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DATA RESOURCE MANAGEMENT DATA RESOURCE MANAGEMENT
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Traditional File Processing Database Management Approach Logical Data Elements Database Structures Database Development Types of Databases Data Warehouses And Data Mining
Traditional File Processing: •
Systems in which data are organized, stored, and processed in independent files of data records.
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In the traditional file processing approach each business application was designed to use one or more specialized data files containing only specific types of data records.
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Problems:
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Data Redundancy: Independent data files included a lot of duplicated data. Lack of Data Integration: specific request of data from various files cannot be fulfilled. Data dependence (files, storage devices, software) Lack of Data Integrity or Standardization. In file processing systems, it was easy for data elements such as stock numbers and customer addresses to be defined differently by different end users and applications.
Database Management Approach: •
To solve the problems encountered with the file processing approach, the database management approach was conceived as the foundation of modern methods for managing organizational data.
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The database management approach consolidates data records, formerly held in separate files, into databases that can be accessed by many different application programs.
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In addition, a database management system (DBMS) serves as a software interface between users and databases, which helps users easily access the data in a database.
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Thus, database management involves the use of database management software to control how databases are created, interrogated, and maintained to provide information that end users need.
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Examples of popular mainframe and server versions of DBMS software are IBM’s DB2Universal Database, Oracle 10g by Oracle Corp., and MySQL, a popular open-source DBMS.
The three main functions of DBMS software are to create, maintain and use the database of an organization.
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DATABASE INTERROGATION: End users use a DBMS query feature or report generator • Response is video display or printed report • No programming is required Query language • Immediate response to ad hoc data requests Report generator • Quickly specify a format for information you want to present as a report • SQL Queries • Structured, international standard query language found in many DBMS packages • Query form is SELECT…FROM…WHERE…
Logical Data Elements: Character • A single alphabetic, numeric, or other symbol Field or data item • Represents an attribute (characteristic or quality)of some entity (object, person, place, event) • Examples: salary, job title Record • Grouping of all the fields used to describe the attributes of an entity • Example: payroll record with name, SSN, pay rate File or table Page # 3
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A group of related records
Database • An shared collection of logically related data elements • Concept of Primary key and Foreign Keys
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DATABASE STRUCTURES: 1. 2. 3. 4. 5.
Hierarchical Structure Network Structure Relational Structure Multi-dimensional Structure Object-oriented Structure
1. Hierarchical Structure: • • •
Early DBMS structure Records arranged in tree-like structure All of the relationships among records are one-to-many because each data element is related to only one element above it.
2. Network Structure: • •
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Used in some mainframe DBMS packages Many-to-many relationships
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3. Relational Structure: • • • • •
Most widely used structure Data elements are stored in tables Row represents a record; column is a field Can relate data in one file with data in another, if both files share a common data element Relational Operations: o Select Create a subset of records that meet a stated criterion Example: employees earning more than $30,000 o Join Combine two or more tables temporarily Looks like one big table o Project Create a subset of columns in a table5
4. Multi-dimensional Structure: • • • •
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Variation of relational model Uses multidimensional structures to organize data Data elements are viewed as being in cubes Popular for analytical databases that support Online Analytical Processing (OLAP)
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5. Object Oriented Structure: • The object-oriented model is considered one of the key technologies of a new generation of multimedia Web-based applications. •
An object consists of data values describing the attributes of an entity, plus the operations that can be performed upon the data.
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Encapsulation o Combine data and operations
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Inheritance o New objects can be created by replicating some or all of the characteristics of parent objects
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Used in object-oriented database management systems (OODBMS) Supports complex data types more efficiently than relational databases
DATABASE DEVELOPMENT: Database Administrator (DBA) • In charge of enterprise database development • Improves the integrity and security of organizational databases • Uses Data Definition Language (DDL) to develop and specify data contents, relationships, and structure • Stores these specifications in a data dictionary or a metadata repository. Page # 7
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A data dictionary • Contains data about data (metadata) • Relies on specialized software component to manage a database of data definitions It contains information on… • The names and descriptions of all types of data records and their interrelationships • Requirements for end users’ access and use of application programs • Database maintenance • Security
Database Development:
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Data Planning Process: Database development is a top-down process • Develop an enterprise model that defines the basic business process of the enterprise •
Define the information needs of end users in a business process
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Identify the key data elements that are needed to perform specific business activities (entity relationship diagrams)
Database Design Process: Data relationships are represented in a data model that supports a business process • This model is the schema or subschema on which to base… o The physical design of the database o The development of application programs to support business processes Logical Design o Schema -overall logical view of relationships o Subschema -logical view for specific end users o Data models for DBMS Physical Design o How data are to be physically stored and accessed on storage devices
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Data Resource Management: Data resource management is a managerial activity • Uses data management, data warehousing, and other IS technologies • Manages data resources to meet the information needs of business stakeholders
TYPES OF DATABASES: 1. 2. 3. 4.
Operational Databases Distributed Databases External Databases Hypermedia Databases
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1. OPERATIONAL DATABASES: Stores detailed data needed to support business processes and operations • •
Also called subject area databases(SADB), transaction databases, and production databases Database examples: customer, human resource, inventory etc.
2. DISTRIBUTED DATABASES: Distributed databases are copies or parts of databases stored on servers at multiple locations • improves database performance at worksites Advantages • Protection of valuable data • Data can be distributed into smaller databases • Each location has control of its local data • All locations can access any data, any where Disadvantages • Maintaining data accuracy Replication • Look at each distributed database and find changes • Apply changes to each distributed database • Very complex
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Duplication • One database is master • Duplicate the master after hours, in all locations • Easier to accomplish 3. EXTERNEL DATABASES: Databases available for a fee from commercial online services, or free from the Web • Examples: hypermedia databases, statistical databases, bibliographic and full text databases • Search engines like Google or Yahoo are external databases 4. HYPERMEDIA DATABASES: A hypermedia database contains • Hyperlinked pages of multimedia • Interrelated hypermedia page elements, rather than interrelated data records
DATA WAREHOUSES: Stores static data that has been extracted from other databases in an organization • •
Central source of data that has been cleaned, transformed, and cataloged Data is used for data mining, analytical processing, analysis, research, decision support
Data warehouses may be divided into data marts • Subsets of data that focus on specific aspects of a company (department or business process)
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Figure 5.17 illustrates the components of a complete data warehouse system.
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Data from various operational and external databases are captured, cleaned, and transformed into data that can be better used for analysis.
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This acquisition process might include activities like consolidating data from several sources, filtering out unwanted data, correcting incorrect data, converting data to new data elements, or aggregating data into new data subsets.
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These data are then stored in the enterprise data warehouse, from which they can be moved into data marts or to an analytical data store that holds data in a more useful form for certain types of analysis.
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Metadata (data that define the data in the data warehouse) are stored in a metadata repository and cataloged by a metadata directory.
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Finally, a variety of analytical software tools can be provided to query, report, mine, and analyze the data for delivery via Internet and intranet Web systems to business end users.
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Unlike a typical database in which changes can occur constantly, data in a data warehouse are static. which means that once the data are gathered up, formatted for storage, and stored in the data warehouse, they will never change.
DATA MINING: •
Data mining is a major use of data warehouse databases and the static data they contain
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In data mining, the data in a data warehouse are analyzed to reveal hidden patterns and trends in historical business activity.
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This analysis can be used to help managers make decisions about strategic changes in business operations to gain competitive advantages in the marketplace.
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See Figure 5.19. Data mining can discover new correlations, patterns, and trends in vast amounts of business data (frequently several terabytes of data) stored in data warehouses
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Data mining software uses advanced pattern recognition algorithms, as well as a variety of mathematical and statistical techniques, to sift through mountains of data to extract previously unknown strategic business information.
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