The document discusses distributed database management systems (DBMS). It covers distributed DBMS architecture and design challenges like data fragmentation, distribution, and allocation. Key points discussed include different levels of data sharing and knowledge; static vs dynamic access patterns; top-down vs bottom-up design approaches; and horizontal and vertical fragmentation with examples. Factors to consider for distribution design such as why and how to fragment data, the degree of fragmentation, and information requirements are also summarized.
The document discusses the design problem in distributed database management systems (DBMS) and outlines various approaches to distributed DBMS architecture and design. It covers topics such as distributed DBMS software placement, data fragmentation, allocation of fragments to sites, and information requirements for fragmentation design. Primary horizontal fragmentation is described as a method to partition relations based on minterm predicates over simple attribute predicates to improve data locality.
The document discusses primary horizontal fragmentation (PHF) for distributing data in a distributed database management system (DBMS). PHF involves fragmenting a relation based on minterm predicates, which are combinations of simple predicates over the relation's attributes. The key steps are determining a complete and minimal set of simple predicates, generating minterm predicates from them, and allocating fragments according to the minterm predicates. An example horizontally fragments a PAY relation into two fragments based on an employee's salary being less than or greater than $30,000.
Intro to Distributed Database Management SystemAli Raza
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.
This is the keynote talk i delivered at GeekCamp.SG 2014
The main purpose of the talk is to create an awareness, if not existent, in the community when it comes to choosing and wanting to building a distributed system.
This presentation is not meant to be a survey of distributed computing through the ages but hopefully it serves as a good starting point in which the journeyman can start from.
I want to thank Jonas, CTO of Typesafe, as his work in Akka strongly influenced my own and i hope it would help you in the way his work helped me.
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.
This document discusses parallel databases and different types of parallelism that can be utilized in databases including I/O parallelism, interquery parallelism, intraquery parallelism, and interoperation parallelism. It provides details on how relations can be partitioned across multiple disks for I/O parallelism using techniques like round robin, hash partitioning, and range partitioning. It also compares these partitioning techniques in terms of supporting full relation scans, point queries, and range queries.
Distribution transparency and Distributed transactionshraddha mane
Distribution transparency and Distributed transaction.deadlock detection .Distributed transaction and their types and threads and processes and their difference.
The document discusses the design problem in distributed database management systems (DBMS) and outlines various approaches to distributed DBMS architecture and design. It covers topics such as distributed DBMS software placement, data fragmentation, allocation of fragments to sites, and information requirements for fragmentation design. Primary horizontal fragmentation is described as a method to partition relations based on minterm predicates over simple attribute predicates to improve data locality.
The document discusses primary horizontal fragmentation (PHF) for distributing data in a distributed database management system (DBMS). PHF involves fragmenting a relation based on minterm predicates, which are combinations of simple predicates over the relation's attributes. The key steps are determining a complete and minimal set of simple predicates, generating minterm predicates from them, and allocating fragments according to the minterm predicates. An example horizontally fragments a PAY relation into two fragments based on an employee's salary being less than or greater than $30,000.
Intro to Distributed Database Management SystemAli Raza
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.
This is the keynote talk i delivered at GeekCamp.SG 2014
The main purpose of the talk is to create an awareness, if not existent, in the community when it comes to choosing and wanting to building a distributed system.
This presentation is not meant to be a survey of distributed computing through the ages but hopefully it serves as a good starting point in which the journeyman can start from.
I want to thank Jonas, CTO of Typesafe, as his work in Akka strongly influenced my own and i hope it would help you in the way his work helped me.
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.
This document discusses parallel databases and different types of parallelism that can be utilized in databases including I/O parallelism, interquery parallelism, intraquery parallelism, and interoperation parallelism. It provides details on how relations can be partitioned across multiple disks for I/O parallelism using techniques like round robin, hash partitioning, and range partitioning. It also compares these partitioning techniques in terms of supporting full relation scans, point queries, and range queries.
Distribution transparency and Distributed transactionshraddha mane
Distribution transparency and Distributed transaction.deadlock detection .Distributed transaction and their types and threads and processes and their difference.
1) The document discusses quality of service (QoS)-aware data replication for data-intensive applications in cloud computing systems. It aims to minimize data replication cost and number of QoS violated replicas.
2) It presents a mathematical model and algorithm to optimally place QoS-satisfied and QoS-violated data replicas. The algorithm uses minimum-cost maximum flow to obtain the optimal placement.
3) The algorithm takes as input a set of requested nodes and outputs the optimal placement for QoS-satisfied and QoS-violated replicas by modeling the problem as a network flow graph and applying existing polynomial-time algorithms.
Distributed data base management systemSonu Mamman
The document discusses different types of distributed database systems. A homogeneous distributed database has identical software, hardware, operating systems, and data structures at each site. A heterogeneous distributed database allows different sites to use varying software, hardware, operating systems, and data structures. The client-server model separates database applications and data with clients making requests to servers housing the data.
The document discusses different types of parallelism that can be utilized in parallel database systems: I/O parallelism to retrieve relations from multiple disks in parallel, interquery parallelism to run different queries simultaneously, intraquery parallelism to parallelize operations within a single query, and intraoperation parallelism to parallelize individual operations like sort and join. It also covers techniques for partitioning relations across disks and handling skew to balance the workload.
This document discusses centralized and distributed messaging systems. A centralized system hosts all server resources in a central data center, allowing for easier management and security. A distributed system places servers locally at branch offices due to unreliable network connections between sites. Consider centralizing for cost savings if network prerequisites are met, and plan contingencies for potential single points of failure. Distributed systems are suitable when network conditions cannot support traffic to a central hub.
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.
Distributed computing system is a collection of interconnected computers that appear as a single system. There are two types of computer architectures for distributed systems - tightly coupled and loosely coupled. In tightly coupled systems, processors share a single memory while in loosely coupled systems, processors have their own local memory and communicate through message passing. Distributed systems provide advantages like better price-performance ratio, resource sharing, reliability, and scalability but also introduce challenges around transparency, communication, performance, heterogeneity, and fault tolerance.
The document discusses various topics related to distributed database management systems (DDBMS), including:
1) Transparency in DDBMS, which aims to hide the complexities of distribution from users. There are four main categories: distribution, transaction, performance, and DBMS transparency.
2) Distributed transaction management, which must ensure the ACID properties of transactions that access data across multiple sites. Objectives include improving utilization, minimizing response time and communication cost.
3) Concurrency control in DDBMS, which coordinates concurrent access to distributed data. Locking-based protocols are commonly used, where transactions acquire read or write locks before accessing data items. The two-phase locking protocol is also discussed.
This document provides an overview of distributed operating systems. It discusses the motivation for distributed systems including resource sharing, reliability, and computation speedup. It describes different types of distributed operating systems like network operating systems where users are aware of multiple machines, and distributed operating systems where users are not aware. It also covers network structures, topologies, communication structures, protocols, and provides an example of networking. The objectives are to provide a high-level overview of distributed systems and discuss the general structure of distributed operating systems.
A distributed database is a collection of logically interrelated databases distributed over a computer network. A distributed database management system (DDBMS) manages the distributed database and makes the distribution transparent to users. There are two main types of DDBMS - homogeneous and heterogeneous. Key characteristics of distributed databases include replication of fragments, shared logically related data across sites, and each site being controlled by a DBMS. Challenges include complex management, security, and increased storage requirements due to data replication.
The document discusses concepts, functions, architecture, and design of distributed database management systems (DDBMS). It covers topics such as data allocation strategies, distributed relational database design, levels of transparency provided by DDBMSs, and Date's 12 rules for distributed database management. The overall goal of a DDBMS is to manage distributed databases across a computer network while hiding the distribution from users.
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.
This document summarizes key concepts from a lecture on computer applications:
1. John Von Neumann's concepts of computing established that memory would contain both data and instructions, and that only one instruction would be executed at a time. Current computer systems are based on these concepts and are multi-programming or multi-tasking.
2. Computer applications can be categorized as interactive systems, fault-tolerant systems, parallel systems, clustered computers, and supercomputers. Distributed and centralized systems each have their own advantages and disadvantages.
3. Computer programming languages have evolved from machine language to various generations of high-level languages like FORTRAN, Pascal, and languages that more closely resemble natural languages.
This document provides an overview of distributed systems and computing concepts through a series of slides. It defines a distributed system as a collection of independent computers interconnected via a network that can collaborate via message passing. Some key challenges of distributed computing discussed include heterogeneity, failure handling, concurrency, scalability, security, and transparency. Examples of distributed systems like web search, online games, and financial trading are provided. The document also discusses trends in distributed systems like ubiquitous networking and mobile/cloud computing.
Data Distribution Handling on Cloud for Deployment of Big Dataijccsa
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to process massive amounts of data. Processing large datasets has become crucial in research and business environments. The big challenges associated with processing large datasets is the vast infrastructure required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer combines individual output from each Vms to produce final result. we have proposed an algorithm to reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall data processing time in cloud.
This document discusses distributed database systems. It defines centralized, distributed, and decentralized database systems. The key topics covered include distributed database management systems (DDBMS), advantages and disadvantages of DDBMS, distributed database design involving data fragmentation, replication and allocation, functions of a DDBMS, types of DDBMS including homogeneous and heterogeneous, and database transparency and gateways. The document is presented by a group with members Zupash, Sana, Marhaba and a group leader Hira Anwar.
This document provides teaching material on distributed systems replication from the book "Distributed Systems: Concepts and Design". It includes slides on replication concepts such as performance enhancement through replication, fault tolerance, and availability. The slides cover replication transparency, consistency requirements, system models, group communication, fault-tolerant and highly available services, and consistency criteria like linearizability.
This document discusses synchronization and replication in occasionally connected mobile database systems. It begins by describing the architecture of such systems, where mobile clients maintain local copies of shared data and synchronize with a central server when reconnected. It then discusses using a data-centric approach where data is grouped and clients subscribe to relevant groups. The document proposes a grouping estimation algorithm to determine optimal data groupings. Finally, it describes how primary and secondary copies are used for replication among clients and servers, and the need for synchronization when clients operate offline.
Components of DDBMS, Computer workstations or remote devices,Network hardware and software components,Communications media,transaction processor (TP), data processor (DP),
This document discusses distributed database management systems (DDBMS). It defines a distributed database as a logical collection of shared data distributed over a computer network. A DDBMS allows for the management of distributed data while making the distribution transparent to users. The document outlines key concepts of DDBMS including characteristics, properties, reference architectures, fragmentation, replication, and transparencies. It also discusses advantages and disadvantages of DDBMS.
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 discusses distributed database design and fragmentation techniques. It begins with an outline of topics covered, then describes the design problem of placing data and applications across computer network sites. Primary horizontal fragmentation is explained as fragmenting a relation based on minterm predicates derived from a complete and minimal set of simple predicates describing the relation and application access patterns. An algorithm is provided to determine this fragmentation through several steps including finding the simple predicates, deriving minterm predicates, and eliminating contradictions to form the fragments. An example demonstrates applying this process to fragment relations based on salary and project budget attributes.
1) The document discusses quality of service (QoS)-aware data replication for data-intensive applications in cloud computing systems. It aims to minimize data replication cost and number of QoS violated replicas.
2) It presents a mathematical model and algorithm to optimally place QoS-satisfied and QoS-violated data replicas. The algorithm uses minimum-cost maximum flow to obtain the optimal placement.
3) The algorithm takes as input a set of requested nodes and outputs the optimal placement for QoS-satisfied and QoS-violated replicas by modeling the problem as a network flow graph and applying existing polynomial-time algorithms.
Distributed data base management systemSonu Mamman
The document discusses different types of distributed database systems. A homogeneous distributed database has identical software, hardware, operating systems, and data structures at each site. A heterogeneous distributed database allows different sites to use varying software, hardware, operating systems, and data structures. The client-server model separates database applications and data with clients making requests to servers housing the data.
The document discusses different types of parallelism that can be utilized in parallel database systems: I/O parallelism to retrieve relations from multiple disks in parallel, interquery parallelism to run different queries simultaneously, intraquery parallelism to parallelize operations within a single query, and intraoperation parallelism to parallelize individual operations like sort and join. It also covers techniques for partitioning relations across disks and handling skew to balance the workload.
This document discusses centralized and distributed messaging systems. A centralized system hosts all server resources in a central data center, allowing for easier management and security. A distributed system places servers locally at branch offices due to unreliable network connections between sites. Consider centralizing for cost savings if network prerequisites are met, and plan contingencies for potential single points of failure. Distributed systems are suitable when network conditions cannot support traffic to a central hub.
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.
Distributed computing system is a collection of interconnected computers that appear as a single system. There are two types of computer architectures for distributed systems - tightly coupled and loosely coupled. In tightly coupled systems, processors share a single memory while in loosely coupled systems, processors have their own local memory and communicate through message passing. Distributed systems provide advantages like better price-performance ratio, resource sharing, reliability, and scalability but also introduce challenges around transparency, communication, performance, heterogeneity, and fault tolerance.
The document discusses various topics related to distributed database management systems (DDBMS), including:
1) Transparency in DDBMS, which aims to hide the complexities of distribution from users. There are four main categories: distribution, transaction, performance, and DBMS transparency.
2) Distributed transaction management, which must ensure the ACID properties of transactions that access data across multiple sites. Objectives include improving utilization, minimizing response time and communication cost.
3) Concurrency control in DDBMS, which coordinates concurrent access to distributed data. Locking-based protocols are commonly used, where transactions acquire read or write locks before accessing data items. The two-phase locking protocol is also discussed.
This document provides an overview of distributed operating systems. It discusses the motivation for distributed systems including resource sharing, reliability, and computation speedup. It describes different types of distributed operating systems like network operating systems where users are aware of multiple machines, and distributed operating systems where users are not aware. It also covers network structures, topologies, communication structures, protocols, and provides an example of networking. The objectives are to provide a high-level overview of distributed systems and discuss the general structure of distributed operating systems.
A distributed database is a collection of logically interrelated databases distributed over a computer network. A distributed database management system (DDBMS) manages the distributed database and makes the distribution transparent to users. There are two main types of DDBMS - homogeneous and heterogeneous. Key characteristics of distributed databases include replication of fragments, shared logically related data across sites, and each site being controlled by a DBMS. Challenges include complex management, security, and increased storage requirements due to data replication.
The document discusses concepts, functions, architecture, and design of distributed database management systems (DDBMS). It covers topics such as data allocation strategies, distributed relational database design, levels of transparency provided by DDBMSs, and Date's 12 rules for distributed database management. The overall goal of a DDBMS is to manage distributed databases across a computer network while hiding the distribution from users.
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.
This document summarizes key concepts from a lecture on computer applications:
1. John Von Neumann's concepts of computing established that memory would contain both data and instructions, and that only one instruction would be executed at a time. Current computer systems are based on these concepts and are multi-programming or multi-tasking.
2. Computer applications can be categorized as interactive systems, fault-tolerant systems, parallel systems, clustered computers, and supercomputers. Distributed and centralized systems each have their own advantages and disadvantages.
3. Computer programming languages have evolved from machine language to various generations of high-level languages like FORTRAN, Pascal, and languages that more closely resemble natural languages.
This document provides an overview of distributed systems and computing concepts through a series of slides. It defines a distributed system as a collection of independent computers interconnected via a network that can collaborate via message passing. Some key challenges of distributed computing discussed include heterogeneity, failure handling, concurrency, scalability, security, and transparency. Examples of distributed systems like web search, online games, and financial trading are provided. The document also discusses trends in distributed systems like ubiquitous networking and mobile/cloud computing.
Data Distribution Handling on Cloud for Deployment of Big Dataijccsa
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to process massive amounts of data. Processing large datasets has become crucial in research and business environments. The big challenges associated with processing large datasets is the vast infrastructure required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer combines individual output from each Vms to produce final result. we have proposed an algorithm to reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall data processing time in cloud.
This document discusses distributed database systems. It defines centralized, distributed, and decentralized database systems. The key topics covered include distributed database management systems (DDBMS), advantages and disadvantages of DDBMS, distributed database design involving data fragmentation, replication and allocation, functions of a DDBMS, types of DDBMS including homogeneous and heterogeneous, and database transparency and gateways. The document is presented by a group with members Zupash, Sana, Marhaba and a group leader Hira Anwar.
This document provides teaching material on distributed systems replication from the book "Distributed Systems: Concepts and Design". It includes slides on replication concepts such as performance enhancement through replication, fault tolerance, and availability. The slides cover replication transparency, consistency requirements, system models, group communication, fault-tolerant and highly available services, and consistency criteria like linearizability.
This document discusses synchronization and replication in occasionally connected mobile database systems. It begins by describing the architecture of such systems, where mobile clients maintain local copies of shared data and synchronize with a central server when reconnected. It then discusses using a data-centric approach where data is grouped and clients subscribe to relevant groups. The document proposes a grouping estimation algorithm to determine optimal data groupings. Finally, it describes how primary and secondary copies are used for replication among clients and servers, and the need for synchronization when clients operate offline.
Components of DDBMS, Computer workstations or remote devices,Network hardware and software components,Communications media,transaction processor (TP), data processor (DP),
This document discusses distributed database management systems (DDBMS). It defines a distributed database as a logical collection of shared data distributed over a computer network. A DDBMS allows for the management of distributed data while making the distribution transparent to users. The document outlines key concepts of DDBMS including characteristics, properties, reference architectures, fragmentation, replication, and transparencies. It also discusses advantages and disadvantages of DDBMS.
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 discusses distributed database design and fragmentation techniques. It begins with an outline of topics covered, then describes the design problem of placing data and applications across computer network sites. Primary horizontal fragmentation is explained as fragmenting a relation based on minterm predicates derived from a complete and minimal set of simple predicates describing the relation and application access patterns. An algorithm is provided to determine this fragmentation through several steps including finding the simple predicates, deriving minterm predicates, and eliminating contradictions to form the fragments. An example demonstrates applying this process to fragment relations based on salary and project budget attributes.
Cloud Camp Milan 2K9 Telecom Italia: Where P2P?Gabriele Bozzi
1. The document discusses the potential for peer-to-peer (P2P) computing as an alternative or complement to the traditional client-server model, especially in the context of cloud computing.
2. It notes challenges with P2P such as lack of centralized control and potential for freeloading, but also advantages like harnessing unused resources.
3. Emerging technologies like autonomic and cognitive networking aim to address P2P challenges by enabling self-configuration and optimization of distributed resources.
1. The document discusses the potential for peer-to-peer (P2P) computing as an alternative or complement to the traditional client-server model, especially in the context of cloud computing.
2. P2P systems offer access to distributed resources but lack centralized control, which makes it difficult to ensure reliability, performance, and security.
3. Autonomic and cognitive approaches may help address issues with P2P by enabling self-configuration, healing, optimization and protection of distributed resources.
4. Future networking approaches like DirecNet envision high-speed mobile mesh networks that could further enable wide-scale distributed computing architectures.
Middleware for High Availability and Scalability in Multi-Tier and Service-Or...Francisco Pérez-Sorrosal
This presentation shows a summary of the main results obtained in my PhD Thesis. The main objectives of my Thesis include the definition, implementation and evaluation of new replication protocols at the middleware level that allow to define high available and scalable multi-tier and service-oriented architectures that preserve data consistency.
From Cloud to Fog: the Tao of IT Infrastructure DecentralizationFogGuru MSCA Project
The document discusses the transition from cloud computing to fog computing. Fog computing decentralizes computing to the edge of networks in order to address issues like high latency and huge data volumes from IoT devices. It describes how Kubernetes works well for containers but is not optimized for proximity-aware routing needed in fog. Algorithms are presented for optimizing pod placement in fog to minimize latency. Models for optimizing stream processing across fog nodes based on computational complexity and network latency are also introduced. The talk concludes by discussing an ongoing research project called FogGuru that is conducting real-world fog computing experiments.
This document provides an introduction and overview of CQRS (Command Query Responsibility Segregation). It discusses typical N-layer architectures and how CQRS changes the architecture by separating read and write operations. The key points are that CQRS moves the system from an N-layer to separating read and write concerns, with commands handling changes, events capturing changes, and read models optimized for queries and kept in sync with events. The document outlines the full flow from commands to events to updating read models and queries against the read models.
DSD-INT 2015 - 3Di pilot application in Taiwan - Jhih-Cyuan Shen, Geert PrinsenDeltares
3Di is a flood modeling software that allows for fast and accurate modeling using detailed elevation data. It allows calculations to be done interactively in the cloud on any device. The document discusses pilots of 3Di modeling in Taiwan, including applications in Meifu and Sanyei areas. It proposes various ways 3Di could be coupled with FEWS-Taiwan, an existing flood early warning system, including running 3Di models standalone or in the cloud driven by FEWS input and measures, and presenting 3Di results within FEWS or on a live 3Di site. Coupling 3Di with FEWS could combine their respective strengths while addressing challenges of running models interactively in the cloud.
Enabling multi tenancy(An Industrial Experience Report)ICSM 2010
This document discusses enabling multi-tenancy in applications. It describes converting a single-tenant application called Codename into a multi-tenant version called CodenameMT. The conversion involved adding a tenant ID to authentication tokens and database tables, loading tenant-specific configurations, and updating the data layer to be tenant-aware. The conversion took approximately 100 lines of code and 3 days to implement. Lessons learned include using a lightweight reengineering approach and maintaining layering and transparency for end-users and developers.
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 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.
Eventual Consistency - Du musst keine Angst habenSusanne Braun
Der Trend zu hochskalierenden Cloud-Anwendungen, die stark auf datengetriebene Features setzen, ist ungebrochen. Dadurch laufen immer mehr Anwendungen nur noch unter Eventual Consistency. Nebenläufige Änderungsoperationen auf inkonsistenten, replizierten Datenbeständen können allerdings zu schweren Replikations-Anomalien wie Lost Updates führen. Das Implementieren korrekter Merge-Logik im Fall von Schreibkonflikten ist eine große Fehlerquelle und selbst für sehr erfahrene Software-Architekt:innen eine Herausforderung. Basierend auf unseren Erfahrungen aus verschiedenen Kunden- und Forschungsprojekten entwickeln wir Architektur-Empfehlungen und Entwurfsmuster für das Design von Anwendungen, die unter Eventual Consistency laufen. Parallel treiben wir die Entwicklung eines Open-Source-Replikations-Frameworks, welches unsere Methoden unterstützt, voran. Der Vortrag gibt konkrete Hilfestellungen für Architekt:innen und beinhaltet eine Demo-Session.
This was a talk, largely on Kamaelia & its original context given at a Free Streaming Workshop in Florence, Italy in Summer 2004. Many of the core
concepts still hold valid in Kamaelia today
Domain modeling for Digital Transformations (FlowCon Paris 2019 edition)Cyrille Martraire
The collaboration between business people and the development teams is often distorted by implementation concerns, past habits and past constraints. This is harmful, especially if you have major ambitions. To achieve better software, developers could try to reverse-engineer from the given specifications, or you could embark into a joint exploration of the domain towards its first principles, which are the main concepts or assumptions that cannot be deduced from anything else.
Join Cyrille on this topic to find out how it can help your most challenging digital initiatives!
The evolution of data center network fabricsCisco Canada
This document discusses Cisco's hybrid cloud solutions and the challenges of adopting a hybrid cloud infrastructure. It provides an overview of Cisco's portfolio including the Nexus 9500/9300 series which provides increased scalability, performance and automation capabilities. Cisco solutions help modernize infrastructure through consolidation, simplification, automation and making the datacenter cloud-ready to improve cost, performance and security across private and public clouds.
[db tech showcase Tokyo 2019] Azure Cosmos DB Deep Dive ~ Partitioning, Globa...Naoki (Neo) SATO
[db tech showcase Tokyo 2019] Azure Cosmos DB Deep Dive ~ Partitioning, Global Distribution and Indexing ~
https://satonaoki.wordpress.com/2019/09/30/dbts2019-azure-cosmos-db-deep-dive/
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!