Databases are useful for storing and organizing large amounts of information. They work well when data has a defined structure and relationships between records. Databases can retrieve information with high accuracy if properly managed. A database contains tables which hold records with the same field structure. Each record contains data fields for a particular item. Fields make up the columns in a table, while records form the rows. Databases also use keys like primary and foreign keys to link records together. Boolean logic operators like AND, OR and NOT can be used to perform operations on data within a database.
The Six pillars for Building big data analytics ecosystemstaimur hafeez
The document discusses the six pillars for building big data analytics ecosystems: storage, processing, analytics, user interfaces, deployment, and future directions. It provides an overview of approaches for each pillar, popular systems, challenges, and how the pillars form a taxonomy to guide organizations in building their ecosystems. Key components discussed include HDFS, MapReduce, YARN, visualizations, product vs service deployment models, and ensuring the components work efficiently together.
This document discusses the differences between SQL and NoSQL databases. SQL databases are more rigid and structured, using tables and schemas to store and relate data. SQL databases ensure ACID compliance and are better for storing structured data. NoSQL databases are less rigid, allow for unstructured and changing data, enable faster development, and better support large, unstructured data like that generated from websites. The document provides examples of popular SQL databases like MySQL and Oracle and NoSQL databases like MongoDB and Cassandra. It also outlines when each type of database is generally more suitable - with SQL fitting well for applications like banking systems and NoSQL fitting better for social media sites.
This document discusses turning unstructured documents into structured data. It notes that while data is often thought of as existing in spreadsheets, much information exists as unstructured documents. It explores different approaches to structuring document data such as putting it into spreadsheets, extracting already structured data, helping partially structured data, or manually processing documents. Common tools mentioned for this include spreadsheet programs, Tabula, Acrobat, CometDocs, Python, R, and DocumentCloud. The overall message is that documents contain valuable data that can be structured and made available to others.
A Comparison between Relational Databases and NoSQL Databasesijtsrd
Databases are used for storing and managing large amounts of data. Relational model is useful when it comes to reliability but when it comes to the modern applications dealing with large amounts of data and the data is unstructured; non-relational models are usable. NoSQL databases are used to store large amounts of data. NoSQL databases are non-relational, distributed, open source and are horizontally scalable. This paper provides the comparison of the relational model with NoSQL Behjat U Nisa"A Comparison between Relational Databases and NoSQL Databases" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11214.pdf http://www.ijtsrd.com/computer-science/database/11214/a-comparison-between-relational-databases-and-nosql-databases/behjat-u-nisa
This document provides an overview of SQL and NoSQL databases. It discusses how relational databases using SQL emerged as the dominant data storage approach but faced challenges in scaling to big data workloads. NoSQL databases were developed to address these scaling needs by using non-relational data models like key-value, document, and column-oriented structures that are better suited to distributed architectures. The document outlines the history and characteristics of SQL and relational databases and how NoSQL databases address needs like scalability that drove their emergence in the big data era.
The document summarizes the key components of a database system. It defines data as unprocessed facts and information as data that has been interpreted to have meaning. Metadata is described as data that provides context and properties of other data. A database is defined as an organized collection of logically related data designed to meet the information requirements of users in an organization. The major components of a database system are identified as CASE tools, a repository, DBMS, databases, application programs, user interface, database administrator, system developers, and end users. Each component is briefly defined.
Databases are useful for storing and organizing large amounts of information. They work well when data has a defined structure and relationships between records. Databases can retrieve information with high accuracy if properly managed. A database contains tables which hold records with the same field structure. Each record contains data fields for a particular item. Fields make up the columns in a table, while records form the rows. Databases also use keys like primary and foreign keys to link records together. Boolean logic operators like AND, OR and NOT can be used to perform operations on data within a database.
The Six pillars for Building big data analytics ecosystemstaimur hafeez
The document discusses the six pillars for building big data analytics ecosystems: storage, processing, analytics, user interfaces, deployment, and future directions. It provides an overview of approaches for each pillar, popular systems, challenges, and how the pillars form a taxonomy to guide organizations in building their ecosystems. Key components discussed include HDFS, MapReduce, YARN, visualizations, product vs service deployment models, and ensuring the components work efficiently together.
This document discusses the differences between SQL and NoSQL databases. SQL databases are more rigid and structured, using tables and schemas to store and relate data. SQL databases ensure ACID compliance and are better for storing structured data. NoSQL databases are less rigid, allow for unstructured and changing data, enable faster development, and better support large, unstructured data like that generated from websites. The document provides examples of popular SQL databases like MySQL and Oracle and NoSQL databases like MongoDB and Cassandra. It also outlines when each type of database is generally more suitable - with SQL fitting well for applications like banking systems and NoSQL fitting better for social media sites.
This document discusses turning unstructured documents into structured data. It notes that while data is often thought of as existing in spreadsheets, much information exists as unstructured documents. It explores different approaches to structuring document data such as putting it into spreadsheets, extracting already structured data, helping partially structured data, or manually processing documents. Common tools mentioned for this include spreadsheet programs, Tabula, Acrobat, CometDocs, Python, R, and DocumentCloud. The overall message is that documents contain valuable data that can be structured and made available to others.
A Comparison between Relational Databases and NoSQL Databasesijtsrd
Databases are used for storing and managing large amounts of data. Relational model is useful when it comes to reliability but when it comes to the modern applications dealing with large amounts of data and the data is unstructured; non-relational models are usable. NoSQL databases are used to store large amounts of data. NoSQL databases are non-relational, distributed, open source and are horizontally scalable. This paper provides the comparison of the relational model with NoSQL Behjat U Nisa"A Comparison between Relational Databases and NoSQL Databases" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11214.pdf http://www.ijtsrd.com/computer-science/database/11214/a-comparison-between-relational-databases-and-nosql-databases/behjat-u-nisa
This document provides an overview of SQL and NoSQL databases. It discusses how relational databases using SQL emerged as the dominant data storage approach but faced challenges in scaling to big data workloads. NoSQL databases were developed to address these scaling needs by using non-relational data models like key-value, document, and column-oriented structures that are better suited to distributed architectures. The document outlines the history and characteristics of SQL and relational databases and how NoSQL databases address needs like scalability that drove their emergence in the big data era.
The document summarizes the key components of a database system. It defines data as unprocessed facts and information as data that has been interpreted to have meaning. Metadata is described as data that provides context and properties of other data. A database is defined as an organized collection of logically related data designed to meet the information requirements of users in an organization. The major components of a database system are identified as CASE tools, a repository, DBMS, databases, application programs, user interface, database administrator, system developers, and end users. Each component is briefly defined.
The document discusses technologies applied in distributed databases (DD) and distributed systems (DS). For DS, layered and client-server approaches are used to reduce complexity. The client-server model can be relational or object-oriented. For DD, important technologies are replication to synchronize data modification across nodes and duplication where a master data source copies content to other nodes. Technologies like client-server, object models, and NoSQL databases can be applied in both DD and DS.
https://www.learntek.org/blog/types-of-databases/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
How to Achieve Self-Service Analytics with a Governed Data Services Layer (UK)Denodo
Watch full webinar here: https://bit.ly/38F77WN
A successfully implemented self-service initiative means that business users have access to holistic and consistent views of data regardless of its location, source or type. However, companies must also ensure that while unlocking the full potential of data for business users, they maintain security requirements.
Data virtualization as a governed data service layer can not only help organizations achieve a unified data access layer that provides integrated views of data to business users in real time, but also allows the organization to establish governance protocols and specify authoritative sources.
What will we talk about?
- Challenges faced by business users
- How data virtualization enables self-service analytics
- A demonstration
- A customer case study
Understanding Metadata Needs when Migrating DAMSAyla Stein
This study identifies and explores metadata needs associated with migrating to a new Digital Asset Management System (DAMS). Drawing upon results from a 2014 survey, titled “Identifying Motivations for DAMS Migration: A Survey,” this paper analyzes survey questions related to metadata, interoperability, and digital preservation. Results indicate three distinct metadata needs for future system development, including support for multiple or all metadata schema, metadata reuse, and digital object identifiers. While some of these needs resemble long-standing conversations in the professional literature, others offer new areas for system development moving forward.
Co-author and presenter: Santi Thompson
Databases have evolved from hierarchical structures to relational database management systems with various architectures. Managing enterprise databases presents challenges due to increasing complexity, storage demands, scalability issues, and security concerns. Managed service providers can help mitigate these risks by providing expert database administration, ensuring high data availability and security, and optimizing performance, freeing internal IT teams to focus on more strategic work. Engaging a managed service provider makes particular sense for companies without full-time database administrators.
This document discusses big data and NoSQL databases. It defines big data as data with high volume, velocity, and variety that is difficult for traditional databases to handle. NoSQL databases are presented as an alternative designed for big data by allowing flexible schemas and easy scaling across data centers. The document uses Apache Cassandra as an example of a NoSQL database that can serve as a primary data store, handle real-time and batch analytics, and accommodate structured and unstructured data.
This document summarizes a presentation about Microsoft SQL Server's Management Data Warehouse (MDW). The MDW collects and stores performance data to allow for easy reporting of growth and performance trends. It collects data from sources like DMVs, traces, and performance counters and stores it in a relational database. The presentation covered how the MDW works, configuring collection sources and limits, and demonstrated built-in reports for disk usage, server usage, and query activity. It also discussed how to set up custom collectors and export reports.
A database is a tool for collecting and organizing information in an organized manner. It allows data to be separated into different tables that can be merged together when needed. Microsoft Access is a database management system that allows users to add, edit, delete, organize and share data through its interface and various components like tables, forms, reports, and queries. The primary functions of a database are to store records of related data in tables, where each record is uniquely identified by a primary key and relationships can be created between tables using foreign keys.
Getting Started with Data Virtualization – What problems DV solvesDenodo
Experts and analysts agree that data virtualization's strategic role in enterprise architecture for increasing agility and flexibility in the delivery of information. In this presentation, you will find how data virtualization enables organizations to access, manage, and integrate data from a wide variety of data sources.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/IS9RGK.
This document discusses hardware and software from a business manager's perspective. It describes the main components of a computer including the CPU, memory, storage, and how they work together. It explains how manager's should consider an employee's tasks and software/file needs when matching them with appropriate computers to reduce frustration and improve productivity. Factors like CPU speed, memory size, and whether a computer needs 32-bit or 64-bit processors are discussed. The document also covers server computers, client-server networks, and common categories of software like operating systems and applications.
MySQL and MongoDB are database management systems that differ in how they structure and store data. MySQL uses a relational model where data is stored in tables and rows and uses SQL, requiring the schema to be defined beforehand. MongoDB is non-relational and stores data as JSON-like documents that can vary in structure and do not require a predefined schema. Key differences include that MySQL uses joins while MongoDB embeds documents and supports arrays, and that MySQL requires a defined schema while MongoDB allows dynamic schemas.
This document summarizes research on using ant colony optimization (ACO) metaheuristics to find safety errors in software models. It introduces ACO and describes its key components, such as pheromone trails and probabilistic solution construction. It then presents ACOhg, a new ACO model for exploring huge graphs with bounded memory. ACOhg allows construction of partial solutions and uses expanding path lengths and periodic pheromone removal. The researchers applied ACOhg to 5 Promela models and found it could find errors in much larger models than exhaustive search algorithms like DFS and BFS, using less memory. They conclude ACO metaheuristics show promise for scalable heuristic model checking of safety properties.
This project aims to develop a Virtual Navigation System (VNS) to enhance navigation capabilities of an UAV. An inertial measurement unit (IMU), GPS module, and Xbee transmitters are mounted on the aircraft to measure its pitch, roll and yaw. This data is transmitted to a simulator to virtually replicate the aircraft's motion. The simulator chosen is X-Plane, into which the IMU data is fed as inputs. This allows pilots to train with a bird's eye view even in low visibility. The system has been integrated and initial tests show the simulator replicating the real aircraft's motion based on onboard sensor readings.
Energy-aware Task Scheduling using Ant-colony Optimization in cloudLinda J
The document proposes an energy-aware task scheduling algorithm using ant colony optimization for cloud computing. It aims to minimize energy consumption in datacenters by scheduling tasks efficiently across virtual machines and physical hosts. The algorithm uses concepts from ant colony optimization to probabilistically determine good task-to-resource allocations. The results show that the proposed approach reduces energy consumption by 22% compared to a first-come, first-served scheduling approach.
A brief introduction on the principles of particle swarm optimizaton by Rajorshi Mukherjee. This presentation has been compiled from various sources (not my own work) and proper references have been made in the bibliography section for further reading. This presentation was made as a presentation for submission for our college subject Soft Computing.
Autonomous Driver Assistance System Using Swarm IntelligenceMadura Pradeep
This is a research regarding driver assistance system for avoid bad traffic on the roads, using Swarm Intelligence technologies. This project gives traffic information in different location in the road network by using color code. So unlike other existing solutions, in this one driver can take decision according to the traffic density of different roads. Swarm Intelligence describes the collective behavior of decentralized, self-organized systems, that can be either natural or artificial. We have validate this project by building a simulator.
This document discusses the bee algorithm, which is an optimization technique inspired by the foraging behavior of honey bees. It begins with an introduction and overview of concepts like nature of bees, hill climbing, swarm intelligence, and bee colony optimization. It then describes the key steps of the proposed bee algorithm, including initializing a population of solutions, evaluating their fitness, selecting sites for neighborhood search, recruiting bees to search those sites, and iterating until an optimal solution is found. An example application to a traveling salesperson problem is provided. The document concludes that bee algorithm can help provide an optimal solution for problems with many possible solutions, such as in artificial intelligence applications.
Seminar on Driver Behaviour Detection using Swarm Intelligence.Rajani Suryavanshi
This document presents an approach for context-aware driver behavior detection using pervasive computing. It aims to reduce road accidents caused by driver errors by alerting drivers in a timely manner. The approach uses a three-tier network to gather context data from sensors using wireless sensor networks. Swarm intelligence and ant colony optimization are then used to infer driver behavior from the collected context data and detect unacceptable behaviors like fatigue or intoxication. The approach integrates wireless sensor networks, vehicle ad hoc networks, and swarm intelligence for comprehensive and reliable driver behavior monitoring.
The document describes the Backtracking Search Optimization Algorithm (BSA), a population-based evolutionary algorithm for solving numerical optimization problems. BSA initializes a random population and uses four main operators: selection I to determine a historical population, mutation to generate trial solutions, crossover to combine solutions, and selection II to replace current solutions with better trial solutions. It also includes a boundary control mechanism to regenerate out-of-bounds individuals and selects the overall best solution found as the global best.
The document summarizes the artificial fish swarm algorithm (AFSA), which is a population-based metaheuristic optimization algorithm inspired by fish schooling behavior. It describes how AFSA simulates behaviors like swarming, chasing, and random movement to explore the search space and exploit promising solutions. The algorithm represents potential solutions as individual fish and moves them through the search space based on their visual scope and interactions with neighboring fish. While AFSA has advantages like global search ability and parameter tolerance, it also has drawbacks such as higher time complexity and lack of balance between exploration and exploitation.
The document summarizes the artificial bee colony (ABC) algorithm, which was introduced in 2005 and is inspired by the foraging behavior of honeybee swarms. The ABC algorithm simulates three groups of bees - employed bees, onlookers, and scouts - to solve optimization problems. It involves phases of employed bee search, onlooker bee choice, and scout bee recruitment to balance exploration and exploitation. The ABC algorithm has few parameters and fast convergence but is limited by its initial solutions. Variations include multi-objective ABC algorithms and parameter studies on swarm size, limit, and dimension.
The document summarizes two nature-inspired metaheuristic algorithms: the Cuckoo Search algorithm and the Firefly algorithm.
The Cuckoo Search algorithm is based on the brood parasitism of some cuckoo species. It lays its eggs in the nests of other host birds. The algorithm uses Lévy flights for generating new solutions and considers the best solutions for the next generation.
The Firefly algorithm is based on the flashing patterns of fireflies to attract mates. It considers attractiveness that decreases with distance and movement of fireflies towards more attractive ones. The pseudo codes of both algorithms are provided along with some example applications.
The document discusses technologies applied in distributed databases (DD) and distributed systems (DS). For DS, layered and client-server approaches are used to reduce complexity. The client-server model can be relational or object-oriented. For DD, important technologies are replication to synchronize data modification across nodes and duplication where a master data source copies content to other nodes. Technologies like client-server, object models, and NoSQL databases can be applied in both DD and DS.
https://www.learntek.org/blog/types-of-databases/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
How to Achieve Self-Service Analytics with a Governed Data Services Layer (UK)Denodo
Watch full webinar here: https://bit.ly/38F77WN
A successfully implemented self-service initiative means that business users have access to holistic and consistent views of data regardless of its location, source or type. However, companies must also ensure that while unlocking the full potential of data for business users, they maintain security requirements.
Data virtualization as a governed data service layer can not only help organizations achieve a unified data access layer that provides integrated views of data to business users in real time, but also allows the organization to establish governance protocols and specify authoritative sources.
What will we talk about?
- Challenges faced by business users
- How data virtualization enables self-service analytics
- A demonstration
- A customer case study
Understanding Metadata Needs when Migrating DAMSAyla Stein
This study identifies and explores metadata needs associated with migrating to a new Digital Asset Management System (DAMS). Drawing upon results from a 2014 survey, titled “Identifying Motivations for DAMS Migration: A Survey,” this paper analyzes survey questions related to metadata, interoperability, and digital preservation. Results indicate three distinct metadata needs for future system development, including support for multiple or all metadata schema, metadata reuse, and digital object identifiers. While some of these needs resemble long-standing conversations in the professional literature, others offer new areas for system development moving forward.
Co-author and presenter: Santi Thompson
Databases have evolved from hierarchical structures to relational database management systems with various architectures. Managing enterprise databases presents challenges due to increasing complexity, storage demands, scalability issues, and security concerns. Managed service providers can help mitigate these risks by providing expert database administration, ensuring high data availability and security, and optimizing performance, freeing internal IT teams to focus on more strategic work. Engaging a managed service provider makes particular sense for companies without full-time database administrators.
This document discusses big data and NoSQL databases. It defines big data as data with high volume, velocity, and variety that is difficult for traditional databases to handle. NoSQL databases are presented as an alternative designed for big data by allowing flexible schemas and easy scaling across data centers. The document uses Apache Cassandra as an example of a NoSQL database that can serve as a primary data store, handle real-time and batch analytics, and accommodate structured and unstructured data.
This document summarizes a presentation about Microsoft SQL Server's Management Data Warehouse (MDW). The MDW collects and stores performance data to allow for easy reporting of growth and performance trends. It collects data from sources like DMVs, traces, and performance counters and stores it in a relational database. The presentation covered how the MDW works, configuring collection sources and limits, and demonstrated built-in reports for disk usage, server usage, and query activity. It also discussed how to set up custom collectors and export reports.
A database is a tool for collecting and organizing information in an organized manner. It allows data to be separated into different tables that can be merged together when needed. Microsoft Access is a database management system that allows users to add, edit, delete, organize and share data through its interface and various components like tables, forms, reports, and queries. The primary functions of a database are to store records of related data in tables, where each record is uniquely identified by a primary key and relationships can be created between tables using foreign keys.
Getting Started with Data Virtualization – What problems DV solvesDenodo
Experts and analysts agree that data virtualization's strategic role in enterprise architecture for increasing agility and flexibility in the delivery of information. In this presentation, you will find how data virtualization enables organizations to access, manage, and integrate data from a wide variety of data sources.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/IS9RGK.
This document discusses hardware and software from a business manager's perspective. It describes the main components of a computer including the CPU, memory, storage, and how they work together. It explains how manager's should consider an employee's tasks and software/file needs when matching them with appropriate computers to reduce frustration and improve productivity. Factors like CPU speed, memory size, and whether a computer needs 32-bit or 64-bit processors are discussed. The document also covers server computers, client-server networks, and common categories of software like operating systems and applications.
MySQL and MongoDB are database management systems that differ in how they structure and store data. MySQL uses a relational model where data is stored in tables and rows and uses SQL, requiring the schema to be defined beforehand. MongoDB is non-relational and stores data as JSON-like documents that can vary in structure and do not require a predefined schema. Key differences include that MySQL uses joins while MongoDB embeds documents and supports arrays, and that MySQL requires a defined schema while MongoDB allows dynamic schemas.
This document summarizes research on using ant colony optimization (ACO) metaheuristics to find safety errors in software models. It introduces ACO and describes its key components, such as pheromone trails and probabilistic solution construction. It then presents ACOhg, a new ACO model for exploring huge graphs with bounded memory. ACOhg allows construction of partial solutions and uses expanding path lengths and periodic pheromone removal. The researchers applied ACOhg to 5 Promela models and found it could find errors in much larger models than exhaustive search algorithms like DFS and BFS, using less memory. They conclude ACO metaheuristics show promise for scalable heuristic model checking of safety properties.
This project aims to develop a Virtual Navigation System (VNS) to enhance navigation capabilities of an UAV. An inertial measurement unit (IMU), GPS module, and Xbee transmitters are mounted on the aircraft to measure its pitch, roll and yaw. This data is transmitted to a simulator to virtually replicate the aircraft's motion. The simulator chosen is X-Plane, into which the IMU data is fed as inputs. This allows pilots to train with a bird's eye view even in low visibility. The system has been integrated and initial tests show the simulator replicating the real aircraft's motion based on onboard sensor readings.
Energy-aware Task Scheduling using Ant-colony Optimization in cloudLinda J
The document proposes an energy-aware task scheduling algorithm using ant colony optimization for cloud computing. It aims to minimize energy consumption in datacenters by scheduling tasks efficiently across virtual machines and physical hosts. The algorithm uses concepts from ant colony optimization to probabilistically determine good task-to-resource allocations. The results show that the proposed approach reduces energy consumption by 22% compared to a first-come, first-served scheduling approach.
A brief introduction on the principles of particle swarm optimizaton by Rajorshi Mukherjee. This presentation has been compiled from various sources (not my own work) and proper references have been made in the bibliography section for further reading. This presentation was made as a presentation for submission for our college subject Soft Computing.
Autonomous Driver Assistance System Using Swarm IntelligenceMadura Pradeep
This is a research regarding driver assistance system for avoid bad traffic on the roads, using Swarm Intelligence technologies. This project gives traffic information in different location in the road network by using color code. So unlike other existing solutions, in this one driver can take decision according to the traffic density of different roads. Swarm Intelligence describes the collective behavior of decentralized, self-organized systems, that can be either natural or artificial. We have validate this project by building a simulator.
This document discusses the bee algorithm, which is an optimization technique inspired by the foraging behavior of honey bees. It begins with an introduction and overview of concepts like nature of bees, hill climbing, swarm intelligence, and bee colony optimization. It then describes the key steps of the proposed bee algorithm, including initializing a population of solutions, evaluating their fitness, selecting sites for neighborhood search, recruiting bees to search those sites, and iterating until an optimal solution is found. An example application to a traveling salesperson problem is provided. The document concludes that bee algorithm can help provide an optimal solution for problems with many possible solutions, such as in artificial intelligence applications.
Seminar on Driver Behaviour Detection using Swarm Intelligence.Rajani Suryavanshi
This document presents an approach for context-aware driver behavior detection using pervasive computing. It aims to reduce road accidents caused by driver errors by alerting drivers in a timely manner. The approach uses a three-tier network to gather context data from sensors using wireless sensor networks. Swarm intelligence and ant colony optimization are then used to infer driver behavior from the collected context data and detect unacceptable behaviors like fatigue or intoxication. The approach integrates wireless sensor networks, vehicle ad hoc networks, and swarm intelligence for comprehensive and reliable driver behavior monitoring.
The document describes the Backtracking Search Optimization Algorithm (BSA), a population-based evolutionary algorithm for solving numerical optimization problems. BSA initializes a random population and uses four main operators: selection I to determine a historical population, mutation to generate trial solutions, crossover to combine solutions, and selection II to replace current solutions with better trial solutions. It also includes a boundary control mechanism to regenerate out-of-bounds individuals and selects the overall best solution found as the global best.
The document summarizes the artificial fish swarm algorithm (AFSA), which is a population-based metaheuristic optimization algorithm inspired by fish schooling behavior. It describes how AFSA simulates behaviors like swarming, chasing, and random movement to explore the search space and exploit promising solutions. The algorithm represents potential solutions as individual fish and moves them through the search space based on their visual scope and interactions with neighboring fish. While AFSA has advantages like global search ability and parameter tolerance, it also has drawbacks such as higher time complexity and lack of balance between exploration and exploitation.
The document summarizes the artificial bee colony (ABC) algorithm, which was introduced in 2005 and is inspired by the foraging behavior of honeybee swarms. The ABC algorithm simulates three groups of bees - employed bees, onlookers, and scouts - to solve optimization problems. It involves phases of employed bee search, onlooker bee choice, and scout bee recruitment to balance exploration and exploitation. The ABC algorithm has few parameters and fast convergence but is limited by its initial solutions. Variations include multi-objective ABC algorithms and parameter studies on swarm size, limit, and dimension.
The document summarizes two nature-inspired metaheuristic algorithms: the Cuckoo Search algorithm and the Firefly algorithm.
The Cuckoo Search algorithm is based on the brood parasitism of some cuckoo species. It lays its eggs in the nests of other host birds. The algorithm uses Lévy flights for generating new solutions and considers the best solutions for the next generation.
The Firefly algorithm is based on the flashing patterns of fireflies to attract mates. It considers attractiveness that decreases with distance and movement of fireflies towards more attractive ones. The pseudo codes of both algorithms are provided along with some example applications.
The document discusses the cuckoo search algorithm, which is a metaheuristic algorithm for global optimization inspired by the breeding behavior of some cuckoo species. It describes how cuckoos lay their eggs in other birds' nests, sometimes ejecting the host birds' eggs. The algorithm uses three rules - cuckoos lay one egg at a time in randomly chosen nests, the best nests carry over to future generations, and hosts can discover alien eggs with some probability. It also discusses Levy flights for random walks and the steps of the cuckoo search algorithm which involves generating nests, replacing eggs based on fitness, and abandoning nests to avoid local optimization. Finally, it lists some applications of the c
This document discusses particle swarm optimization (PSO), which is an optimization technique inspired by swarm intelligence. It summarizes that PSO was developed in 1995 and can be applied to various search and optimization problems. PSO works by having a swarm of particles that communicate locally to find the best solution within a search space, balancing exploration and exploitation.
This document discusses swarm intelligence and how decentralized groups can exhibit complex behaviors through self-organization and local interactions. Key points include:
- Swarm intelligence relies on emergent properties from simple interactions between autonomous agents to achieve complex global tasks.
- Examples of swarming behaviors include ants depositing pheromone trails to guide other ants to food sources and termites building nests through distributed pellet gathering and construction.
- Narratives can be written as a swarm through individuals leaving cues in the environment that are detected and expanded on by others, similar to how pheromone trails guide swarming insects.
Web Services-Enhanced Agile Modeling and Integrating Business ProcessesMustafa Salam
We propose a model-driven approach, based on Web services standards, for modeling and integrating agile business processes using Web services. The choice of focusing on Web services technology was not arbitrary. The large and broad adoption of this technology by enterprises will lead most business processes to be performed using Web services. Besides, the added value of Web services and their great interest to business process management are beyond doubt. Web services produce, on the one hand, loosely coupled applicative components.
On the other hand, they are the most widely used implementation technology of SOA (Service-Oriented Architecture), which is based on the large experiences of software and distributed component technologies. Being founded on the XML (eXtensible Markup Language) language, the SOAP (Simple Object Access Protocol) protocol and the UDDI (Universal Description Discovery and Integration) repository, this technology can be considered as an appropriate mean to ensure interoperability, data exchange and the publication and discovery of business processes when they can be implemented as Web services.
Enhance the energy awareness with ant colony optimazation in cloud computingjaygovindchauhan
The document discusses optimizing VM migration in cloud workflows using ant colony optimization to reduce energy consumption and processing time. It describes components of workflow systems like the workflow engine and scheduler. The methodology section outlines parsing workflows, initially assigning tasks randomly to VMs, then optimizing VM placement through ant colony-inspired migration to minimize time and energy based on task dependencies. The goal is to decentralize failure points and improve scheduling over existing methods.
1) The document presents a firefly-optimized routing algorithm for mobile ad hoc networks (MANETs). It aims to improve route acquisition efficiency over MANETs using the firefly algorithm.
2) The proposed algorithm models firefly behavior to select optimal routes. Fireflies communicate via flashing lights, and the algorithm models this to determine the best next hop.
3) Simulation results show the firefly-optimized routing algorithm outperforms AOMDV in terms of packet delivery ratio, packet loss, throughput, and end-to-end delay. The algorithm adapts well to dynamic network changes.
This document discusses several digital ecosystem solutions developed by Kai Pata and Tallinn University. It describes tools like LePlanner for developing and sharing digital learning scenarios, DigiMirror for monitoring school digital maturity, and eDidakikum for competence-based learning and analytics in higher education. It also outlines informal learning tools from the Learning Layers project and the Open Adventure Trail for gamified outdoor learning. The tools aim to support digital transformation across K-12, higher education, and workplace learning.
The document provides an introduction to NoSQL databases. It discusses that NoSQL databases provide a mechanism for storage and retrieval of data without using tabular relations like relational databases. NoSQL databases are used in real-time web applications and for big data. They also support SQL-like query languages. The document outlines different data modeling approaches, distribution models, consistency models and MapReduce in NoSQL databases.
This document provides an introduction to NoSQL databases, including the motivation behind them, where they fit, types of NoSQL databases like key-value, document, columnar, and graph databases, and an example using MongoDB. NoSQL databases are a new way of thinking about data that is non-relational, schema-less, and can be distributed and fault tolerant. They are motivated by the need to scale out applications and handle big data with flexible and modern data models.
GraphTalks Rome - Selecting the right TechnologyNeo4j
Dirk Möller discusses selecting the right database technology, with a focus on graph databases like Neo4j. He outlines the benefits of graph databases over relational and NoSQL databases for connected data, including high performance, easy maintenance, and seamless evolution. Möller also provides examples of common use cases where graph databases have business benefits in areas like recommendations, fraud detection, and network operations.
Selecting the right database type for your knowledge management needs.Synaptica, LLC
This presentation looks at relational vs. graph databases and their advantages and disadvantages in storing semantic data for taxonomies and ontologies.
This document discusses relational and non-relational databases. It begins by introducing NoSQL databases and some of their key characteristics like not requiring a fixed schema and avoiding joins. It then discusses why NoSQL databases became popular for companies dealing with huge data volumes due to limitations of scaling relational databases. The document covers different types of NoSQL databases like key-value, column-oriented, graph and document-oriented databases. It also discusses concepts like eventual consistency, ACID properties, and the CAP theorem in relation to NoSQL databases.
NoSQL is a non-relational database designed for large-scale data storage needs. It has several key features: it is non-relational, schema-free, uses simple APIs, and is distributed. The four main types of NoSQL databases are key-value, column-oriented, document-oriented, and graph-based. Key advantages of NoSQL include scalability, flexibility in data structures, and ease of development. However, NoSQL sacrifices some consistency and lacks standardization compared to SQL databases.
This document provides an overview of NoSQL databases. It begins with a brief history of early database systems and their limitations in handling big data and complex relationships. It then discusses the rise of NoSQL databases to address these limitations by providing a more scalable and flexible solution. The main sections define what a NoSQL database is, describe its key characteristics like schema-less design and horizontal scalability, categorize the different types of NoSQL databases, outline advantages like flexibility and performance for big data, and discuss challenges to consider regarding consistency and learning curves.
How to Survive as a Data Architect in a Polyglot Database WorldKaren Lopez
Karen Lopez talks to data architects and data moders how they can best deliver value on modern data drive projects beyond relational database technologies. She covers NoSQL Databases and Datastores, which data stories they best fit and which ones they don't. She ends with 10 tips for adding more value to ployschematic database solutions.
This document provides an overview of non-relational (NoSQL) databases. It discusses the history and characteristics of NoSQL databases, including that they do not require rigid schemas and can automatically scale across servers. The document also categorizes major types of NoSQL databases, describes some popular NoSQL databases like Dynamo and Cassandra, and discusses benefits and limitations of both SQL and NoSQL databases.
The document provides an introduction to NoSQL databases, including key definitions and characteristics. It discusses that NoSQL databases are non-relational and do not follow RDBMS principles. It also summarizes different types of NoSQL databases like document stores, key-value stores, and column-oriented stores. Examples of popular databases for each type are also provided.
This document discusses emerging trends in databases, including NoSQL databases and object-oriented databases. It provides information on the characteristics, categories, advantages, and disadvantages of NoSQL databases. It also compares relational databases to object-oriented databases and discusses object-relational mapping.
The document discusses the history and concepts of NoSQL databases. It notes that traditional single-processor relational database management systems (RDBMS) struggled to handle the increasing volume, velocity, variability, and agility of data due to various limitations. This led engineers to explore scaled-out solutions using multiple processors and NoSQL databases, which embrace concepts like horizontal scaling, schema flexibility, and high performance on commodity hardware. Popular NoSQL database models include key-value stores, column-oriented databases, document stores, and graph databases.
NoSQL is a non-relational database approach that accommodates a wide variety of data models. It is non-relational, distributed, flexible and scalable. The four main types of NoSQL databases are document databases, key-value stores, column-oriented databases, and graph databases. MongoDB is an example of a document-oriented NoSQL database. NoSQL databases offer benefits over relational databases like flexible schemas, horizontal scalability, and fast queries. Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its parallel programming model and the Hadoop Distributed File System for storage.
The document discusses NoSQL databases and their advantages compared to SQL databases. It defines NoSQL as any database that is not relational and describes the main categories of NoSQL databases - key-value stores, document databases, wide column stores like BigTable, and graph databases. It also covers common use cases for different NoSQL databases and examples of companies using NoSQL technologies like MongoDB, Cassandra, and HBase.
Exploring Relational and NoSQL Databases.pdfUncodemy
Exploring Relational and NoSQL Databases: Understanding the Foundations of Data Management" delves into the fundamental principles of data management, comparing and contrasting relational and NoSQL database systems. This comprehensive exploration equips you with insights into choosing the right database solution for various applications, enhancing your data handling expertise.
This document provides an overview of NoSQL data architecture patterns, including key-value stores, graph stores, and column family stores. It describes key aspects of each pattern such as how keys and values are structured. Key-value stores use a simple key-value approach with no query language, while graph stores are optimized for relationships between objects. Column family stores use row and column identifiers as keys and scale well for large volumes of data.
The rising interest in NoSQL technology over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies From survey we create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use from the software engineer point of view.
This document discusses multidimensional databases and provides comparisons to relational databases. It describes how multidimensional databases are optimized for data warehousing and online analytical processing (OLAP) applications. Key aspects covered include dimensional modeling using star and snowflake schemas, data storage in cubes with dimensions and members, and performance benefits of multidimensional databases for interactive analysis of large datasets to support decision making.
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...Paul Brebner
Closing talk for the Performance Engineering track at Community Over Code EU (Bratislava, Slovakia, June 5 2024) https://eu.communityovercode.org/sessions/2024/why-apache-kafka-clusters-are-like-galaxies-and-other-cosmic-kafka-quandaries-explored/ Instaclustr (now part of NetApp) manages 100s of Apache Kafka clusters of many different sizes, for a variety of use cases and customers. For the last 7 years I’ve been focused outwardly on exploring Kafka application development challenges, but recently I decided to look inward and see what I could discover about the performance, scalability and resource characteristics of the Kafka clusters themselves. Using a suite of Performance Engineering techniques, I will reveal some surprising discoveries about cosmic Kafka mysteries in our data centres, related to: cluster sizes and distribution (using Zipf’s Law), horizontal vs. vertical scalability, and predicting Kafka performance using metrics, modelling and regression techniques. These insights are relevant to Kafka developers and operators.
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian CompaniesQuickdice ERP
Explore the seamless transition to e-invoicing with this comprehensive guide tailored for Saudi Arabian businesses. Navigate the process effortlessly with step-by-step instructions designed to streamline implementation and enhance efficiency.
The Rising Future of CPaaS in the Middle East 2024Yara Milbes
Explore "The Rising Future of CPaaS in the Middle East in 2024" with this comprehensive PPT presentation. Discover how Communication Platforms as a Service (CPaaS) is transforming communication across various sectors in the Middle East.
Unlock the Secrets to Effortless Video Creation with Invideo: Your Ultimate G...The Third Creative Media
"Navigating Invideo: A Comprehensive Guide" is an essential resource for anyone looking to master Invideo, an AI-powered video creation tool. This guide provides step-by-step instructions, helpful tips, and comparisons with other AI video creators. Whether you're a beginner or an experienced video editor, you'll find valuable insights to enhance your video projects and bring your creative ideas to life.
DECODING JAVA THREAD DUMPS: MASTER THE ART OF ANALYSISTier1 app
Are you ready to unlock the secrets hidden within Java thread dumps? Join us for a hands-on session where we'll delve into effective troubleshooting patterns to swiftly identify the root causes of production problems. Discover the right tools, techniques, and best practices while exploring *real-world case studies of major outages* in Fortune 500 enterprises. Engage in interactive lab exercises where you'll have the opportunity to troubleshoot thread dumps and uncover performance issues firsthand. Join us and become a master of Java thread dump analysis!
UI5con 2024 - Bring Your Own Design SystemPeter Muessig
How do you combine the OpenUI5/SAPUI5 programming model with a design system that makes its controls available as Web Components? Since OpenUI5/SAPUI5 1.120, the framework supports the integration of any Web Components. This makes it possible, for example, to natively embed own Web Components of your design system which are created with Stencil. The integration embeds the Web Components in a way that they can be used naturally in XMLViews, like with standard UI5 controls, and can be bound with data binding. Learn how you can also make use of the Web Components base class in OpenUI5/SAPUI5 to also integrate your Web Components and get inspired by the solution to generate a custom UI5 library providing the Web Components control wrappers for the native ones.
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UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
INTRODUCTION TO AI CLASSICAL THEORY TARGETED EXAMPLESanfaltahir1010
Image: Include an image that represents the concept of precision, such as a AI helix or a futuristic healthcare
setting.
Objective: Provide a foundational understanding of precision medicine and its departure from traditional
approaches
Role of theory: Discuss how genomics, the study of an organism's complete set of AI ,
plays a crucial role in precision medicine.
Customizing treatment plans: Highlight how genetic information is used to customize
treatment plans based on an individual's genetic makeup.
Examples: Provide real-world examples of successful application of AI such as genetic
therapies or targeted treatments.
Importance of molecular diagnostics: Explain the role of molecular diagnostics in identifying
molecular and genetic markers associated with diseases.
Biomarker testing: Showcase how biomarker testing aids in creating personalized treatment plans.
Content:
• Ethical issues: Examine ethical concerns related to precision medicine, such as privacy, consent, and
potential misuse of genetic information.
• Regulations and guidelines: Present examples of ethical guidelines and regulations in place to safeguard
patient rights.
• Visuals: Include images or icons representing ethical considerations.
Content:
• Ethical issues: Examine ethical concerns related to precision medicine, such as privacy, consent, and
potential misuse of genetic information.
• Regulations and guidelines: Present examples of ethical guidelines and regulations in place to safeguard
patient rights.
• Visuals: Include images or icons representing ethical considerations.
Content:
• Ethical issues: Examine ethical concerns related to precision medicine, such as privacy, consent, and
potential misuse of genetic information.
• Regulations and guidelines: Present examples of ethical guidelines and regulations in place to safeguard
patient rights.
• Visuals: Include images or icons representing ethical considerations.
Real-world case study: Present a detailed case study showcasing the success of precision
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Data challenges: Address the challenges associated with managing large sets of patient data in precision
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A Comprehensive Guide on Implementing Real-World Mobile Testing Strategies fo...kalichargn70th171
In today's fiercely competitive mobile app market, the role of the QA team is pivotal for continuous improvement and sustained success. Effective testing strategies are essential to navigate the challenges confidently and precisely. Ensuring the perfection of mobile apps before they reach end-users requires thoughtful decisions in the testing plan.
Consistent toolbox talks are critical for maintaining workplace safety, as they provide regular opportunities to address specific hazards and reinforce safe practices.
These brief, focused sessions ensure that safety is a continual conversation rather than a one-time event, which helps keep safety protocols fresh in employees' minds. Studies have shown that shorter, more frequent training sessions are more effective for retention and behavior change compared to longer, infrequent sessions.
Engaging workers regularly, toolbox talks promote a culture of safety, empower employees to voice concerns, and ultimately reduce the likelihood of accidents and injuries on site.
The traditional method of conducting safety talks with paper documents and lengthy meetings is not only time-consuming but also less effective. Manual tracking of attendance and compliance is prone to errors and inconsistencies, leading to gaps in safety communication and potential non-compliance with OSHA regulations. Switching to a digital solution like Safelyio offers significant advantages.
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This method minimizes disruptions to work schedules, eliminates the hassle of paperwork, and ensures that all safety communications are tracked and recorded accurately. Ultimately, using a digital platform like Safelyio enhances engagement, compliance, and overall safety performance on site. https://safelyio.com/
Preparing Non - Technical Founders for Engaging a Tech AgencyISH Technologies
Preparing non-technical founders before engaging a tech agency is crucial for the success of their projects. It starts with clearly defining their vision and goals, conducting thorough market research, and gaining a basic understanding of relevant technologies. Setting realistic expectations and preparing a detailed project brief are essential steps. Founders should select a tech agency with a proven track record and establish clear communication channels. Additionally, addressing legal and contractual considerations and planning for post-launch support are vital to ensure a smooth and successful collaboration. This preparation empowers non-technical founders to effectively communicate their needs and work seamlessly with their chosen tech agency.Visit our site to get more details about this. Contact us today www.ishtechnologies.com.au
Most important New features of Oracle 23c for DBAs and Developers. You can get more idea from my youtube channel video from https://youtu.be/XvL5WtaC20A
4. Relational Design
Post_id Post Post_at …
1 What is relational db? 2014-12-09 08:32:16
2 Java …. 2014-12-09 08:45:44
Post_id User_id Comment
1 54 Organizes data into
multiple tables with
relation between them
1 63 ….
What is relational db?
• Organizes data into multiple tables with
relation between them
• Table which have relations
4
5. Problem
Can you examine the comments
without looking at post?
5
7. NoSQL
Provides a mechanism for storage
and retrieval of data that is
modeled in means other than the
tabular relations used in relational
databases
- wikipedia -
7
8. Non Relational Design
Post_id Post Created_at comments …
1 What is relational db? 2014-12-09
08:32:16
Organizes data into
multiple tables with
relation between them,
Table which have
relations,
…
8
9. Reasons to marry
• Scalability
• Big data
• Economics
• Flexible data model
9
10. Don’t kick out Relational DB
• Maturity
• Support & Design skills
• Analytics
10