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DATA RESOURCE MANAGEMENT. Data Hierarchy in a Computer System. Entitities and Attributes. Problems with the Traditional File Environment. Data redundancy Program-Data dependence Lack of flexibility Poor security Lack of data-sharing and availability. Figure 7-3.
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Information RESOURCE MANAGEMENT

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Data Hierarchy in a Computer System

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Entitities and Attributes

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Problems with the Traditional File Environment Data repetition Program-Data reliance Lack of adaptability Poor security Lack of information sharing and accessibility

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Figure 7-3 Traditional File Processing

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Database Management System (DBMS) Creates and keeps up databases Eliminates necessity for information definition explanations Acts as interface between application programs and physical information records Separates sensible and physical perspectives of information

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The Contemporary Database Environment

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Components of DBMS Data definition dialect: Specifies substance and structure of database and characterizes every information component Data control dialect: Manipulates information in a database Data word reference: Stores meanings of information components, and information qualities

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Sample Data Dictionary Report

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Figure 7-6 Relational Data Model

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Three Basic Operations in a Relational Database Select: Creates subset of columns that meet particular criteria Join: Combines social tables to furnish clients with data Project: Enables clients to make new tables containing just important data

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Figure 7-7 Three Basic Operations in a Relational Database

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FLAT FILE – NOT NORMALIZED

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A Normalized Relation of ORDER

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Ensuring Database Integrity Database respectability includes the support of the coherent and business guidelines of the database. There are two sorts of "DB Integrity" that must be tended to: Entity Integrity Referential Integrity

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Entity Integrity Entity trustworthiness manages inside substance rules. These guidelines manage ranges and the authorization of invalid qualities in characteristics or conceivably between records

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Examples of Entity Integrity Data Type Integrity: extremely normal and generally essential. Checks just for "information sort" similarity with DB Schema, for example, numeric, character, consistent, date group, and so forth. Ordinarily alluded to in GIS manuals as: Range and List spaces Ranges - adequate Numeric reaches for info List - satisfactory content passages or drop-down records.

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Enforcing Integrity Not a minor assignment! Not all database administration frameworks or GIS programming empower clients to "uphold information uprightness" amid quality passage or alter sessions. In this way, the software engineer or the Database Administrator must authorize and/or check for "Trustworthiness."

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Referential Integrity Referential honesty concerns two or more tables that are connected. Case: IF table A contains an outside key that matches the essential key of table B THEN estimations of this remote key either coordinate the estimation of the essential key for a column in table B or must be invalid . Important to maintain a strategic distance from: Update peculiarity, Delete abnormality.

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Querying Databases: Elements of SQL Basic SQL Commands SELECT: Specifies sections FROM: Identifies tables or perspectives WHERE: Specifies conditions

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Using SQL-Structured Query Language SQL is a standard database convention, embraced by most "social" databases Provides linguistic structure for information: Definition Retrieval Functions (COUNT, SUM, MIN, MAX, and so forth) Updates and Deletes

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SQL Examples CREATE TABLE SALESREP Item definition expression(s) {item, sort, (width)} DELETE table WHERE expression

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Data Retrieval SELECT rundown FROM table WHERE condition list - a rundown of things or * for all things WHERE - a legitimate expression constraining the quantity of records chose can be joined with Boolean rationale: AND, OR, NOT ORDER might be utilized to arrangement comes about

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UPDATE tables SET thing = expression WHERE expression INSERT INTO table VALUES … ..

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Database Normalization: The way toward organizing information to minimize duplication and irregularities. The procedure as a rule includes separating a solitary Table into two or more tables and characterizing connections between those tables. Standardization is typically done in stages, with every stage applying more thorough tenets to the sorts of data which can be put away in a table .

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Normalization: a procedure for breaking down the outline of a social Database Design - Arrangement of properties into elements It allows the distinguishing proof of potential issues in your database plan Concepts identified with Normalization: KEYS and FUNCTIONAL DEPENDENCE

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Ex: Database Normalization (1) Sample Student Activities DB Table Poorly Designed Non-exceptional records John Smith Test the Design by creating test reports and inquiries

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Ex: Database Normalization (2) Created a special "ID" for every Record in the Activities Table Required the making of an "ID" gaze upward table for reporting (Students Table) Converted the "Level File into a Relational Database

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Ex: Database Normalization (3) Wasted Space Redundant information passage What about taking a third Activity? Inquiry Difficulties - attempting to discover all swimmers Data Inconsistencies - clashing costs

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Ex: Database Normalization (4) Students table is fine Elimination of two segments and an Activities Table rebuilding, Simplifies the Table BUT , despite everything we have Redundant information (movement charges) and information addition peculiarities. Issue: If understudy #219 exchanges we lose all references to Golf and its cost.

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Ex: Database Normalization (5) Modify the Design to guarantee that "each non-key field is subject to the entire key" Creation of the Participants Table, redresses our issues and structures a union between 2 tables. This is a Better Design!

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The Normal Forms A progression of intelligent strides to take to standardize information tables First Normal Form Second Third Boyce Codd There\'s additional, however past extent of this

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First Normal Form (1NF) All sections (fields) must be nuclear Means : no rehashing things in segments Solution: make a different table for every arrangement of traits with an essential key (parser, add inquiry) Customers CustomerID Name Orders OrderID Item CustomerID OrderDate

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Second Normal Form (2NF) In 1NF and each non-key segment is completely reliant on the (whole) essential key Means : Do(es) the key field(s) suggest whatever remains of the fields? Do we have to know both OrderID and Item to know the Customer and Date? Sign: rehashing fields Solution: Remove to a different table (Make Table) Orders OrderID CustomerID OrderDate OrderDetails OrderID Item

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Third Normal Form (3NF) In 2NF and each non-key segment is commonly autonomous means : Calculations Solution: Put computations in inquiries and structures OrderDetails OrderID Item Quantity Price Put expression in content control or in inquiry: =Quantity * Price

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Data Warehousing and Datamining Data distribution center Supports reporting and question instruments Stores present and authentic information Consolidates information for administration investigation and basic leadership

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What is a Data Warehouse? "A distribution center is a subject-arranged, incorporated, time-variation and non-unstable gathering of information in backing of administration\'s basic leadership process". Bill Inmon (1990) "A Data Warehouse is a vault of incorporated data, accessible for questions and examination. Information and data are removed from heterogeneous sources as they are created.… " Anonymous

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Components of a Data Warehouse

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Data Mining ON-LINE ANALYTICAL PROCESSING (OLAP): capacity to control, break down huge volumes of information from different viewpoints MINING: Seeking connections that are not known ahead of time. An element of the product and information association.

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DW Characteristics Subject Oriented :Data that gives data around a specific subject rather than around an organization\'s progressing operations. Coordinated : Data that is accumulated into the information stockroom from an assortment of sources and converged into a sound entirety. Time Variant : All information in the information distribution center is related to a specific day and age.

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Data Acquisition The way toward moving organization information from the source frameworks into the distribution center . Frequently the most tedious and expensive exertion. Performed with programming items known as ETL (Extract/Transform/Load) devices. More than 50 ETL instruments on business sector.

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Data Cleansing Typically performed in conjunction with information procurement. An entangled procedure that approves and, if fundamental, amends the information before it is embedded. Otherwise known as "data scrubbing" or "data quality assurance".

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