MYCIN was an early expert system developed in the 1970s to diagnose and recommend treatments for infections. It used a knowledge base of around 200 rules, certainty factors, and backward chaining to evaluate patients' symptoms and test results. MYCIN was found to match expert physician recommendations for treating infections 52% of the time in evaluations. The system helped demonstrate the potential for rule-based and probabilistic reasoning in medical expert systems.
The document discusses the requirements and components of expert systems. It notes that expert systems must have a narrow problem area requiring significant human expertise. They must perform at an expert level and be able to explain their reasoning. Structurally, they require a knowledge base containing domain expertise separated from inference procedures, as well as a working memory, inference engine, explanation facility, and user interface. The knowledge base stores facts and rules, the inference engine applies rules to derive new facts, and working memory stores known facts.
Rumbaugh's Object Modeling Technique (OMT) is an object-oriented analysis and design methodology. It uses three main modeling approaches: object models, dynamic models, and functional models. The object model defines the structure of objects in the system through class diagrams. The dynamic model describes object behavior over time using state diagrams and event flow diagrams. The functional model represents system processes and data flow using data flow diagrams.
Importance & Principles of Modeling from UML DesigningABHISHEK KUMAR
Object oriented analysis and design uses modeling to understand systems being developed. Models simplify systems at different abstraction levels to visualize structure and behavior, provide templates for building systems, and document decisions. Effective modeling requires choosing appropriate models that influence solutions, expressing models at different abstraction levels for different stakeholders, ensuring models are grounded in reality, and using multiple complementary models to solve complex systems.
This document discusses distributed data processing (DDP) as an alternative to centralized data processing. Some key points:
1) DDP involves dispersing computers and processing throughout an organization to allow for greater flexibility and redundancy compared to centralized systems.
2) Factors driving the increase of DDP include dramatically reduced workstation costs, improved desktop interfaces and power, and the ability to share data across servers.
3) While DDP provides benefits like increased responsiveness, availability, and user involvement, it also presents drawbacks such as more points of failure, incompatibility issues, and complex management compared to centralized systems.
This document provides an overview of object-oriented analysis and design concepts. It defines key OO terms like objects, classes, inheritance, polymorphism, and relationships. It also describes several OO methodologies like OMT, Booch, and Objectory. Finally, it discusses design patterns, frameworks, and the motivation for a unified OO approach.
This document discusses software metrics and measurement. It describes how measurement can be used throughout the software development process to assist with estimation, quality control, productivity assessment, and project control. It defines key terms like measures, metrics, and indicators and explains how they provide insight into the software process and product. The document also discusses using metrics to evaluate and improve the software process as well as track project status, risks, and quality. Finally, it covers different types of metrics like size-oriented, function-oriented, and quality metrics.
MYCIN was an early expert system developed in the 1970s to diagnose and recommend treatments for infections. It used a knowledge base of around 200 rules, certainty factors, and backward chaining to evaluate patients' symptoms and test results. MYCIN was found to match expert physician recommendations for treating infections 52% of the time in evaluations. The system helped demonstrate the potential for rule-based and probabilistic reasoning in medical expert systems.
The document discusses the requirements and components of expert systems. It notes that expert systems must have a narrow problem area requiring significant human expertise. They must perform at an expert level and be able to explain their reasoning. Structurally, they require a knowledge base containing domain expertise separated from inference procedures, as well as a working memory, inference engine, explanation facility, and user interface. The knowledge base stores facts and rules, the inference engine applies rules to derive new facts, and working memory stores known facts.
Rumbaugh's Object Modeling Technique (OMT) is an object-oriented analysis and design methodology. It uses three main modeling approaches: object models, dynamic models, and functional models. The object model defines the structure of objects in the system through class diagrams. The dynamic model describes object behavior over time using state diagrams and event flow diagrams. The functional model represents system processes and data flow using data flow diagrams.
Importance & Principles of Modeling from UML DesigningABHISHEK KUMAR
Object oriented analysis and design uses modeling to understand systems being developed. Models simplify systems at different abstraction levels to visualize structure and behavior, provide templates for building systems, and document decisions. Effective modeling requires choosing appropriate models that influence solutions, expressing models at different abstraction levels for different stakeholders, ensuring models are grounded in reality, and using multiple complementary models to solve complex systems.
This document discusses distributed data processing (DDP) as an alternative to centralized data processing. Some key points:
1) DDP involves dispersing computers and processing throughout an organization to allow for greater flexibility and redundancy compared to centralized systems.
2) Factors driving the increase of DDP include dramatically reduced workstation costs, improved desktop interfaces and power, and the ability to share data across servers.
3) While DDP provides benefits like increased responsiveness, availability, and user involvement, it also presents drawbacks such as more points of failure, incompatibility issues, and complex management compared to centralized systems.
This document provides an overview of object-oriented analysis and design concepts. It defines key OO terms like objects, classes, inheritance, polymorphism, and relationships. It also describes several OO methodologies like OMT, Booch, and Objectory. Finally, it discusses design patterns, frameworks, and the motivation for a unified OO approach.
This document discusses software metrics and measurement. It describes how measurement can be used throughout the software development process to assist with estimation, quality control, productivity assessment, and project control. It defines key terms like measures, metrics, and indicators and explains how they provide insight into the software process and product. The document also discusses using metrics to evaluate and improve the software process as well as track project status, risks, and quality. Finally, it covers different types of metrics like size-oriented, function-oriented, and quality metrics.
This document discusses active databases and how they differ from conventional passive databases. Active databases can monitor a database for predefined situations and trigger actions automatically in response. This is accomplished through the use of active rules embedded within the database. The document outlines the key components of active rules, including events, conditions, and actions. It also covers the execution model of active databases and how rules are evaluated and triggered at runtime. Examples are provided of how active databases and triggers can be used for tasks like maintaining derived data values and enforcing integrity constraints.
Architectural design steps, Representing the system in context, Archetypes, instantiations of system, Refine architecture into components, Refine components structure, ADL, Fundamentals of Software Engineering
The document discusses the physical symbol system hypothesis proposed by Allen Newell and Herbert Simon. The hypothesis states that symbol manipulation is essential to both human and machine intelligence. It claims that a system with the ability to manipulate physical symbols according to formal rules can exhibit generally intelligent behavior. The document provides background on the philosophical roots of the idea. It also gives examples of physical symbol systems, such as formal logic and digital computers. Finally, it discusses some of the contributions and criticisms of the physical symbol system hypothesis in artificial intelligence research.
Security in Clouds: Cloud security challenges – Software as a
Service Security, Common Standards: The Open Cloud Consortium – The Distributed management Task Force – Standards for application Developers – Standards for Messaging – Standards for Security, End user access to cloud computing, Mobile Internet devices and the cloud. Hadoop – MapReduce – Virtual Box — Google App Engine – Programming Environment for Google App Engine.
Big Data: Its Characteristics And Architecture CapabilitiesAshraf Uddin
This document discusses big data, including its definition, characteristics, and architecture capabilities. It defines big data as large datasets that are challenging to store, search, share, visualize, and analyze due to their scale, diversity and complexity. The key characteristics of big data are described as volume, velocity and variety. The document then outlines the architecture capabilities needed for big data, including storage and management, database, processing, data integration and statistical analysis capabilities. Hadoop and MapReduce are presented as core technologies for storage, processing and analyzing large datasets in parallel across clusters of computers.
What is Heuristics?
A heuristic is a technique that is used to solve a problem faster than the classic methods. These techniques are used to find the approximate solution of a problem when classical methods do not. Heuristics are said to be the problem-solving techniques that result in practical and quick solutions.
Heuristics are strategies that are derived from past experience with similar problems. Heuristics use practical methods and shortcuts used to produce the solutions that may or may not be optimal, but those solutions are sufficient in a given limited timeframe.
History
Psychologists Daniel Kahneman and Amos Tversky have developed the study of Heuristics in human decision-making in the 1970s and 1980s. However, this concept was first introduced by the Nobel Laureate Herbert A. Simon, whose primary object of research was problem-solving.
Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of a short-term solution. On facing complex situations with limited resources and time, Heuristics can help the companies to make quick decisions by shortcuts and approximated calculations. Most of the heuristic methods involve mental shortcuts to make decisions on past experiences.
Heuristic techniques
The heuristic method might not always provide us the finest solution, but it is assured that it helps us find a good solution in a reasonable time.
Based on context, there can be different heuristic methods that correlate with the problem's scope. The most common heuristic methods are - trial and error, guesswork, the process of elimination, historical data analysis. These methods involve simply available information that is not particular to the problem but is most appropriate. They can include representative, affect, and availability heuristics.
We can perform the Heuristic techniques into two categories:
Direct Heuristic Search techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search techniques are not always possible as they require much memory and time. These techniques search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and Depth First Search (DFS).
Weak Heuristic Search techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques are helpful when they are applied properly to the right types of tasks. They usually require domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.
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 the introduction to Internet of Things (IoT). It defines IoT and describes its characteristics, physical and logical design, enabling technologies, and deployment levels. The physical design covers IoT devices, protocols, and communication layers. The logical design includes functional blocks, communication models, and APIs. Enabling technologies include wireless sensor networks, cloud computing, big data analysis, communication protocols, and embedded systems. IoT deployment levels range from single node local systems to multi-node systems with local coordination and cloud-based storage, analysis and applications.
Dynamic Itemset Counting (DIC) is an algorithm for efficiently mining frequent itemsets from transactional data that improves upon the Apriori algorithm. DIC allows itemsets to begin being counted as soon as it is suspected they may be frequent, rather than waiting until the end of each pass like Apriori. DIC uses different markings like solid/dashed boxes and circles to track the counting status of itemsets. It can generate frequent itemsets and association rules using conviction in fewer passes over the data compared to Apriori.
Data base management system(DBMS), sourav mathursourav mathur
This document discusses database management systems (DBMS) and database design. It provides definitions of key concepts including:
- A DBMS allows for easy collection, retrieval, and updating of stored data across an organization.
- The database design process involves understanding real-world entities and relationships, creating entity-relationship diagrams, normalizing data tables, and refining the design if needed.
- Entity-relationship modeling defines entities, attributes, keys, and relationship types such as one-to-one, one-to-many, and many-to-many.
- The database design process results in a logical data model that represents the structure for an organization's database.
This document discusses data partitioning strategies for large scale systems. It explains that partitioning data across multiple data stores can improve performance, scalability, availability, security and operational flexibility of applications. The key partitioning strategies described are horizontal partitioning (sharding), vertical partitioning and functional partitioning. Horizontal partitioning involves splitting data into shards, each containing a subset of data. Vertical partitioning splits data into different fields or columns. Functional partitioning splits data based on functionality, such as invoicing vs product inventory. The document then focuses on horizontal partitioning and elastic databases, describing how data can be partitioned across multiple SQL databases while maintaining a global shard map for routing queries. It discusses issues to consider with partitioning such as minimizing cross-partition operations and maintaining referential
This document describes a study on using artificial neural networks for intelligent real-time power quality monitoring. It provides an overview of the need for real-time monitoring, introduces artificial neural networks and discusses power quality issues. It then describes the proposed neural network model for predicting power demand values and classifying harmonics and abnormal waveforms with over 99% accuracy. The conclusions state that neural networks can make monitoring systems more intelligent and robust by handling noisy data during worst conditions.
This document provides an overview of a requirements specification (SRS) for a software engineering project. It defines what an SRS is, its purpose, types of requirements it should include, its typical structure, characteristics of a good SRS, and benefits of developing an SRS. The SRS is intended to clearly define the requirements for a software product to guide its design and development.
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
This document provides a syllabus for a course on big data. The course introduces students to big data concepts like characteristics of data, structured and unstructured data sources, and big data platforms and tools. Students will learn data analysis using R software, big data technologies like Hadoop and MapReduce, mining techniques for frequent patterns and clustering, and analytical frameworks and visualization tools. The goal is for students to be able to identify domains suitable for big data analytics, perform data analysis in R, use Hadoop and MapReduce, apply big data to problems, and suggest ways to use big data to increase business outcomes.
Semantic networks are a knowledge representation technique where concepts are represented as nodes in a graph, and relationships between concepts are represented as links between nodes. There are different types of semantic networks, including definitional networks that emphasize subclass relationships, assertional networks for making propositions, and executable networks that can change based on operations. Common semantic relations include IS-A for subclasses, INSTANCE for examples, and HAS-PART for components. While semantic networks provide a natural representation of relationships, they have disadvantages like lack of standard link names and difficulty representing some logical constructs.
The Grid means the infrastructure for the Advanced Web, for computing, collaboration and communication.
The goal is to create the illusion of a simple yet large and powerful self managing virtual computer out of a large collection of connected heterogeneous systems sharing various combinations of resources.
“Grid” computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and ,in some cases, high-performance orientation .
We presented the Grid concept in analogy with that of an electrical power grid and Grid vision
SE_Lec 05_System Modelling and Context ModelAmr E. Mohamed
System modeling is the process of developing abstract models of a system using graphical notations like the Unified Modeling Language (UML) to represent different views of a system. Models help analysts understand system functionality and communicate with customers. Models of existing and new systems are used during requirements engineering to clarify current systems, discuss strengths/weaknesses, and explain proposed requirements.
The document discusses various architectural styles used in software design. It describes styles such as main program and subroutines, object-oriented, layered, client-server, data-flow, shared memory, interpreter, and implicit invocation. Each style has different components, communication methods, and advantages/disadvantages for managing complexity and promoting qualities like reuse and modifiability. Common examples of each style are also provided.
This document describes LinkedIn's Databus, a distributed change data capture system that reliably captures and propagates changes from primary data stores. It has four main components - a fetcher that extracts changes from data sources, a log store that caches changes, a snapshot store that maintains a moving snapshot, and a subscription client that pulls changes. Databus uses a pull-based model where consumers pull changes based on a monotonically increasing system change number. It supports capturing transaction boundaries, commit order, and consistent states to preserve consistency from the data source.
This document discusses active databases and how they differ from conventional passive databases. Active databases can monitor a database for predefined situations and trigger actions automatically in response. This is accomplished through the use of active rules embedded within the database. The document outlines the key components of active rules, including events, conditions, and actions. It also covers the execution model of active databases and how rules are evaluated and triggered at runtime. Examples are provided of how active databases and triggers can be used for tasks like maintaining derived data values and enforcing integrity constraints.
Architectural design steps, Representing the system in context, Archetypes, instantiations of system, Refine architecture into components, Refine components structure, ADL, Fundamentals of Software Engineering
The document discusses the physical symbol system hypothesis proposed by Allen Newell and Herbert Simon. The hypothesis states that symbol manipulation is essential to both human and machine intelligence. It claims that a system with the ability to manipulate physical symbols according to formal rules can exhibit generally intelligent behavior. The document provides background on the philosophical roots of the idea. It also gives examples of physical symbol systems, such as formal logic and digital computers. Finally, it discusses some of the contributions and criticisms of the physical symbol system hypothesis in artificial intelligence research.
Security in Clouds: Cloud security challenges – Software as a
Service Security, Common Standards: The Open Cloud Consortium – The Distributed management Task Force – Standards for application Developers – Standards for Messaging – Standards for Security, End user access to cloud computing, Mobile Internet devices and the cloud. Hadoop – MapReduce – Virtual Box — Google App Engine – Programming Environment for Google App Engine.
Big Data: Its Characteristics And Architecture CapabilitiesAshraf Uddin
This document discusses big data, including its definition, characteristics, and architecture capabilities. It defines big data as large datasets that are challenging to store, search, share, visualize, and analyze due to their scale, diversity and complexity. The key characteristics of big data are described as volume, velocity and variety. The document then outlines the architecture capabilities needed for big data, including storage and management, database, processing, data integration and statistical analysis capabilities. Hadoop and MapReduce are presented as core technologies for storage, processing and analyzing large datasets in parallel across clusters of computers.
What is Heuristics?
A heuristic is a technique that is used to solve a problem faster than the classic methods. These techniques are used to find the approximate solution of a problem when classical methods do not. Heuristics are said to be the problem-solving techniques that result in practical and quick solutions.
Heuristics are strategies that are derived from past experience with similar problems. Heuristics use practical methods and shortcuts used to produce the solutions that may or may not be optimal, but those solutions are sufficient in a given limited timeframe.
History
Psychologists Daniel Kahneman and Amos Tversky have developed the study of Heuristics in human decision-making in the 1970s and 1980s. However, this concept was first introduced by the Nobel Laureate Herbert A. Simon, whose primary object of research was problem-solving.
Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of a short-term solution. On facing complex situations with limited resources and time, Heuristics can help the companies to make quick decisions by shortcuts and approximated calculations. Most of the heuristic methods involve mental shortcuts to make decisions on past experiences.
Heuristic techniques
The heuristic method might not always provide us the finest solution, but it is assured that it helps us find a good solution in a reasonable time.
Based on context, there can be different heuristic methods that correlate with the problem's scope. The most common heuristic methods are - trial and error, guesswork, the process of elimination, historical data analysis. These methods involve simply available information that is not particular to the problem but is most appropriate. They can include representative, affect, and availability heuristics.
We can perform the Heuristic techniques into two categories:
Direct Heuristic Search techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search techniques are not always possible as they require much memory and time. These techniques search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and Depth First Search (DFS).
Weak Heuristic Search techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques are helpful when they are applied properly to the right types of tasks. They usually require domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.
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 the introduction to Internet of Things (IoT). It defines IoT and describes its characteristics, physical and logical design, enabling technologies, and deployment levels. The physical design covers IoT devices, protocols, and communication layers. The logical design includes functional blocks, communication models, and APIs. Enabling technologies include wireless sensor networks, cloud computing, big data analysis, communication protocols, and embedded systems. IoT deployment levels range from single node local systems to multi-node systems with local coordination and cloud-based storage, analysis and applications.
Dynamic Itemset Counting (DIC) is an algorithm for efficiently mining frequent itemsets from transactional data that improves upon the Apriori algorithm. DIC allows itemsets to begin being counted as soon as it is suspected they may be frequent, rather than waiting until the end of each pass like Apriori. DIC uses different markings like solid/dashed boxes and circles to track the counting status of itemsets. It can generate frequent itemsets and association rules using conviction in fewer passes over the data compared to Apriori.
Data base management system(DBMS), sourav mathursourav mathur
This document discusses database management systems (DBMS) and database design. It provides definitions of key concepts including:
- A DBMS allows for easy collection, retrieval, and updating of stored data across an organization.
- The database design process involves understanding real-world entities and relationships, creating entity-relationship diagrams, normalizing data tables, and refining the design if needed.
- Entity-relationship modeling defines entities, attributes, keys, and relationship types such as one-to-one, one-to-many, and many-to-many.
- The database design process results in a logical data model that represents the structure for an organization's database.
This document discusses data partitioning strategies for large scale systems. It explains that partitioning data across multiple data stores can improve performance, scalability, availability, security and operational flexibility of applications. The key partitioning strategies described are horizontal partitioning (sharding), vertical partitioning and functional partitioning. Horizontal partitioning involves splitting data into shards, each containing a subset of data. Vertical partitioning splits data into different fields or columns. Functional partitioning splits data based on functionality, such as invoicing vs product inventory. The document then focuses on horizontal partitioning and elastic databases, describing how data can be partitioned across multiple SQL databases while maintaining a global shard map for routing queries. It discusses issues to consider with partitioning such as minimizing cross-partition operations and maintaining referential
This document describes a study on using artificial neural networks for intelligent real-time power quality monitoring. It provides an overview of the need for real-time monitoring, introduces artificial neural networks and discusses power quality issues. It then describes the proposed neural network model for predicting power demand values and classifying harmonics and abnormal waveforms with over 99% accuracy. The conclusions state that neural networks can make monitoring systems more intelligent and robust by handling noisy data during worst conditions.
This document provides an overview of a requirements specification (SRS) for a software engineering project. It defines what an SRS is, its purpose, types of requirements it should include, its typical structure, characteristics of a good SRS, and benefits of developing an SRS. The SRS is intended to clearly define the requirements for a software product to guide its design and development.
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
This document provides a syllabus for a course on big data. The course introduces students to big data concepts like characteristics of data, structured and unstructured data sources, and big data platforms and tools. Students will learn data analysis using R software, big data technologies like Hadoop and MapReduce, mining techniques for frequent patterns and clustering, and analytical frameworks and visualization tools. The goal is for students to be able to identify domains suitable for big data analytics, perform data analysis in R, use Hadoop and MapReduce, apply big data to problems, and suggest ways to use big data to increase business outcomes.
Semantic networks are a knowledge representation technique where concepts are represented as nodes in a graph, and relationships between concepts are represented as links between nodes. There are different types of semantic networks, including definitional networks that emphasize subclass relationships, assertional networks for making propositions, and executable networks that can change based on operations. Common semantic relations include IS-A for subclasses, INSTANCE for examples, and HAS-PART for components. While semantic networks provide a natural representation of relationships, they have disadvantages like lack of standard link names and difficulty representing some logical constructs.
The Grid means the infrastructure for the Advanced Web, for computing, collaboration and communication.
The goal is to create the illusion of a simple yet large and powerful self managing virtual computer out of a large collection of connected heterogeneous systems sharing various combinations of resources.
“Grid” computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and ,in some cases, high-performance orientation .
We presented the Grid concept in analogy with that of an electrical power grid and Grid vision
SE_Lec 05_System Modelling and Context ModelAmr E. Mohamed
System modeling is the process of developing abstract models of a system using graphical notations like the Unified Modeling Language (UML) to represent different views of a system. Models help analysts understand system functionality and communicate with customers. Models of existing and new systems are used during requirements engineering to clarify current systems, discuss strengths/weaknesses, and explain proposed requirements.
The document discusses various architectural styles used in software design. It describes styles such as main program and subroutines, object-oriented, layered, client-server, data-flow, shared memory, interpreter, and implicit invocation. Each style has different components, communication methods, and advantages/disadvantages for managing complexity and promoting qualities like reuse and modifiability. Common examples of each style are also provided.
This document describes LinkedIn's Databus, a distributed change data capture system that reliably captures and propagates changes from primary data stores. It has four main components - a fetcher that extracts changes from data sources, a log store that caches changes, a snapshot store that maintains a moving snapshot, and a subscription client that pulls changes. Databus uses a pull-based model where consumers pull changes based on a monotonically increasing system change number. It supports capturing transaction boundaries, commit order, and consistent states to preserve consistency from the data source.
The document provides an overview of architectural design in software engineering. It defines software architecture as the structure of components, relationships between them, and properties. The key steps in architectural design are creating data design, representing structure, analyzing styles, and elaborating chosen style. It emphasizes software components and their focus. Examples of architectural styles discussed include data flow, call-and-return, data-centered, and virtual machine.
Attributes are properties or characteristics that describe entities. In the EMPLOYEE entity example, attributes could include:
- Employee ID
- Name
- Date of birth
- Address
- Salary
These attributes describe and provide information about each employee entity instance. Attributes help define and differentiate entity instances from each other.
The document provides an overview of software architectural design, including:
1) Definitions of software architecture, architectural design, and architectural design models. The architectural design process involves creating a data design, deriving architectural representations, analyzing styles, and elaborating the selected style.
2) An emphasis on software components as the basic elements of an architecture. Common component types include modules, classes, databases, and middleware.
3) The importance of architectural design for communication, early impactful decisions, and managing complexity through abstraction. Example diagrams are provided.
4) Descriptions of common architectural styles like data flow, call-and-return, object-oriented, layered, and data-centered styles. Adv
This document discusses database system architectures and distributed database systems. It covers transaction server systems, distributed database definitions, promises of distributed databases, complications introduced, and design issues. It also provides examples of horizontal and vertical data fragmentation and discusses parallel database architectures, components, and data partitioning techniques.
The document discusses process management in data warehousing. It describes the typical components involved - load manager, warehouse manager, and query manager. The load manager is responsible for extracting, transforming and loading data. The warehouse manager manages the data in the warehouse through indexing, aggregation and normalization. The query manager directs user queries to appropriate tables. Additionally, the document outlines the three perspectives for process modeling - conceptual, logical, and physical. The conceptual perspective represents interrelationships abstractly, the logical captures structure and data characteristics, while the physical provides execution details.
Unit 1: Introduction to DBMS Unit 1 CompleteRaj vardhan
This document discusses database management systems (DBMS) and their advantages over traditional file-based data storage. It describes the key components of a DBMS, including the hardware, software, data, procedures, and users. It also explains the three levels of abstraction in a DBMS - the physical level, logical level, and view level - and how they provide data independence. Finally, it provides an overview of different data models like hierarchical, network, and relational models.
A relational model of data for large shared data banksSammy Alvarez
This document introduces the relational model of data organization for large shared databases. It discusses inadequacies of existing tree-structured and network models, including ordering, indexing, and access path dependencies that impair data independence. The relational model represents data as mathematical n-ary relations and relationships between domains, providing independence from representation changes. It allows a clearer evaluation of existing systems and competing internal representations. The relational view forms a basis for treating issues like derivability, redundancy, and consistency in a sound way.
The document discusses Oracle Golden Gate software. It provides real-time data integration across heterogeneous database systems with low overhead. It captures transactional data from source databases using log-based extraction and delivers the data to target systems with transactional integrity. Key features include high performance, flexibility to integrate various database types, and reliability during outages.
Databases allow for the storage and organization of related data. A database contains tables that store data in rows and columns. A database management system (DBMS) helps define, construct, and manipulate the database. Relational databases follow a relational model and store data in related tables. Benefits of databases over file systems include reduced data redundancy, avoidance of data inconsistency, ability to share data among multiple users, and application of security restrictions. Transactions allow multiple database operations to be executed atomically as a single unit.
Web based-distributed-sesnzer-using-service-oriented-architectureAidah Izzah Huriyah
This document proposes a web-based distributed network analyzer using System Entity Structure (SES) over a service-oriented architecture. It aims to efficiently analyze large amounts of network behavior data. The system would distribute network monitoring and data capture inside target networks, while performing analysis outside networks for security. It uses Discrete Event System Specification and service-oriented architecture to deploy workloads across multiple servers, improving performance. The SES approach structures data hierarchically to optimize analysis based on user requirements. The document provides background on DEVS modeling, SES, and web services before describing the proposed distributed analysis system.
The document summarizes key aspects of architectural design for software systems. It defines software architecture as the structure of system components and relationships between them. Architecture is important for analyzing design effectiveness, considering alternatives, and managing risks. Key architectural styles described include data-centered, data flow, call and return, object-oriented, and layered. The document also discusses defining architectural context diagrams, archetypes, and components to design system architecture.
Data Warehouse Physical Design,Physical Data Model, Tablespaces, Integrity Constraints, ETL (Extract-Transform-Load) ,OLAP Server Architectures, MOLAP vs. ROLAP, Distributed Data Warehouse ,
The document discusses securing query processing in cloud computing environments. It identifies three key requirements for secure query processing: 1) authenticating users and machines, 2) securing data transfer across machines, and 3) ensuring integrity of query results. The document also analyzes existing and proposed systems for wireless multi-hop networks, including analyzing performance under different conditions.
An architectural style establishes a structure for all components of a system and includes data-centered, data-flow, call and return, object-oriented, and layered architectures. Architectural patterns differ from styles by focusing on one aspect of architecture rather than the whole, imposing rules on how software handles functionality, and addressing specific behavioral issues within an architectural context. Patterns can be used with styles to shape an overall system structure.
The document discusses various data models, database system architectures, database languages, and components of database management systems. It provides details on hierarchical, network, and relational data models including their advantages and disadvantages. It also describes physical centralized and distributed database architectures. Key database languages covered are DDL, DML, DCL, and transaction control language. DBMS interfaces and utilities are also summarized.
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CoVID-19 sprang up in Wuhan China in November 2019 and was declared a pandemic by the in January 2020 World Health Organization (WHO). Like the Spanish flu of 1918 that claimed millions of lives, the COVID-19 has caused the demise of thousands with China, Italy, Spain, USA and India having the highest statistics on infection and mortality rates. Regardless of existing sophisticated technologies and medical science, the spread has continued to surge high. With this COVID-19 Management System, organizations can respond virtually to the COVID-19 pandemic and protect, educate and care for citizens in the community in a quick and effective manner. This comprehensive solution not only helps in containing the virus but also proactively empowers both citizens and care providers to minimize the spread of the virus through targeted strategies and education.
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afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
This is an overview of my current metallic design and engineering knowledge base built up over my professional career and two MSc degrees : - MSc in Advanced Manufacturing Technology University of Portsmouth graduated 1st May 1998, and MSc in Aircraft Engineering Cranfield University graduated 8th June 2007.
2. In data flow architecture, the whole software system is seen as a
series of transformations on consecutive pieces or set of input data,
where data and operations are independent of each other. In this
approach, the data enters into the system and then flows through the
modules one at a time until they are assigned to some final
destination (output or a data store).
The connections between the components or modules may be
implemented as I/O stream, I/O buffers, piped, or other types of
connections. The data can be flown in the graph topology with cycles,
in a linear structure without cycles, or in a tree type structure.
The main objective of this approach is to achieve the qualities of
reuse and modifiability. It is suitable for applications that involve a
well-defined series of independent data transformations or
computations on orderly defined input and output such as compilers
and business data processing applications.
There are three types of execution sequences between modules−
1. Batch sequential
2. Pipe and filter or non-sequential pipeline mode
3. Process control
4. Batch sequential is a classical data processing model, in which a
data transformation subsystem can initiate its process only after its
previous subsystem is completely through −
The flow of data carries a batch of data as a whole from one
subsystem to another.
The communications between the modules are conducted through
temporary intermediate files which can be removed by successive
subsystems.
It is applicable for those applications where data is batched, and
each subsystem reads related input files and writes output files.
Typical application of this architecture includes business data
processing such as banking and utility billing.
6. Advantages:-
1. Provides simpler divisions on subsystems.
2. Each subsystem can be an independent program working on
input data and producing output data.
Disadvantages:-
1. Provides high latency and low throughput.
2. Does not provide concurrency and interactive interface.
3. External control is required for implementation.
8. This approach lays emphasis on the incremental
transformation of data by successive component. In this
approach, the flow of data is driven by data and the
whole system is decomposed into components of data
source, filters, pipes, and data sinks.
The connections between modules are data stream
which is first-in/first-out buffer that can be stream of
bytes, characters, or any other type of such kind. The
main feature of this architecture is its concurrent and
incremented execution.
9. Filter
A filter is an independent data stream transformer or stream
transducers. It transforms the data of the input data stream, processes
it, and writes the transformed data stream over a pipe for the next
filter to process. It works in an incremental mode, in which it starts
working as soon as data arrives through connected pipe. There are
two types of filters − active filter and passive filter.
Active filter
Active filter lets connected pipes to pull data in and push out the
transformed data. It operates with passive pipe, which provides
read/write mechanisms for pulling and pushing. This mode is used in
UNIX pipe and filter mechanism.
Passive filter
Passive filter lets connected pipes to push data in and pull data out. It
operates with active pipe, which pulls data from a filter and pushes
data into the next filter. It must provide read/write mechanism.
12. Advantages
Provides concurrency and high throughput for excessive data
processing.
Provides reusability and simplifies system maintenance.
Provides modifiability and low coupling between filters.
Provides simplicity by offering clear divisions between any two
filters connected by pipe.
Provides flexibility by supporting both sequential and parallel
execution.
13. Disadvantages
Not suitable for dynamic interactions.
A low common denominator is needed for transmission of data in
ASCII formats.
Overhead of data transformation between filters.
Does not provide a way for filters to cooperatively interact to solve
a problem.
Difficult to configure this architecture dynamically.
14. Pipe
Pipes are stateless and they carry binary or character stream which
exist between two filters. It can move a data stream from one filter to
another. Pipes use a little contextual information and retain no state
information between instantiations.
16. It is a type of data flow architecture where data is neither batched sequential nor
pipelined stream. The flow of data comes from a set of variables, which controls the
execution of process. It decomposes the entire system into subsystems or modules and
connects them.
Types of Subsystems
A process control architecture would have a processing unit for changing the process
control variables and a controller unit for calculating the amount of changes.
A controller unit must have the following elements −
Controlled Variable − Controlled Variable provides values for the underlying system
and should be measured by sensors. For example, speed in cruise control system.
Input Variable − Measures an input to the process. For example, temperature of
return air in temperature control system
Manipulated Variable − Manipulated Variable value is adjusted or changed by the
controller.
Process Definition − It includes mechanisms for manipulating some process
variables.
Sensor − Obtains values of process variables pertinent to control and can be used as a
feedback reference to recalculate manipulated variables.
Set Point − It is the desired value for a controlled variable.
Control Algorithm − It is used for deciding how to manipulate process variables.
17. Application Areas:
Process control architecture is suitable in the following
domains −
Embedded system software design, where the system is
manipulated by process control variable data.
Applications, which aim is to maintain specified properties
of the outputs of the process at given reference values.
Applicable for car-cruise control and building temperature
control systems.
Real-time system software to control automobile anti-lock
brakes, nuclear power plants, etc.
19. In data-centered architecture, the data is centralized and accessed
frequently by other components, which modify data. The main
purpose of this style is to achieve integrality of data. Data-centered
architecture consists of different components that communicate
through shared data repositories. The components access a shared
data structure and are relatively independent, in that, they interact
only through the data store.
The most well-known examples of the data-centered architecture is
a database architecture, in which the common database schema is
created with data definition protocol – for example, a set of related
tables with fields and data types in an RDBMS.
Another example of data-centered architectures is the web
architecture which has a common data schema (i.e. meta-structure
of the Web) and follows hypermedia data model and processes
communicate through the use of shared web-based data services.
22. There are two types of components −
A central data structure or data store or data repository, which is
responsible for providing permanent data storage. It represents the
current state.
A data accessor or a collection of independent components that
operate on the central data store, perform computations, and might
put back the results.
Interactions or communication between the data accessors is only
through the data store. The data is the only means of communication
among clients. The flow of control differentiates the architecture into
two categories −
1. Repository Architecture Style
2. Blackboard Architecture Style
23. 1. Repository Architecture
Repository Architecture StyleIn Repository Architecture Style, the data store is
passive and the clients (software components or agents) of the data store are active,
which control the logic flow. The participating components check the data-store for
changes.
The client sends a request to the system to perform actions (e.g. insert data).
The computational processes are independent and triggered by incoming requests.
If the types of transactions in an input stream of transactions trigger selection of
processes to execute, then it is traditional database or repository architecture, or
passive repository.
This approach is widely used in DBMS, library information system, the interface
repository in CORBA, compilers and CASE (computer aided software engineering)
environments.
26. Advantages
Provides data integrity, backup and restore features.
Provides scalability and reusability of agents as they do not have
direct communication with each other.
Reduces overhead of transient data between software components.
Disadvantages
It is more vulnerable to failure and data replication or duplication is
possible.
High dependency between data structure of data store and its agents.
Changes in data structure highly affect the clients.
Evolution of data is difficult and expensive.
Cost of moving data on network for distributed data.
27. 2. Blackboard Architecture Style
In Blackboard Architecture Style, the data store is active and its clients are
passive. Therefore the logical flow is determined by the current data status in
data store.
It has a blackboard component, acting as a central data repository, and an
internal representation is built and acted upon by different computational
elements.
A number of components that act independently on the common data structure
are stored in the blackboard.
In this style, the components interact only through the blackboard. The data-
store alerts the clients whenever there is a data-store change.
The current state of the solution is stored in the blackboard and processing is
triggered by the state of the blackboard.
The system sends notifications known as trigger and data to the clients when
changes occur in the data.
28. This approach is found in certain AI applications and complex applications,
such as speech recognition, image recognition, security system, and business
resource management systems etc.
If the current state of the central data structure is the main trigger of selecting
processes to execute, the repository can be a blackboard and this shared data
source is an active agent.
A major difference with traditional database systems is that the invocation of
computational elements in a blackboard architecture is triggered by the
current state of the blackboard, and not by external inputs.
29. Parts of Blackboard Model:------
The blackboard model is usually presented with three major parts −
Knowledge Sources (KS)
Knowledge Sources, also known as Listeners or Subscribers are distinct and
independent units. They solve parts of a problem and aggregate partial results.
Interaction among knowledge sources takes place uniquely through the
blackboard.
Blackboard Data Structure
The problem-solving state data is organized into an application-dependent
hierarchy. Knowledge sources make changes to the blackboard that lead
incrementally to a solution to the problem.
Control
Control manages tasks and checks the work state.
32. Advantages
Provides scalability which provides easy to add or update knowledge source.
Provides concurrency that allows all knowledge sources to work in parallel as
they are independent of each other.
Supports experimentation for hypotheses.
Supports reusability of knowledge source agents.
Disadvantages
The structure change of blackboard may have a significant impact on all of its
agents as close dependency exists between blackboard and knowledge source.
It can be difficult to decide when to terminate the reasoning as only
approximate solution is expected.
Problems in synchronization of multiple agents.
Major challenges in designing and testing of system.
34. Call and Return architectures:
It is used to create a program that is easy to scale and modify.
Many sub-styles exist within this category.
Two of them are explained below.
1. Remote procedure call architecture: This component is used to
present in a main program or sub program architecture distributed
among multiple computers on a network.
2. Main program or Subprogram architectures: The main
program structure decomposes into number of subprograms or
function into a control hierarchy. Main program contains number of
subprograms that can invoke other components.
37. Layered architecture:
A number of different layers are defined with each layer
performing a well-defined set of operations. Each layer will do
some operations that becomes closer to machine instruction set
progressively.
At the outer layer, components will receive the user interface
operations and at the inner layers, components will perform the
operating system interfacing(communication and coordination
with OS)
Intermediate layers to utility services and application software
functions.
40. Microservices Architecture:
Microservices are independently maintainable, testable, and deployable. The
loose coupling makes them faster and easier to maintain. An application is
divided into multiple independent modules that communicate through a well-
defined interface using lightweight APIs. Standalone functionalities allow a
complex design to be organized around specific business needs. As a company
grows and evolves its tech stack, Microservices allow seamless integration of
new features.
Types of Microservices :---
There are two types of Microservices:
1. Differential: Microservices architecture decomposes into self-encapsulated
services, which can break into different transactions. This means a local
transaction is distributed into multiple services.
2. Integral: Microservices applications weave multiple atomic microservices
into holistic user experiences. It addresses on-demand provisioning, dynamic
composition, and service level management.
42. Advantages of Microservices :—
Here are some unique advantages which set Microservices apart from other
architecture styles:
● Autonomous: Each component of service in a Microservices architecture
can be developed, deployed, operated, and scaled without affecting the
functionality of other services.
● Specialized: Each service is built with a set of capabilities and focuses on
solving a specific problem. Over time, if it scales, the service can be further
bifurcated into smaller services. It helps teams to measure the size of
infrastructure needs, the value of a product, and sustain reliability if the system
is facing a surge in demand.
● Resilience: In the Microservices architecture, independence prevails —
failure of any service does not affect the entire application.
● Technological Freedom: A Microservices system does not adopt the “one
size fits all” strategy. Teams can choose the best tool to address their issues and
goals independently.
● Easy Deployment: Microservices allow continuous integration and delivery
which makes it easy to try out new concepts or roll back if something goes
wrong. The low cost of failure encourages development, promotes software
upgrades, and accelerates time-to-market for new features.
43. Disadvantages of Microservices :—
Here is a list of some potential pain areas associated with Microservices:
● Complex Communication: Increased interconnections and
interdependencies raise complexity and the chance of mismanagement.
● More Resources: ‘More services mean more resources’
Microservices are great for large companies but can be complicated for small
companies in terms of resources and implementation.
● Independence: The same independence which acts as an advantage is also
a disadvantage. As each service has its own database and transaction
management, data consistency becomes difficult.
● Security: Increased inter-service communication results in a security threat
for a network hack.
46. Reactive Architecture:-
A Reactive system is an architectural style that allows multiple individual
applications to coalesce as a single unit, reacting to its surroundings while aware of
each other, and enable automatic scale up and down, load balancing, responsiveness
under failure, and more.
Reactive Architecture can elastically scale in the face of varying incoming traffic.
Scaling usually serves one of two purposes: either we need to scale out (by adding
more machines) and up (by adding beefier machines), or we need to scale down,
reducing the number of resources occupied by our application.
An interesting scaling pattern popularized by the likes of Netflix is predictive
scaling, in which we know when spikes are going to hit so we can proactively
provision servers for that period, and once traffic starts going down again, decrease
the cluster size incrementally.
47. Reactive programming is an asynchronous programming paradigm,
concerned with streams of information and the propagation of
changes. This differs from imperative programming, where that
paradigm uses statements to change a program’s state.
Reactive Architecture is nothing more than the combination of
reactive programming and software architectures. Also known as
reactive systems, the goal is to make the system responsive, resilient,
elastic, and message driven.
48. Benefits of Reactive Architecture :-
•Be responsive to interactions with its users
•Handle failure and remain available during outages
•Strive under varying load conditions
•Be able to send, receive, and route messages in varying network
conditions
50. Authors of Reactive Manifesto believe that Reactive Systems are:----
1. Responsive
2. Resilient
3. Elastic
4. Message Driven
1. Responsive:----
Responsive systems focus on providing rapid and consistent response times,
establishing reliable upper bounds so they deliver a consistent quality of service.
This consistent behavior in turn simplifies error handling, builds end user
confidence, and encourages further interaction.
51. 2. Resilient :-----
Resilience is achieved by replication, containment, isolation and delegation.
3. Elastic:----
elasticity achieve in a cost-effective way on commodity hardware and software
platforms.
The system stays responsive under varying workload. Reactive Systems can react
to changes in the input rate by increasing or decreasing the resources allocated
to service these inputs.
This implies designs that have no contention points or central bottlenecks,
resulting in the ability to shard or replicate components and distribute inputs
among them.
4. Message Driven:------
Message Driven as a means of communication makes it possible for the
management of failure to work with the same constructs and semantics across a
cluster or within a single host.
53. REST:-
REST, or REpresentational State Transfer, is an architectural style for providing
standards between computer systems on the web, making it easier for systems to
communicate with each other. REST-compliant systems, often called RESTful
systems, are characterized by how they are stateless and separate the concerns of
client and server.
We will go into what these terms mean and why they are beneficial
characteristics for services on the Web.
REST Architectural Constraints:-----
EST defines 6 architectural constraints which make any web service – a true
RESTful API.
1. Uniform interface
2. Client–server
3. Stateless
4. Cacheable
5. Layered system
54. 1. Uniform interface:----
Any single resource should not be too large and contain each and everything in its
representation. Whenever relevant, a resource should contain links pointing to
relative URIs to fetch related information.
Also, the resource representations across the system should follow specific guidelines
such as naming conventions, link formats, or data format (XML or/and JSON).
All resources should be accessible through a common approach such as HTTP GET
and similarly modified using a consistent approach.
2. Client–server:----
Client–server means “the client application and server application MUST be able to
evolve separately without any dependency on each other.”
A client should know only resource URIs, and that’s all.
55. 3. Stateless:----
All client-server interactions stateless. The server will not store anything about the
latest HTTP request the client made.
It will treat every request as new. No session, no history.
4. Cacheable:-----
The webpage you are reading here is also a cached version of the HTML page.
Caching brings performance improvement for the client-side and better scope for
scalability for a server because the load has reduced.
5. Layered system:-----
REST allows you to use a layered system architecture where you deploy the APIs
on server A, and store data on server B and authenticate requests in Server C.
For example. A client cannot ordinarily tell whether it is connected directly to the
end server or an intermediary along the way.