This document outlines the key topics covered in distributed database management systems (DDBMS). It introduces DDBMS and discusses their advantages over centralized systems, including improved performance, reliability, availability, and scalability. The document also summarizes major challenges in DDBMS, such as distributed database design, query processing, concurrency control, and reliability.
Distributed database management systems (DDBMS) allow data to be spread across multiple computer sites connected by a network. A DDBMS provides location transparency so users can access data without knowing its physical location. It also coordinates transactions that involve data stored at multiple sites. DDBMS architectures include transaction managers, data managers, and transaction coordinators to process transactions and subtransactions across distributed data.
The document discusses different database system architectures including centralized, client-server, server-based transaction processing, data servers, parallel, and distributed systems. It covers key aspects of each architecture such as hardware components, process structure, advantages and limitations. The main types are centralized systems with one computer, client-server with backend database servers and frontend tools, parallel systems using multiple processors for improved performance, and distributed systems with data and users spread across a network.
A centralized database stores all data in a single location, typically on a central server, making it easy to get a complete view of data but slowing down with many users accessing the same file. A distributed database splits data across multiple physical locations and processing across nodes, avoiding bottlenecks while requiring synchronization and replication across locations.
Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
Distributed database management systems (DDBMS) allow data to be spread across multiple computer sites connected by a network. A DDBMS provides location transparency so users can access data without knowing its physical location. It also coordinates transactions that involve data stored at multiple sites. DDBMS architectures include transaction managers, data managers, and transaction coordinators to process transactions and subtransactions across distributed data.
The document discusses different database system architectures including centralized, client-server, server-based transaction processing, data servers, parallel, and distributed systems. It covers key aspects of each architecture such as hardware components, process structure, advantages and limitations. The main types are centralized systems with one computer, client-server with backend database servers and frontend tools, parallel systems using multiple processors for improved performance, and distributed systems with data and users spread across a network.
A centralized database stores all data in a single location, typically on a central server, making it easy to get a complete view of data but slowing down with many users accessing the same file. A distributed database splits data across multiple physical locations and processing across nodes, avoiding bottlenecks while requiring synchronization and replication across locations.
Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
A distributed database system is a database in which portions of the database are stored on multiple computers within a network. This provides advantages like reliability if one site crashes, and speed since information is distributed rather than centralized. However, proper hardware and software is needed to connect the distributed sites, and there may be connection errors that impact users.
The document discusses distributed database management systems (DDBMS). It describes how DDBMS evolved from centralized systems to address needs for decentralized management, improved performance and reliability. Key components of a DDBMS include transaction processors, data processors and protocols to coordinate communication and ensure consistency. The document also covers different levels of data and process distribution, and techniques for providing transparency in distributed databases.
● Distributed Database Management Systems Advantages and Disadvantages.
● Characteristics of Distributed Database Management Systems.
● Levels of Data and Process Distribution.
● Distributed Database Transparency Features.
● Transaction Performance and Failure Transparency.
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
A database administrator (DBA) is responsible for the design, operation, and management of an organization's database. A DBA requires technical skills to understand hardware and software issues, management skills to plan and coordinate tasks, and diplomatic skills to communicate with users and determine data needs. Key responsibilities of a DBA include planning the database, developing and maintaining the operational database, and ensuring optimal database performance.
The document defines distributed and parallel systems. A distributed system consists of independent computers that communicate over a network to collaborate on tasks. It has features like no common clock and increased reliability. Examples include telephone networks and the internet. Advantages are information sharing and scalability, while disadvantages include difficulty developing software and security issues. A parallel system uses multiple processors with shared memory to solve problems. Examples are supercomputers and server clusters. Advantages are concurrency and saving time, while the main disadvantage is lack of scalability between memory and CPUs.
Transaction servers are used in relational database systems and have multiple server processes that receive queries, execute transactions, and return results. The server processes operate on shared memory and data is stored in a buffer pool. Data servers are used in object-oriented database systems and ship data and processing to powerful client systems to perform computations and return results to the centralized server.
Data cube computation involves precomputing aggregations to enable fast query performance. There are different materialization strategies like full cubes, iceberg cubes, and shell cubes. Full cubes precompute all aggregations but require significant storage, while iceberg cubes only store aggregations that meet a threshold. Computation strategies include sorting and grouping to aggregate similar values, caching intermediate results, and aggregating from smallest child cuboids first. The Apriori pruning method can efficiently compute iceberg cubes by avoiding computing descendants of cells that do not meet the minimum support threshold.
This document discusses distributed database management systems (DDBMS). It outlines the evolution of DDBMS from centralized systems to today's distributed systems over the internet. It describes the advantages and disadvantages of DDBMS, components of DDBMS including transaction processors and data processors, and levels of data and process distribution including single-site, multiple-site, and fully distributed systems. It also discusses concepts like distribution transparency, transaction transparency, and distributed concurrency control in DDBMS.
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
The document provides information about what a data warehouse is and why it is important. A data warehouse is a relational database designed for querying and analysis that contains historical data from transaction systems and other sources. It allows organizations to access, analyze, and report on integrated information to support business processes and decisions.
This document defines a data warehouse as a collection of corporate information derived from operational systems and external sources to support business decisions rather than operations. It discusses the purpose of data warehousing to realize the value of data and make better decisions. Key components like staging areas, data marts, and operational data stores are described. The document also outlines evolution of data warehouse architectures and best practices for implementation.
This document discusses database fragmentation in distributed database management systems (DDBMS). Database fragmentation allows a single database object to be broken into multiple segments that can be stored across different sites on a network. This improves efficiency, security, parallelism, availability, reliability and performance. There are three main types of fragmentation: horizontal, vertical, and mixed. Horizontal fragmentation breaks data by attributes like location, vertical by attributes like departments, and mixed uses both. While fragmentation provides advantages, it also increases complexity, cost, and makes security and integrity control more difficult.
This document discusses distributed databases and distributed database management systems (DDBMS). It defines a distributed database as a logically interrelated collection of shared data physically distributed over a computer network. A DDBMS is software that manages the distributed database and makes the distribution transparent to users. The document outlines key concepts of distributed databases including data fragmentation, allocation, and replication across multiple database sites connected by a network. It also discusses reference architectures, components, design considerations, and types of transparency provided by DDBMS.
The document discusses naming in distributed systems. It covers desirable features of naming systems like location transparency and location independence. It differentiates between human-oriented and system-oriented names. It also discusses name spaces, name servers, name resolution including recursive and iterative approaches, and name caching.
The document provides an overview of database systems, including their purpose, components, and architecture. It describes how database systems offer solutions to problems with using file systems to store data by providing data independence, concurrency control, recovery from failures, and more. It also defines key concepts like data models, data definition and manipulation languages, transactions, storage management, database users, administrators, and the roles they play in overall database system structure.
The key characteristics of the database approach include: self-describing metadata that defines the database structure; insulation between programs and data through program-data and program-operation independence; data abstraction through conceptual data representation; support for multiple views of the data; and sharing of data through multiuser transaction processing that allows concurrent access while maintaining isolation and atomicity.
This document discusses different data models used in database management systems including record-based, relational, network, hierarchical, and entity-relationship models. It provides details on each model such as how data is organized. A record-based model uses fixed-length records and fields. The relational model organizes data into tables with rows and columns. The network model links entities through multiple paths in a graph structure. The hierarchical model arranges data in a tree structure. Finally, the entity-relationship model views the real world as entities and relationships between entities.
The document outlines the key topics covered in a book on distributed database management systems (DDBMS). It begins with an introduction to distributed databases and DDBMS architectures. Some of the major topics covered in the book include distributed database design, query processing, transaction management, data replication, and parallel databases. The document also discusses challenges of distributed systems like transparency, reliability, performance, and system expansion. It explains concepts like data independence, network transparency, and how distributed transactions provide failure atomicity and concurrency control.
A distributed database system is a database in which portions of the database are stored on multiple computers within a network. This provides advantages like reliability if one site crashes, and speed since information is distributed rather than centralized. However, proper hardware and software is needed to connect the distributed sites, and there may be connection errors that impact users.
The document discusses distributed database management systems (DDBMS). It describes how DDBMS evolved from centralized systems to address needs for decentralized management, improved performance and reliability. Key components of a DDBMS include transaction processors, data processors and protocols to coordinate communication and ensure consistency. The document also covers different levels of data and process distribution, and techniques for providing transparency in distributed databases.
● Distributed Database Management Systems Advantages and Disadvantages.
● Characteristics of Distributed Database Management Systems.
● Levels of Data and Process Distribution.
● Distributed Database Transparency Features.
● Transaction Performance and Failure Transparency.
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
A database administrator (DBA) is responsible for the design, operation, and management of an organization's database. A DBA requires technical skills to understand hardware and software issues, management skills to plan and coordinate tasks, and diplomatic skills to communicate with users and determine data needs. Key responsibilities of a DBA include planning the database, developing and maintaining the operational database, and ensuring optimal database performance.
The document defines distributed and parallel systems. A distributed system consists of independent computers that communicate over a network to collaborate on tasks. It has features like no common clock and increased reliability. Examples include telephone networks and the internet. Advantages are information sharing and scalability, while disadvantages include difficulty developing software and security issues. A parallel system uses multiple processors with shared memory to solve problems. Examples are supercomputers and server clusters. Advantages are concurrency and saving time, while the main disadvantage is lack of scalability between memory and CPUs.
Transaction servers are used in relational database systems and have multiple server processes that receive queries, execute transactions, and return results. The server processes operate on shared memory and data is stored in a buffer pool. Data servers are used in object-oriented database systems and ship data and processing to powerful client systems to perform computations and return results to the centralized server.
Data cube computation involves precomputing aggregations to enable fast query performance. There are different materialization strategies like full cubes, iceberg cubes, and shell cubes. Full cubes precompute all aggregations but require significant storage, while iceberg cubes only store aggregations that meet a threshold. Computation strategies include sorting and grouping to aggregate similar values, caching intermediate results, and aggregating from smallest child cuboids first. The Apriori pruning method can efficiently compute iceberg cubes by avoiding computing descendants of cells that do not meet the minimum support threshold.
This document discusses distributed database management systems (DDBMS). It outlines the evolution of DDBMS from centralized systems to today's distributed systems over the internet. It describes the advantages and disadvantages of DDBMS, components of DDBMS including transaction processors and data processors, and levels of data and process distribution including single-site, multiple-site, and fully distributed systems. It also discusses concepts like distribution transparency, transaction transparency, and distributed concurrency control in DDBMS.
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
The document provides information about what a data warehouse is and why it is important. A data warehouse is a relational database designed for querying and analysis that contains historical data from transaction systems and other sources. It allows organizations to access, analyze, and report on integrated information to support business processes and decisions.
This document defines a data warehouse as a collection of corporate information derived from operational systems and external sources to support business decisions rather than operations. It discusses the purpose of data warehousing to realize the value of data and make better decisions. Key components like staging areas, data marts, and operational data stores are described. The document also outlines evolution of data warehouse architectures and best practices for implementation.
This document discusses database fragmentation in distributed database management systems (DDBMS). Database fragmentation allows a single database object to be broken into multiple segments that can be stored across different sites on a network. This improves efficiency, security, parallelism, availability, reliability and performance. There are three main types of fragmentation: horizontal, vertical, and mixed. Horizontal fragmentation breaks data by attributes like location, vertical by attributes like departments, and mixed uses both. While fragmentation provides advantages, it also increases complexity, cost, and makes security and integrity control more difficult.
This document discusses distributed databases and distributed database management systems (DDBMS). It defines a distributed database as a logically interrelated collection of shared data physically distributed over a computer network. A DDBMS is software that manages the distributed database and makes the distribution transparent to users. The document outlines key concepts of distributed databases including data fragmentation, allocation, and replication across multiple database sites connected by a network. It also discusses reference architectures, components, design considerations, and types of transparency provided by DDBMS.
The document discusses naming in distributed systems. It covers desirable features of naming systems like location transparency and location independence. It differentiates between human-oriented and system-oriented names. It also discusses name spaces, name servers, name resolution including recursive and iterative approaches, and name caching.
The document provides an overview of database systems, including their purpose, components, and architecture. It describes how database systems offer solutions to problems with using file systems to store data by providing data independence, concurrency control, recovery from failures, and more. It also defines key concepts like data models, data definition and manipulation languages, transactions, storage management, database users, administrators, and the roles they play in overall database system structure.
The key characteristics of the database approach include: self-describing metadata that defines the database structure; insulation between programs and data through program-data and program-operation independence; data abstraction through conceptual data representation; support for multiple views of the data; and sharing of data through multiuser transaction processing that allows concurrent access while maintaining isolation and atomicity.
This document discusses different data models used in database management systems including record-based, relational, network, hierarchical, and entity-relationship models. It provides details on each model such as how data is organized. A record-based model uses fixed-length records and fields. The relational model organizes data into tables with rows and columns. The network model links entities through multiple paths in a graph structure. The hierarchical model arranges data in a tree structure. Finally, the entity-relationship model views the real world as entities and relationships between entities.
The document outlines the key topics covered in a book on distributed database management systems (DDBMS). It begins with an introduction to distributed databases and DDBMS architectures. Some of the major topics covered in the book include distributed database design, query processing, transaction management, data replication, and parallel databases. The document also discusses challenges of distributed systems like transparency, reliability, performance, and system expansion. It explains concepts like data independence, network transparency, and how distributed transactions provide failure atomicity and concurrency control.
The document outlines the key topics covered in a textbook on distributed database management systems (DDBMS). It discusses what constitutes a distributed DBMS, highlights the promises of distributed DBMS like transparency and improved performance/reliability. It also examines some of the main challenges in distributed DBMS like distributed query processing, concurrency control, and reliability. Finally, it discusses the relationship between the different issues in distributed DBMS design.
The document provides an overview of distributed database management systems (DDBMS). It discusses key concepts such as distributed DBMS architecture, distributed database design, distributed query processing, distributed concurrency control, and distributed reliability protocols. The document outlines different implementation alternatives for distributed DBMS, including client-server, peer-to-peer, and multi-DBMS architectures. It also covers issues in distributed database design like fragmentation and data placement across multiple sites in a network.
The document outlines the key concepts of distributed database management systems (DDBMS). It defines a DDBMS as a collection of logically interrelated databases distributed over a computer network and managed by a software system that makes the distribution transparent to users. The document discusses the motivation for DDBMS, their promises around transparent access and improved reliability, performance and scalability. It also covers important concepts like data distribution, query processing, concurrency control and transaction management that DDBMS must address.
This document discusses parallel database management systems (DBMS) and distributed DBMS. It covers topics like the database problem of handling large volumes of data, solutions like data partitioning and parallel data access. It describes objectives of parallel DBMS like high performance and availability. It discusses hardware architectures for parallel DBMS like shared memory, shared disk and shared nothing. It also covers techniques for parallel DBMS including data placement, parallel query processing and optimization, and challenges of load balancing.
This document outlines the course content and schedule for an advanced database systems course. It includes 14 lectures covering topics like distributed database design, query processing, transaction management, and parallel database systems. Assessment will be based on attendance, exercises, class tests, and a final exam. The course is taught on Fridays from 2:30-4:30 PM, with the schedule subject to change. Tutorial sessions will be held on selected dates to provide additional support.
1. The document discusses distributed databases, which involve spreading a single logical database across multiple physical locations connected by a network.
2. Key aspects covered include definitions of distributed and decentralized databases, reasons for using distributed databases, options for distributing data including homogeneous/heterogeneous systems and data replication/partitioning approaches.
3. The document also outlines objectives of distributed databases like location and failure transparency and challenges related to maintaining data integrity and performance across distributed systems.
The document outlines the key topics covered in a book on distributed database management systems (DDBMS). It discusses distributed DBMS architecture and how data is distributed across multiple sites connected by a network. It also covers important DDBMS concepts like distributed database design, distributed query processing, concurrency control, and reliability. The relationships between these distributed database issues are interdependent. The goal of a DDBMS is to provide transparent access to data located across a computer network.
The document discusses database management systems (DBMS) and their components and functions. A DBMS is software that allows for the creation, management and use of databases. It provides functions like data storage, retrieval, updating, transaction processing and security. The DBMS ensures data consistency and sharing between users. Common DBMS architectures include two-tier client-server and three-tier architectures with user interface, business logic and data layers.
The document discusses database management systems (DBMS) and their components and functions. A DBMS is software that allows for the creation, management and use of databases. It provides functions like data storage, retrieval and updating, transaction processing, concurrency control and security. The DBMS architecture can be either two-tier client-server or three-tier with separate layers for the user interface, business logic and data storage. Key advantages of a DBMS include data consistency, redundancy control, sharing of data across users and applications, and improved data integrity and maintenance.
The document discusses database management systems (DBMS) and their components and functions. A DBMS is software that allows for the creation, management and use of databases. It provides functions like data storage, retrieval and updating, transaction processing, concurrency control and security. The DBMS architecture can be either two-tier client-server or three-tier with separate layers for the user interface, business logic and data storage. Key advantages of a DBMS include data consistency, redundancy control, sharing of data across users and applications, and improved data integrity and maintenance.
The document discusses database management systems (DBMS) and their components and functions. A DBMS is software that allows for the creation, management and use of databases. It provides functions like data storage, retrieval, updating, transaction processing and security. The DBMS ensures data consistency and sharing between users. Common DBMS architectures include two-tier client-server and three-tier architectures with user interface, business logic and data layers.
The document discusses database management systems (DBMS) and their components and functions. A DBMS is software that allows for the creation, management and use of databases. It provides functions like data storage, retrieval, updating, transaction processing and security. The DBMS ensures data consistency and sharing between users. It also provides data independence to allow applications to work even if the database structure changes.
The document discusses database management systems (DBMS) which allow multiple users to access and update shared data simultaneously. It provides examples of DBMS use for tasks like shopping, payments, travel details, and online bookstores. The document then covers DBMS components, architectures, functions, advantages, and the typical development cycle.
The document discusses database management systems (DBMS) and their components and functions. A DBMS is software that allows for the creation, management and use of databases. It provides functions like data storage, retrieval, updating, transaction processing and security. The DBMS ensures data consistency and integrity even when accessed by multiple users simultaneously. It also provides data independence so that applications continue to work even if the database structure changes.
A distributed database management system (DDBMS) governs the storage and processing of logically related data over interconnected computer systems where both data and processing are distributed among several sites. A DDBMS has functions like application interfaces, validation, transformation, query optimization, mapping, security, backup/recovery, concurrency control, and transaction management to ensure data consistency across database fragments. Components of a DDBMS include workstations or remote devices that form the network, network components in each device, communications media to transfer data, transaction processors at each device, and data processors at each site to store and retrieve local data.
This document provides information about database management systems (DBMS). It defines a DBMS as software that interacts with users and applications to capture and analyze data from a database. It then discusses different types of databases like centralized, operational, end-user, personal, distributed, and commercial databases. Finally, it provides examples of specific DBMS like Access, Visual FoxPro, MySQL, SQL Server, and Oracle.
The document discusses security issues in distributed database systems. It begins by defining distributed databases and their architecture. It then discusses three main security aspects: access control, authentication, and encryption. The document also discusses distributed database system design considerations like concurrency control and data fragmentation. Emerging security tools for distributed databases mentioned include data warehousing, data mining, collaborative computing, distributed object systems, and web applications. Maintaining security when building and querying data warehouses from multiple sources is highlighted as a key challenge.
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