three-dimensional space.

Data presentation and visualization tools

This component is in charge of presenting the data to the end user in a variety of ways. Used by the data analyst to organize and present the data.

Data query and analysis tools

This component performs data retrieval, data analysis, and data-mining tasks using the data in the data store.

Cube Cache

To speed data access, data cubes are normally help in memory called cube cache. Its only a window to a predefined subset of data in the database.