This document provides an introduction to database management systems and Microsoft Access. It discusses who needs a database and the differences between data and information. Key database concepts are explained, including data processing activities, database terminology, database management systems, relational database management systems and their applications. Different types of databases are also outlined.
This document discusses decision support systems (DSS) and data warehousing. It provides definitions of DSS as interactive computer-based systems that help decision makers use data and models to identify and solve problems. It also defines data warehousing as a subject-oriented, integrated, nonvolatile, and time-variant collection of data used to support management decisions. The document outlines the concepts of operational databases, data warehousing architectures, and multidimensional database structures.
Data Ware House System in Cloud EnvironmentIJERA Editor
To reduce Cost of data ware house deployment , virtualization is very Important. virtualization can reduce Cost
and as well as tremendous Pressure of managing devices, Storages Servers, application models & main Power.
In current time, data were house is more effective and important Concepts that can make much impact in
decision support system in Organization. Data ware house system takes large amount of time, cost and efforts
then data base system to Deploy and develop in house system for an Organization . Due to this reason that,
people now think about cloud computing as a solution of the problem instead of implementing their own data
were house system . In this paper, how cloud environment can be established as an alternative of data ware
house system. It will given the some knowledge about better environment choice for the organizational need.
Organizational Data were house and EC2 (elastic cloud computing ) are discussed with different parameter like
ROI, Security, scalability, robustness of data, maintained of system etc
Implementation of Multi-node Clusters in Column Oriented Database using HDFSIJEACS
Generally HBASE is NoSQL database which runs in the Hadoop environment, so it can be called as Hadoop Database. By using Hadoop distributed file system and map reduce with the implementation of key/value store as real time data access combines the deep capabilities and efficiency of map reduce. Basically testing is done by using single node clustering which improved the performance of query when compared to SQL, even though performance is enhanced, the data retrieval becomes complicated as there is no multi node clusters and totally based on SQL queries. In this paper, we use the concepts of HBase, which is a column oriented database and it is on the top of HDFS (Hadoop distributed file system) along with multi node clustering which increases the performance. HBase is key/value store which is Consistent, Distributed, Multidimensional and Sorted map. Data storage in HBase in the form of cells, and here those cells are grouped by a row key. Hence our proposal yields better results regarding query performance and data retrieval compared to existing approaches.
A Computer database is a collection of logically related data that is stored in a computer system,so that a computer program or person using a query language can use it to answer queries. An operational database (OLTP) contains up-to-date, modifiable application specific data. A data warehouse (OLAP) is a subject-oriented, integrated, time-variant and non-volatile collection of data used to make business decisions. Hadoop Distributed File System (HDFS) allows storing large amount of data on a cloud of
machines. In this paper, we surveyed the literature related to operational databases, data warehouse and hadoop technology.
This document provides an overview of data warehousing concepts including definitions of data warehousing, the components of a data warehouse architecture, characteristics of data, and the process of data modeling. It describes what a data warehouse is and some key elements like the data sources, data integration, business intelligence tools, and different types of databases. It also discusses data attributes, metadata, and the three levels of data modeling.
Explain growth and importance of databases
Name limitations of conventional file processing
Identify five categories of databases
Explain advantages of databases
Identify costs and risks of databases
List components of database environment
Describe evolution of database systems
This document provides an introduction to database management systems. It discusses how database systems have become essential for organizations to store and access crucial information needed to run their businesses. The document traces the evolution of data systems from manual files to modern database management systems and examines how increasing information demands and rapid technology growth drove this transition. It also provides overviews of key database concepts like data modeling, database design, database capabilities for storage, queries and more. Finally, it outlines the roles of various people who work with databases, such as system analysts, database designers, application developers and administrators, and end users.
Data warehousing change in a challenging environmentDavid Walker
This white paper discusses the challenges of managing changes in a data warehousing environment. It describes a typical data warehouse architecture with source systems feeding data into a data warehouse and then into data marts or cubes. It also outlines the common processes involved like development, operations and data quality processes. The paper then discusses two major challenges - configuration/change management as there are frequent changes from source systems, applications and technologies that impact the data warehouse. The other challenge is managing and improving data quality as issues from source systems are often replicated in the data warehouse.
This document discusses decision support systems (DSS) and data warehousing. It provides definitions of DSS as interactive computer-based systems that help decision makers use data and models to identify and solve problems. It also defines data warehousing as a subject-oriented, integrated, nonvolatile, and time-variant collection of data used to support management decisions. The document outlines the concepts of operational databases, data warehousing architectures, and multidimensional database structures.
Data Ware House System in Cloud EnvironmentIJERA Editor
To reduce Cost of data ware house deployment , virtualization is very Important. virtualization can reduce Cost
and as well as tremendous Pressure of managing devices, Storages Servers, application models & main Power.
In current time, data were house is more effective and important Concepts that can make much impact in
decision support system in Organization. Data ware house system takes large amount of time, cost and efforts
then data base system to Deploy and develop in house system for an Organization . Due to this reason that,
people now think about cloud computing as a solution of the problem instead of implementing their own data
were house system . In this paper, how cloud environment can be established as an alternative of data ware
house system. It will given the some knowledge about better environment choice for the organizational need.
Organizational Data were house and EC2 (elastic cloud computing ) are discussed with different parameter like
ROI, Security, scalability, robustness of data, maintained of system etc
Implementation of Multi-node Clusters in Column Oriented Database using HDFSIJEACS
Generally HBASE is NoSQL database which runs in the Hadoop environment, so it can be called as Hadoop Database. By using Hadoop distributed file system and map reduce with the implementation of key/value store as real time data access combines the deep capabilities and efficiency of map reduce. Basically testing is done by using single node clustering which improved the performance of query when compared to SQL, even though performance is enhanced, the data retrieval becomes complicated as there is no multi node clusters and totally based on SQL queries. In this paper, we use the concepts of HBase, which is a column oriented database and it is on the top of HDFS (Hadoop distributed file system) along with multi node clustering which increases the performance. HBase is key/value store which is Consistent, Distributed, Multidimensional and Sorted map. Data storage in HBase in the form of cells, and here those cells are grouped by a row key. Hence our proposal yields better results regarding query performance and data retrieval compared to existing approaches.
A Computer database is a collection of logically related data that is stored in a computer system,so that a computer program or person using a query language can use it to answer queries. An operational database (OLTP) contains up-to-date, modifiable application specific data. A data warehouse (OLAP) is a subject-oriented, integrated, time-variant and non-volatile collection of data used to make business decisions. Hadoop Distributed File System (HDFS) allows storing large amount of data on a cloud of
machines. In this paper, we surveyed the literature related to operational databases, data warehouse and hadoop technology.
This document provides an overview of data warehousing concepts including definitions of data warehousing, the components of a data warehouse architecture, characteristics of data, and the process of data modeling. It describes what a data warehouse is and some key elements like the data sources, data integration, business intelligence tools, and different types of databases. It also discusses data attributes, metadata, and the three levels of data modeling.
Explain growth and importance of databases
Name limitations of conventional file processing
Identify five categories of databases
Explain advantages of databases
Identify costs and risks of databases
List components of database environment
Describe evolution of database systems
This document provides an introduction to database management systems. It discusses how database systems have become essential for organizations to store and access crucial information needed to run their businesses. The document traces the evolution of data systems from manual files to modern database management systems and examines how increasing information demands and rapid technology growth drove this transition. It also provides overviews of key database concepts like data modeling, database design, database capabilities for storage, queries and more. Finally, it outlines the roles of various people who work with databases, such as system analysts, database designers, application developers and administrators, and end users.
Data warehousing change in a challenging environmentDavid Walker
This white paper discusses the challenges of managing changes in a data warehousing environment. It describes a typical data warehouse architecture with source systems feeding data into a data warehouse and then into data marts or cubes. It also outlines the common processes involved like development, operations and data quality processes. The paper then discusses two major challenges - configuration/change management as there are frequent changes from source systems, applications and technologies that impact the data warehouse. The other challenge is managing and improving data quality as issues from source systems are often replicated in the data warehouse.
Topics in Data Management include data analysis, database management systems, data modeling, database administration, data warehousing, data mining, data quality assurance, data security, and data architecture. Data analysis involves looking at and summarizing data to extract useful information and develop conclusions. Database management systems are used to manage databases and are used by over 90% of people using computers. Data modeling is the process of structuring and organizing data to be implemented in a database. Database administrators are responsible for ensuring the security, performance, and availability of organizational data.
Storage Characteristics Of Call Data Records In Column Store DatabasesDavid Walker
This document summarizes the storage characteristics of call data records (CDRs) in column store databases. It discusses what CDRs are, what a column store database is, and how efficient column stores are for storing CDR and similar machine-generated data. It provides details on the structure and content of sample CDR data, how the data was loaded into a Sybase IQ column store database for testing purposes, and the results in terms of storage characteristics and what would be needed for a production environment.
This document provides a review of Hadoop storage and clustering algorithms. It begins with an introduction to big data and the challenges of storing and processing large, diverse datasets. It then discusses related technologies like cloud computing and Hadoop, including the Hadoop Distributed File System (HDFS) and MapReduce processing model. The document analyzes and compares various clustering techniques like K-means, fuzzy C-means, hierarchical clustering, and Self-Organizing Maps based on parameters such as number of clusters, size of clusters, dataset type, and noise.
This document provides an overview of data warehousing. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used to support management decisions. The document discusses why data warehousing differs from operational systems, sample data warehouse designs, and the mechanics of the design process including interviewing users, assembling teams, hardware/software choices, and handling aggregates.
This document provides an overview of database management systems (DBMS). It discusses the objectives and features of DBMS, including organizing data in a structured way and storing data only once. Common applications of DBMS are also outlined, such as enterprise information systems, banking, universities, and telecommunications. The document then examines the purpose of using a DBMS to share and secure data. Key concepts like data models, database languages, and the relational database model are introduced at a high level.
This article provides insight into relational database management systems - RDBMS, sheds light on the importance of data in any organization and how critical the role of a DBA is to its success. It highlights DBA attributes, responsibilities, tasks, career path & remuneration.
This document discusses Relational Database Management Systems (RDBMS). It provides an overview of early database systems like hierarchical and network models. It then describes the key concepts of RDBMS including relations, attributes, and using tables, rows, and columns. RDBMS uses Structured Query Language (SQL) and has advantages over early systems by allowing data to be spread across multiple tables and accessed simultaneously by users.
Representing Non-Relational Databases with Darwinian NetworksIJERA Editor
The Darwinian networks (DNs) are first introduced by Dr Butz [1] to simplify and clarify how to work with Bayesian networks (BNs). DNs can unify modeling and reasoning tasks into a single platform using the graphical manipulation of the probability tables that takes on a biological feel. From this view of the DNs, we propose a graphical library to represent and depict non-relational databases using DNs. Because of the growing of this kind of databases, we need even more tools to help in the management work, and the DNs can help with these tasks.
Capitalizing on the New Era of In-memory ComputingInfosys
- In-memory computing processes large amounts of data stored in server memory within seconds, enabling real-time insights from massive data volumes. This overcomes limitations of traditional disk-based systems with their longer processing times.
- Key advantages include vastly reduced processing latency from milliseconds for disk reads to nanoseconds for memory, enabling real-time decisions. It also reduces costs by consolidating transactional and analytical workloads on a single system.
- Application areas that can benefit include personalized incentives, optimized pricing, high-frequency trading, next-generation analytics, and risk management where rapid insights from large data volumes are critical.
This document outlines a course on data warehousing and data mining. It introduces key concepts like relational databases, data warehouses, dimensional modeling, and data mining techniques. It also details the course objectives, schedule, assignments, and policies. The goal is for students to gain experience applying data mining methods and understanding the relationship between data mining and other fields.
Rohit Sharma presented a seminar on a project that discussed data warehousing, data mining, and how to apply data warehousing concepts to project data. The presentation covered terminology, pulling together and correctly using data from multiple sources, software requirements including PHP and MySQL, and screenshots of the admin panel and user interfaces.
Applications of machine learning are widely used in the real world with either supervised or unsupervised
learning process. Recently emerged domain in the information technologies is Big Data which refers to
data with characteristics such as volume, velocity and variety. The existing machine learning approaches
cannot cope with Big Data. The processing of big data has to be done in an environment where distributed
programming is supported. In such environment like Hadoop, a distributed file system like Hadoop
Distributed File System (HDFS) is required to support scalable and efficient access to data. Distributed
environments are often associated with cloud computing and data centres. Naturally such environments are
equipped with GPUs (Graphical Processing Units) that support parallel processing. Thus the environment
is suitable for processing huge amount of data in short span of time. In this paper we propose a framework
that can have generic operations that support processing of big data. Our framework provides building
blocks to support clustering of unstructured data which is in the form of documents. We proposed an
algorithm that works in scheduling jobs of multiple users. We built a prototype application to demonstrate
the proof of concept. The empirical results revealed that the proposed framework shows 95% accuracy
when the results are compared with the ground truth.
This document provides an overview of key concepts in database and database management systems (DBMS). It defines what a database is, different data models, database languages like DDL and SQL, types of database users, and the core functions and structure of a DBMS like storage management, transaction management, and query optimization. It also discusses database applications, levels of abstraction in database design, and common database architectures.
The document discusses database concepts and systems. It describes the role of database administrators in planning, designing, and maintaining databases. It also discusses popular database management systems, specialized databases, factors to consider when selecting a database, and how databases can be linked to the internet and used for business intelligence. The summary provides an overview of key database topics covered in the document like data models, database management systems, data warehouses, and data mining.
This document discusses data modeling and the relational database model. It explains that building a database requires both a logical design of how data should be structured and arranged, as well as a physical design to optimize performance and costs. The relational database model stores data in tables and allows for selecting, projecting, joining, and linking of tables. A database management system provides an interface for users and applications to define, modify, store, retrieve, and manipulate data using languages like SQL.
This document provides an overview of data mining, data warehousing, and decision support systems. It defines data mining as extracting hidden predictive patterns from large databases and data warehousing as integrating data from multiple sources into a central repository for reporting and analysis. Common data warehousing techniques include data marts, online analytical processing (OLAP), and online transaction processing (OLTP). The document also discusses the benefits of data warehousing such as enhanced business intelligence and historical data analysis, as well challenges around meeting user expectations and optimizing systems. Finally, it describes decision support systems and executive information systems as tools that combine data and models to support business decision making.
Data mining is an important part of business intelligence and refers to discovering interesting patterns from large amounts of data. It involves applying techniques from multiple disciplines like statistics, machine learning, and information science to large datasets. While organizations collect vast amounts of data, data mining is needed to extract useful knowledge and insights from it. Some common techniques of data mining include classification, clustering, association analysis, and outlier detection. Data mining tools can help organizations apply these techniques to gain intelligence from their data warehouses.
The document discusses key concepts related to databases including:
- A database is an organized collection of data stored electronically and accessed via a DBMS.
- Data is logically organized into records, tables, and databases for meaningful representation to users.
- Databases offer advantages like reduced data redundancy, improved data integrity, and easier data sharing.
- Database subsystems include the database engine, data definition language, and data administration.
The document then covers database types, uses, issues, and security concepts.
Topics in Data Management include data analysis, database management systems, data modeling, database administration, data warehousing, data mining, data quality assurance, data security, and data architecture. Data analysis involves looking at and summarizing data to extract useful information and develop conclusions. Database management systems are used to manage databases and are used by over 90% of people using computers. Data modeling is the process of structuring and organizing data to be implemented in a database. Database administrators are responsible for ensuring the security, performance, and availability of organizational data.
Storage Characteristics Of Call Data Records In Column Store DatabasesDavid Walker
This document summarizes the storage characteristics of call data records (CDRs) in column store databases. It discusses what CDRs are, what a column store database is, and how efficient column stores are for storing CDR and similar machine-generated data. It provides details on the structure and content of sample CDR data, how the data was loaded into a Sybase IQ column store database for testing purposes, and the results in terms of storage characteristics and what would be needed for a production environment.
This document provides a review of Hadoop storage and clustering algorithms. It begins with an introduction to big data and the challenges of storing and processing large, diverse datasets. It then discusses related technologies like cloud computing and Hadoop, including the Hadoop Distributed File System (HDFS) and MapReduce processing model. The document analyzes and compares various clustering techniques like K-means, fuzzy C-means, hierarchical clustering, and Self-Organizing Maps based on parameters such as number of clusters, size of clusters, dataset type, and noise.
This document provides an overview of data warehousing. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used to support management decisions. The document discusses why data warehousing differs from operational systems, sample data warehouse designs, and the mechanics of the design process including interviewing users, assembling teams, hardware/software choices, and handling aggregates.
This document provides an overview of database management systems (DBMS). It discusses the objectives and features of DBMS, including organizing data in a structured way and storing data only once. Common applications of DBMS are also outlined, such as enterprise information systems, banking, universities, and telecommunications. The document then examines the purpose of using a DBMS to share and secure data. Key concepts like data models, database languages, and the relational database model are introduced at a high level.
This article provides insight into relational database management systems - RDBMS, sheds light on the importance of data in any organization and how critical the role of a DBA is to its success. It highlights DBA attributes, responsibilities, tasks, career path & remuneration.
This document discusses Relational Database Management Systems (RDBMS). It provides an overview of early database systems like hierarchical and network models. It then describes the key concepts of RDBMS including relations, attributes, and using tables, rows, and columns. RDBMS uses Structured Query Language (SQL) and has advantages over early systems by allowing data to be spread across multiple tables and accessed simultaneously by users.
Representing Non-Relational Databases with Darwinian NetworksIJERA Editor
The Darwinian networks (DNs) are first introduced by Dr Butz [1] to simplify and clarify how to work with Bayesian networks (BNs). DNs can unify modeling and reasoning tasks into a single platform using the graphical manipulation of the probability tables that takes on a biological feel. From this view of the DNs, we propose a graphical library to represent and depict non-relational databases using DNs. Because of the growing of this kind of databases, we need even more tools to help in the management work, and the DNs can help with these tasks.
Capitalizing on the New Era of In-memory ComputingInfosys
- In-memory computing processes large amounts of data stored in server memory within seconds, enabling real-time insights from massive data volumes. This overcomes limitations of traditional disk-based systems with their longer processing times.
- Key advantages include vastly reduced processing latency from milliseconds for disk reads to nanoseconds for memory, enabling real-time decisions. It also reduces costs by consolidating transactional and analytical workloads on a single system.
- Application areas that can benefit include personalized incentives, optimized pricing, high-frequency trading, next-generation analytics, and risk management where rapid insights from large data volumes are critical.
This document outlines a course on data warehousing and data mining. It introduces key concepts like relational databases, data warehouses, dimensional modeling, and data mining techniques. It also details the course objectives, schedule, assignments, and policies. The goal is for students to gain experience applying data mining methods and understanding the relationship between data mining and other fields.
Rohit Sharma presented a seminar on a project that discussed data warehousing, data mining, and how to apply data warehousing concepts to project data. The presentation covered terminology, pulling together and correctly using data from multiple sources, software requirements including PHP and MySQL, and screenshots of the admin panel and user interfaces.
Applications of machine learning are widely used in the real world with either supervised or unsupervised
learning process. Recently emerged domain in the information technologies is Big Data which refers to
data with characteristics such as volume, velocity and variety. The existing machine learning approaches
cannot cope with Big Data. The processing of big data has to be done in an environment where distributed
programming is supported. In such environment like Hadoop, a distributed file system like Hadoop
Distributed File System (HDFS) is required to support scalable and efficient access to data. Distributed
environments are often associated with cloud computing and data centres. Naturally such environments are
equipped with GPUs (Graphical Processing Units) that support parallel processing. Thus the environment
is suitable for processing huge amount of data in short span of time. In this paper we propose a framework
that can have generic operations that support processing of big data. Our framework provides building
blocks to support clustering of unstructured data which is in the form of documents. We proposed an
algorithm that works in scheduling jobs of multiple users. We built a prototype application to demonstrate
the proof of concept. The empirical results revealed that the proposed framework shows 95% accuracy
when the results are compared with the ground truth.
This document provides an overview of key concepts in database and database management systems (DBMS). It defines what a database is, different data models, database languages like DDL and SQL, types of database users, and the core functions and structure of a DBMS like storage management, transaction management, and query optimization. It also discusses database applications, levels of abstraction in database design, and common database architectures.
The document discusses database concepts and systems. It describes the role of database administrators in planning, designing, and maintaining databases. It also discusses popular database management systems, specialized databases, factors to consider when selecting a database, and how databases can be linked to the internet and used for business intelligence. The summary provides an overview of key database topics covered in the document like data models, database management systems, data warehouses, and data mining.
This document discusses data modeling and the relational database model. It explains that building a database requires both a logical design of how data should be structured and arranged, as well as a physical design to optimize performance and costs. The relational database model stores data in tables and allows for selecting, projecting, joining, and linking of tables. A database management system provides an interface for users and applications to define, modify, store, retrieve, and manipulate data using languages like SQL.
This document provides an overview of data mining, data warehousing, and decision support systems. It defines data mining as extracting hidden predictive patterns from large databases and data warehousing as integrating data from multiple sources into a central repository for reporting and analysis. Common data warehousing techniques include data marts, online analytical processing (OLAP), and online transaction processing (OLTP). The document also discusses the benefits of data warehousing such as enhanced business intelligence and historical data analysis, as well challenges around meeting user expectations and optimizing systems. Finally, it describes decision support systems and executive information systems as tools that combine data and models to support business decision making.
Data mining is an important part of business intelligence and refers to discovering interesting patterns from large amounts of data. It involves applying techniques from multiple disciplines like statistics, machine learning, and information science to large datasets. While organizations collect vast amounts of data, data mining is needed to extract useful knowledge and insights from it. Some common techniques of data mining include classification, clustering, association analysis, and outlier detection. Data mining tools can help organizations apply these techniques to gain intelligence from their data warehouses.
The document discusses key concepts related to databases including:
- A database is an organized collection of data stored electronically and accessed via a DBMS.
- Data is logically organized into records, tables, and databases for meaningful representation to users.
- Databases offer advantages like reduced data redundancy, improved data integrity, and easier data sharing.
- Database subsystems include the database engine, data definition language, and data administration.
The document then covers database types, uses, issues, and security concepts.
The document provides an introduction to database management systems. It defines key terms like data, information, database, and record. It describes the differences between manual and computerized data processing. It explains that a database management system (DBMS) is software that manages databases and allows data to be easily accessed, managed, and updated. It then discusses the history of DBMS, common applications of databases, the data processing cycle, features of databases, types of database users, concepts of data abstraction, and database system architectures.
The document provides an introduction to database management systems and related concepts. It defines key terms like data, information, database, and record. It describes the differences between manual and computerized data processing. It then discusses traditional file-based data storage approaches and their limitations. The document introduces database management systems and their applications. It provides a brief history of DBMS and discusses the data processing cycle and the roles of different database users. Finally, it covers various database models including hierarchical, network, relational, object-oriented, and object-relational models.
This document provides an introduction to databases. It defines what a database is and explains that a database is a collection of related data for a specific purpose. It also defines what a database management system (DBMS) is, describing its key functions such as defining, creating, and manipulating databases. Some advantages of DBMSs over traditional file systems are also discussed, such as reducing data redundancy and improving data integrity and security. Finally, it outlines some common types of users who interact with databases.
This document provides an introduction to fundamental concepts of database systems. It discusses what a database is and different approaches to data management, including manual, file-based and database approaches. The key benefits of the database approach are that it allows data sharing, reduces redundancy and improves data integrity. A database management system (DBMS) is software that manages databases and provides users with facilities to work with data. Major components of a DBMS include data definition language, data manipulation language and data dictionary. The document also covers database models like hierarchical and network models and roles involved in database design and use.
dbms rdbms book by Muhammad Sharif
Database systems handbook 4rth edition.
This book is written by Muhammad Sharif, Software Engineer in SKMCHRC Lahore.
This document provides an overview of database management systems (DBMS). It defines what a DBMS is and discusses the need for DBMS compared to traditional file systems. Specifically, it notes that a DBMS allows for centralized control of data to reduce redundancy and improve data sharing, integrity, security and access. Examples of popular DBMS are provided, along with different database types based on number of users and data location. Common applications of DBMS are also listed.
This document contains 26 questions and their answers related to management information systems. The questions cover topics such as data resource management, databases, data warehousing, transaction processing, decision support systems, end user computing, information systems in various business functions like marketing, manufacturing, human resources, accounting, and financial management. Other topics include information resource management, file organization techniques, and humans as information processors.
The document discusses database management systems. It defines a database as an organized collection of stored data that can be accessed electronically. A database management system (DBMS) is software that allows users and applications to capture, analyze, and interact with a database. A DBMS performs tasks like data definition, updates, retrieval, and administration. It stores data on dedicated database servers for security, reliability, and high-performance access and management of the stored data. A DBMS provides multiple logical views of the database data for different user groups and roles.
This document provides an introduction and overview of database management systems (DBMS). It discusses what a DBMS is, its functions such as data definition, manipulation, retrieval and administration. It also covers the history of DBMS, differences between file systems and DBMS, and concepts like data abstraction. DBMS has advanced significantly in the last 20-22 years and makes it easier to store, retrieve and manipulate data from a database in an accurate and reliable manner compared to traditional file systems.
This document outlines chapters from a book on database systems and management. It includes 21 chapters covering topics such as data types, data modeling, database design, normalization, transactions, queries, file structures, security, backup and recovery, Oracle technologies and applications development. The author dedicates the book to his reader for inspiring his work. It was completed in October 2022 with acknowledgments to reviewers and thanks to God.
CHAPTER 1 INTRODUCTION TO DATABASE AND DATABASE MANAGEMENT SYSTEM
CHAPTER 2 DATA TYPES, DATABASE KEYS, SQL FUNCTIONS AND OPERATORS
CHAPTER 3 DATA MODELS AND MAPPING TECHNIQUES
CHAPTER 4 DISCOVERING BUSINESS RULES AND DATABASE CONSTRAINTS
CHAPTER 5 DATABASE DESIGN STEPS AND IMPLEMENTATIONS
CHAPTER 6 DATABASE NORMALIZATION AND DATABASE JOINS
CHAPTER 7 FUNCTIONAL DEPENDENCIES IN THE DATABASE MANAGEMENT SYSTEM
CHAPTER 8 DATABASE TRANSACTION, SCHEDULES, AND DEADLOCKS
CHAPTER 9 RELATIONAL ALGEBRA AND QUERY PROCESSING
CHAPTER 10 FILE STRUCTURES, INDEXING, AND HASHING
CHAPTER 11 DATABASE USERS AND DATABASE SECURITY MANAGEMENT
CHAPTER 12 BUSINESS INTELLIGENCE TERMINOLOGIES IN DATABASE SYSTEMS
CHAPTER 13 DBMS INTEGRATION WITH BPMS
CHAPTER 14 RAID STRUCTURE AND MEMORY MANAGEMENT
CHAPTER 15 ORACLE DATABASE FUNDAMENTAL AND ITS ADMINISTRATION
CHAPTER 16 DATABASE BACKUPS AND RECOVERY, LOGS MANAGEMENT
CHAPTER 17 ORACLE TECHNOLOGIES AND INSTALLATIONS
CHAPTER 18 ORACLE DATABASE APPLICATIONS DEVELOPMENT USING ORACLE APPLICATION EXPRESS
CHAPTER 19 ORACLE WEBLOGIC SERVERS AND ITS CONFIGURATIONS
CHAPTER 20 ORACLE PLSQL PROGRAMMING BASIC CONCEPTS
CHAPTER 21 GEOGRAPHICAL INFORMATION AND DATABASE SYSTEM
Database management systems handbook
dbms handbook
dbms slides handbook
dbms ppt handbook
database systems handbook
database management handbook
rdbms management handbook
rdbms systems handbook
rdbms slides handbook
Database systems slides
database management systems slides
This document outlines chapters from a book on database systems and management. It includes 21 chapters covering topics such as data types, data modeling, database design, normalization, transactions, queries, file structures, security, backup and recovery, Oracle technologies and applications development. The author dedicates the book to his reader for inspiring his work. It was completed in October 2022 with thanks to reviewers and God.
This document outlines chapters from a book on database systems and management. It includes 21 chapters covering topics such as data types, data modeling, database design, normalization, transactions, queries, file structures, security, backup and recovery, Oracle technologies and applications development. The author dedicates their efforts to inspiring readers to work more. It acknowledges numerous individuals who contributed to preparing the 4th edition of the book, which was completed on October 28, 2022.
Database management systems handbook
dbms handbook
dbms slides handbook
dbms ppt handbook
database systems handbook
database management handbook
rdbms management handbook
rdbms systems handbook
rdbms slides handbook
Database systems slides
database management systems slides
CHAPTER 1 INTRODUCTION TO DATABASE AND DATABASE MANAGEMENT SYSTEM
CHAPTER 2 DATA TYPES, DATABASE KEYS, SQL FUNCTIONS AND OPERATORS
CHAPTER 3 DATA MODELS AND MAPPING TECHNIQUES
CHAPTER 4 DISCOVERING BUSINESS RULES AND DATABASE CONSTRAINTS
CHAPTER 5 DATABASE DESIGN STEPS AND IMPLEMENTATIONS
CHAPTER 6 DATABASE NORMALIZATION AND DATABASE JOINS
CHAPTER 7 FUNCTIONAL DEPENDENCIES IN THE DATABASE MANAGEMENT SYSTEM
CHAPTER 8 DATABASE TRANSACTION, SCHEDULES, AND DEADLOCKS
CHAPTER 9 RELATIONAL ALGEBRA AND QUERY PROCESSING
CHAPTER 10 FILE STRUCTURES, INDEXING, AND HASHING
CHAPTER 11 DATABASE USERS AND DATABASE SECURITY MANAGEMENT
CHAPTER 12 BUSINESS INTELLIGENCE TERMINOLOGIES IN DATABASE SYSTEMS
CHAPTER 13 DBMS INTEGRATION WITH BPMS
CHAPTER 14 RAID STRUCTURE AND MEMORY MANAGEMENT
CHAPTER 15 ORACLE DATABASE FUNDAMENTAL AND ITS ADMINISTRATION
CHAPTER 16 DATABASE BACKUPS AND RECOVERY, LOGS MANAGEMENT
CHAPTER 17 ORACLE TECHNOLOGIES AND INSTALLATIONS
CHAPTER 18 ORACLE DATABASE APPLICATIONS DEVELOPMENT USING ORACLE APPLICATION EXPRESS
CHAPTER 19 ORACLE WEBLOGIC SERVERS AND ITS CONFIGURATIONS
CHAPTER 20 ORACLE PLSQL PROGRAMMING BASIC CONCEPTS
CHAPTER 21 GEOGRAPHICAL INFORMATION AND DATABASE SYSTEM
This document provides an overview and summary of databases. It begins with defining what a database is as a collection of related data stored in a computer system. It then discusses different database structures like tables with rows and columns. Examples of popular database software are provided like MySQL, Microsoft Access, and Oracle Database. The functions of databases are summarized as organizing, processing, retrieving and storing data in appropriate structures. Applications in various sectors like manufacturing, transportation, and IT companies are highlighted. Advantages include improved security, integration, data sharing and decision making. Limitations include costs of creation, management, hardware, upgradation and potential for failure.
Muhammad sharif Software Engineer, SKMCHRC. This book is copywrite of Muhammad Sharif
This book title: Database Systems handbook. Other Names are DBMS, RDBMS, Database slides and database management systems, relational database management systems
This is final and 4rth edition of this book.
This document is the table of contents for a book titled "Database Systems Handbook" written by Muhammad Sharif. It contains 23 chapters that cover topics related to database concepts, models, design, implementation, technologies, and programming. Some of the chapter topics include data types, data modeling, database design, normalization, transactions, query processing, indexing, security, Oracle database fundamentals, backup and recovery, and PL/SQL programming. The book is intended to provide a comprehensive guide to database systems and is acknowledged as being completed on October 28, 2022.
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Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
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- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
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- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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- Understanding the DLAU tool and how to best utilize it
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- Practical examples and best practices to implement right away
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During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
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We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
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- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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2. AbduSalam 2
INTRODUCTION TO OFFICE MANAGEMENT TOOL-II
Microsoft Access
3. Week-1
3
Introduction to Database
Who need a Database?
Data vs Information
Data Processing and their Activities
Database Terminology
Keys Used in DBMS
Database Management System
Relational Database Management System
AbduSalam
4. Week-2
4
Introduction to Database (Cont’d)
Types of Database
DBMS Applications
Database User’s
Components of DBMS
Database Models
Objectives of RDBMS
AbduSalam
5. Introduction
5
Definition
The term database means different things to different people. However,
the following definitions are used in the literature:
A database is a collection of logically related data sets or files.
Each file may contain different types of information and are used for specific
purpose.
The files may be organized in different ways to meet different processing
and retrieval requirements of the users.
A Database is an organized collection of stored data. OR
Database is a structure collection of logically related data.
OR
An organized collection of information in computerized format.
or
A Computerized representation of any organizations flow of information and
storage of data.
AbduSalam
6. Introduction (Cont’d)
6
Example
A bank may have separate files for the clients as follows:
Saving A/C, Automobile Loan, Personal Loan, Clients Information etc
The client database of bank will consist of the records from all of the above files.
The data of any client can be added, retrieved, or updated by using database
program.
The facilities of database are:
Adding new file to database
Inserting new data in existing files
Retrieving data from existing files
Updating data in existing files
Deleting data from existing files
Removing existing files from database
AbduSalam
7. Who Needs a Database?
7
Anyone who uses information to market their business.
Anyone who uses information to provide goods or services to their customers.
Anyone who wants to save time and improve the efficiency of using their
business information.
Anyone who wants to be more organized about their business information.
Anyone who wants to use information about their business more consistently.
Anyone who wishes to present their business documents or output more
professionally.
AbduSalam
8. Introduction (Cont’d)
8
Data (Cont’d)
The database is the collection of data about anything, Like cricket teams,
students and many more, anything about which you want to store data.
What we mean by data; simply the facts or figures.
Following table shows the things and the data that we may want to store
about them:
Cricket Country, name, date of birth, specialty, matches played, runs etc.
Player
Scholars Name, data of birth, age, country, field, books published etc.
Movies Name, director, language (Punjabi is default in case of Pakistan)
etc.
Food Name, ingredients, taste, preferred time, origin, etc.
AbduSalam
9. Introduction (Cont’d)
9
Data (Cont’d)
Data is a collection of facts made up of text, numbers, figures, dates and
objects. An objects can be a person, event or anything about which data is
gathered.
Data is a valuable resource to any business.
Data can be used by the managers to perform effective and successful
operations of management.
It provide a view of past activities related to the rise and fall of an
organization.
It also enable the user to make better decision for future.
Date is very useful for generating reports and graphs.
AbduSalam
10. Introduction (Cont’d)
10
Data (Cont’d)
Example
Data can be names, addresses, phone numbers, dates etc.
Mr. Khan 35000 7/18/86
Data can be processed to create useful information. Information is the
meaning given to data in the way it is interpreted:
Mr. Khan is a sales person whose annual salary is $35,000 and whose hire
date is July 18, 1986.
The manipulated and processed form of data is called information.
It is more meaningful than data.
AbduSalam
11. Introduction (Cont’d)
11
Data vs Information
Data is unprocessed raw facts about a particular entity while Information is the
processed form of data.
Data is used as input in the computer while Information is the output of computer.
Data in normally huge in the volume while Information is normally short in the
volume.
Data is the asset of organization and is not available to people for sale while
Information is normally available to people for sale.
Data is difficult or impossible to reproduce while Information is easier to
reproduce if lost.
Data is used rarely while Information is used frequently.
Data is an independent entity while information depends on data.
AbduSalam
13. Data Processing and Activities
13
The process of manipulating data to achieve the required objectives and
results is called Data Processing.
Software used to process raw data and converts raw data into meaningful
information.
Series of actions or operations are performed on data to get required
output or results.
Activities on Data Processing
Data Capturing
Data Manipulation
Managing output results
AbduSalam
14. Data Processing and Activities (Cont’d)
14
Data Capturing
Process of recording the data in some form is called Data Capturing.
Data may recorded from source documents. Or
Data can also be given directly to the computer through input devices.
Data Manipulation
Process of applying different operations on data is called Data manipulation.
The following operation can be performed on data:
Classifying
Process of organizing data into classes or groups is called classifying.
Example: Data in college can be classified in two groups: Student & Teacher
Calculation
Process of applying arithmetic operations on data is called Calculation.
AbduSalam
15. Data Processing and Activities (Cont’d)
15
Sorting
Process of arranging data in logical sequence is called sorting
Summarizing
Process of reducing a large amount of data in a more concise and usable form is
called summarizing.
Managing output results
The following activities can be performed on data after the data has been captured
and manipulated.
Storage
Process of retaining data for future use is called data storage.
Different storage medium used to store data.
AbduSalam
16. Data Processing and Activities (Cont’d)
16
Retrieval
Process of accessing or fetching the store data is called data retrieval.
Data can be retrieved when required.
Communication
Process of transferring data from source to destination for further processing is
called communication.
Reproduction
Process of copying or duplicating data is called reproduction of data.
Data can be reproduced if different users need data at different locations.
AbduSalam
17. Database Terminology
17
Database File
The main file that include the entire database and that is saved to your hard-drive or
floppy disk. For example StudentDatabase.mdb in access 2003 and .accdb in access
2007 & 2010
Also called Master files or Latest updated files
These files are updated when any change in their are required.
Transaction File
It is used to store input data before processing.
It may be temporary file or may exist until the master file is updated.
It may also be used to maintain a permanent record of the data about a transaction.
AbduSalam
18. Database Terminology (Cont’d)
18
Backup File
It is used to take backup of important data.
It is permanent file.
It is used to store an additional copy of data.
Data can be recovered from this file if the original file is lost or damaged.
Backup files are mostly created by using specific software utilities.
Entity
Real world things (entities) you need to store information about.
For example Employees, Products, Customers, Orders.
Entities are represented by tables in the database
Entities are represented by rectangles
AbduSalam
19. Database Terminology (Cont’d)
19
Table
A two dimensional array of data that contains descriptive information about
an entity is known as table or relation.
A table is a collection of data about a specific topic, such as students or
contacts, Customers, Orders, or products.
Field
Facts (attributes) you need to know about each entity, e.g. an Employee’s
date of birth, salary.
Attributes are represented by fields in the tables
A field is a single characteristic of a person, place, object, event or idea.
Attributes are represented by oval.
AbduSalam
20. Database Terminology (Cont’d)
20
Record
A set of related field values. e.g. An employee record includes a set of
fields about the employee such as Employee no, name etc.
Degree
Degree is the number of attributes in a relation.
Cardinality
Cardinality is the number of tuples in a relation.
AbduSalam
21. Example
Relationship
A logical connection between different entities is called relationship
Fields
Records
Student ID Student Name Phone Department
101 Khan 392-3900 Pharmacy
102 Jawad 392-5555 Statistics
103 Imran 846-5656 Economics
Course Code Course Name Student ID
1001 Botany 101
1002 SPSS 102
AbduSalam 21
22. Primary Key & Foreign Key
22
Primary Key
To ensure that each record is unique in each table, we can set one field to
be a Primary Key field.
A Primary Key is a field that will contain no duplicates and no blank
values. Or
A Primary key is a field, or a collection of fields, whose values uniquely
identify each record
Foreign Key
When the primary key is included in a second table, it’s called as a Foreign
key
Foreign Keys link to data in other tables
AbduSalam
23. Candidate/Alternate, Composite & Secondary Key
23
Candidate/Alternate Key
Field or combination of fields that are not used as a primary key.
Users can also access data by using an alternate key.
e.g.
If student relation contain a Roll No, it can be used as primary key because one Roll
No can be assigned to only one student.
Suppose the relation contains another field Registration No that has been used as
primary key. In this situation, Roll No becomes an alternate key.
Composite Key
Primary key that consist of two or more attributes
e.g.
Relation uses two fields Registration No and Subject to identify each tuple, then it is
called Composite key.
Secondary Key
A field or combination of fields that is basis for retrieval is known as secondary key.
Secondary is a non-unique field. One secondary key may refers to many records.
AbduSalam
27. Database Management System
27
A DBMS is a software tool that allows multiple users to store, access, and
process data into useful information.
Used for Organizing, Storing, Maintaining, Retrieving, and Sorting data.
A RDBMS is a collection of tables that are related to one another based
on a common field.
A relational database uses multiple tables
Example: Microsoft Access, dBASE, FOXPRO, Oracle.
To manage databases, companies purchase programs called RDBMS
Student Information System
Inventory System
AbduSalam 27
28. Relational Database Management System (RDBMS)
28
Relationships between the entities in the database; i.e. what attributes do
they have in common.
Relationships are formed in the database between entities that have
common attributes. They have common fields in the related tables.
For example, customer ‘Hassan’ can place an order for product ‘Laptop’.
So the Orders table has relationships with Customers table & Products
table
DON’T DUPLICATE DATA
That is, once relationships are created, tables can “talk” to each other. We
can link (relate) the tables to find:
AbduSalam 28
30. DBMS Applications (Cont’d)
30
The purpose of a relational database management system is to transform
Data Information Knowledge Action
Data driven decision making
AbduSalam 30
31. Types of Database
31
Two types of databases are Centralized & Distributed Database
Centralized Database
A logically interrelated collection of shared data, physically located on a
central computer and the user access this data base through their
terminals.
All the processing is performed on that central computer.
It provide greater control over accessing and updating data than
distributed databases, however,
Centralized databases are more vulnerable to failure since they depend
on resources at a central location.
Examples
Personal Computer Databases- those used in small businesses (Accounting,
Inventory)
AbduSalam
32. Types of Database (Cont’d)
32
Central Computer Databases- Usually involve very large, integrated
databases accessible to a large number of users (Airline reservation system,
Financial Institutions, etc.)
Client/Server Database- Designed for distribution of work on a computer
network in which many clients share services of a single server.
AbduSalam
33. Types of Database (Cont’d)
33
Distributed Database
A logically interrelated collection of shared data, physically distributed
over a computer network.
A distributed database (DDB) is a collection of multiple, logically
interrelated databases distributed over a computer network.
The database must have a single logical data model.
e.g. Banking and insurance applications
DDB increase reliability and availability
When centralized database fails, the database is unavailable to all users. A
distributed system will continue to function at some reduced level even when a
component fails.
AbduSalam
35. Database User
35
Users of Database Systems:
Application Programmers
End Users
Naïve User
Sophisticated User
Database Administrator
Application programmers:
who create different types of database application programs
Application programmers design the application according to the needs
Application programmers are skilled people who have clear idea of the
structure of the database and know clearly about the needs of the organizations.
End Users:
Group of users contains the people who use the database application
programs developed by the Application programmers. This category further
contains three types of users AbduSalam
36. Database User (Cont’d)
36
This category further contains three types of users
Naïve Users
Sophisticated Users
Database Administrator
Naïve Users
Simply use the application database programs created by the programmers.
Has no interaction with other parts of there database and only use the programs
meant for them.
They have not to worry about the further working of the database.
Sophisticated Users:
Have some additional rights over the Naïve users, which means that they can
access the data stored in the database any of their desired way.
Can access data using the application programs as well as other ways of
accessing data.
AbduSalam
37. Database User (Cont’d)
37
Database Administrators (DBA):
The most technical class of db users.
Have the knowledge of how to design and manage the database use as well as
to manage the data in the database.
DBA is a very responsible position in an organization for development of any
database system.
Responsible for design, proper working, implementation, operation of the
database and RDBMS, has the responsibility of making proper database
backups and make necessary actions for recovering the database in case of a
database crash.
To fulfill the requirements of a DBA position a DBA needs vast experience
and very elegant technical skills.
AbduSalam
38. Database User (Cont’d)
38
Database Administrator takes over the charge and performs specific DBA related
activities including:
Installation of software
Database maintenance
Database Backup
Grant of rights to database users
Responsible for grant of access rights to the database users.
Granting and revoking (taking back) the rights
Monitoring of Running Jobs
When a new database is created it takes a limited space but as a result of
daily activity the database acquires more data and grows in size very
rapidly.
The DBA has to monitor the disk space usage and statistics to ensure that no
data over flow occurs at any stage.
Managing Print jobs
Restoring the system AbduSalam
39. Components of Database System
39
The four major components of Database System are:
Data
Data is the most important component of database system.
Data covers the collection of facts stored in the database.
Main purpose of database system to store, maintain and process data for the user.
Hardware
Physical components of a computer system are known as hardware.
Hardware are used to perform different tasks as input, output, storage, and
processing.
Software
Collection of programs used by computer within a database system.
Most important software is DBMS itself.
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40. Components of Database System (Cont’d)
40
Three types of software to enable the database system function fully.
Operating System software
It manages all the hardware components and enable other software to run on the
computer.
RDBMS software
It manages the database in the database system
Application Programs and Utilities
Used to access and manipulate the data stored in the database
Personnel
People related to database system are called Personnel
e.g. DBA, Programmer, & End User
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41. Database Models
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Set of rules and standards that define how the database organizes data is
called Database Model.
Three types of logical database models
Hierarchical Model
It arranges records in hierarchy like an organizational chart.
Each record type in this model is called a node or segment.
Node on the chart represents a particular entity.
Each node is a subordinate of the node that is the next highest level.
This kind of structure is often called inverted tree.
The top-most node is called Root.
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43. Database Models (Cont’d)
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Network Model
Each record can have multiple parents
Network model is similar to hierarchical model.
Major difference is that the subordinate entity may participate in as many subordinate
relationships as required.
Subordinating entities are represented by arrows in the network model.
Require more complex program to represent a database. It also provide more
flexibility than hierarchical model.
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45. Database Models (Cont’d)
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Relation Model
Most commonly used database model and more flexible than others
Consists of simple relations and these relation represent a particular entity.
Relation are used to hold information about the entity to be represented in the
database.
Relations are also called Tables
Tables are a series of row/column intersections, Tables related by sharing common
entity characteristic(s)
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47. Database Models (Cont’d)
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Represented in an entity relationship diagram (ERD)
Based on entities, attributes, and relationships
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48. RDBMS Objectives
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RDBMS have some important objectives:
Database Security
Protection from malicious attempts to steal or modify data.
Database security means to protect the data from unauthorized (access) users,
which can modify, update, destroy or delete the data is known as Database
security.
Thus data base is always under a responsible person called DBA.
Authentication and authorization mechanisms to allow specific users access only to
required data.
Share ability
An ability to share data resources
Share ability means that the actual data must be shared among different people
and different processes at the same time.
This capability allows user to store data at a central place.
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49. DBMS Objectives (Cont’d)
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Availability
It means that the users must be able to access data easily.
The data should be available when and where it is required.
Integrity
Protecting the existing database, maintaining the quality of database and
ensuring the privacy of database.
The Integrity of a DB is concerned with its consistency, correctness, validity and
accuracy.
Database integrity refers to the validity and consistency of stored data.
Integrity is usually expressed in terms of constraints, which are consistency rules
that database is not permitted to violate.
Enforcing integrity constraints generally requires access to a large amount of
data that defines the constraints but which is not involved in the actual update
operation itself.
In a distributed DBMS, the communication and processing costs that are required
to enforce integrity constraints may be prohibitive.
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50. Types of Integrity
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Relational Integrity
CREATE Table Student
The primary key can neither be null nor duplicate.
(Std-ID INTEGER NOT NULL)
Referential Integrity
Thus referential integrity means that, if the foreign key contains a value, that
value must refer to an existing, valid row in the parent table.
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51. 51
Think about the data that you may want to store about
different things around you
List the changes that may arise during the working of any
system,
lets say
Railway Reservation System
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A database is a shared collection of logically related data, designed to meet the information needs of multiple users in an organization.
Data that describe the properties and context of user data.
A relational database is a collection of tables from which data can be accessed in many different ways without having to reorganize the database tables.
A relational database is a collection of tables from which data can be accessed in many different ways without having to reorganize the database tables.
A relational database is a collection of tables from which data can be accessed in many different ways without having to reorganize the database tables.
A relational database is a collection of tables from which data can be accessed in many different ways without having to reorganize the database tables.
Collection of logical constructs used to represent data structure and relationships within the database
Perceived by user as a collection of tables for data storage, Tables are a series of row/column intersections, Tables related by sharing common entity characteristic(s)
Collection of logical constructs used to represent data structure and relationships within the database
Think about the data that you may want to store about different things around you List the changes that may arise during the working of any system, lets say Railway Reservation System
Think about the data that you may want to store about different things around you List the changes that may arise during the working of any system, lets say Railway Reservation System