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What is the web? How is the web made? This section delves into '''core components''' of the world-wide-web. It is likely you use the web every day. Like everything in computer science, we want you to understand the '''depth of this topic'''.  
Modeling and simulation (M&S) is the use of models – physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process – as a basis for simulations – methods for implementing a model (either statically or) over time – to develop data as a basis for managerial or technical decision making. M&S supports analysis, experimentation, and training. As such, M&S can facilitate understanding a system's behavior without actually testing the system in the real world.<ref>https://en.wikipedia.org/wiki/Modeling_and_simulation</ref>


== The big ideas ==  
== The big ideas ==


== Standards ==
=== The Basic Model ===  
* Distinguish between the internet and World Wide Web (web).
 
* Describe how the web is constantly evolving.
* [[Computer modeling]]
* Identify the characteristics of the following: HTTP, HTTPS, HTML, URL, XML, XSLT, CSS.
* [[Systems that can be modelled]]
* Identify the characteristics of a uniform resource identifier (URI) URL.
* [[Variables required to model a given system]]
* Describe the purpose of a URL.
* [[Limitations of computer (mathematical) models]]
* Describe how a domain name server functions.
* [[Sensible grouping for collections of data items]]
* Identify the characteristics of the internet protocol (IP) transmission control protocol (TCP) file transfer protocol (FTP).
* [[Test-cases to evaluate a model]]
* Outline the different components of a web page.
* [[Effectiveness of test-cases in a specified situation]]
* Explain the importance of protocols and standards on the web.
* [[Correctness of a model]]
* Describe the different types of web page.
 
* Explain the differences between a static web page and a dynamic web page.
=== Simulations ===
* Explain the functions of a browser.
 
* Evaluate the use of client-side scripting and server-side scripting in web pages.
 
* Describe how web pages can be connected to underlying data sources.
* [[Define the term simulation]]
* Describe the function of the common gateway interface (CGI).
* [[Model and a simulation]]
* Evaluate the structure of different types of web pages.
* [[Rules of simulations]]
* [[Rules and data representations]]
* [[Simple models]]
* [[Test-cases to evaluate simulations]]
* [[Software and hardware required for a simulation]]
* [[Changes in rules, formulae and algorithms]]
* [[Examples of simulations that involve changes in rules, formulae and algorithms]]
* [[Changes in data collection that could improve the model or simulation]]
* [[Reliability of a simulation]]
* [[Advantages and disadvantages of simulation]]
* [[Advantages and disadvantages of using a simulation for making predictions]]
 
=== Visualizations ===
 
* [[Define the term visualization]]
* [[Two-dimensional use of visualization]]
* [[Memory needs of 2D visualization]]
* [[Three-dimensional use of visualization]]
* [[Images in memory and the 3D visualization]]
* [[Time and memory considerations of 3D animation]]
 
=== Communication modeling and simulation (HL only) ===


* Define the term search engine.
* [[Genetic Algorithms]]
* Distinguish between the surface web and the deep web.
* [[Structure of neural networks]]
* Outline the principles of searching algorithms used by search engines.
* [[Neural network modeling]]
* Describe how a web crawler functions.
* [[Neural networks, recognizing patterns]]
* Discuss the relationship between data in a meta-tag and how it is accessed by a web crawler.
* [[Compare different ways in which neural networks can be used to recognize patterns]]
* Discuss the use of parallel web crawling.
* [[Key structures of natural language]]
* Outline the purpose of web-indexing in search engines.
* [[Human and machine learning when related to language]]
* Suggest how web developers can create pages that appear more prominently in search engine results.
* [[Evolution of modern machine translators]]
* Describe the different metrics used by search engines.
* [[Chatbots]]
*  Explain why the effectiveness of a search engine is determined by the assumptions made when developing it.
* [[Natural language processing]]
* Discuss the use of white hat and black hat search engine optimization.
* Outline future challenges to search engines as the web continues to grow.


*  Define the terms: mobile computing, ubiquitous computing, peer-2-peer network, grid computing.
== helpful resources ==
*  Compare the major features of: mobile computing ubiquitous computing peer-2-peer network grid computing.
* Distinguish between interoperability and open standards.
* Describe the range of hardware used by distributed networks.
* Explain why distributed systems may act as a catalyst to a greater decentralization of the web.
*  Distinguish between lossless and lossy compression.
*  Evaluate the use of decompression software in the transfer of information.


* Discuss how the web has supported new methods of online interaction such as social networking.
* https://sim4edu.com/
* Describe how cloud computing is different from a client-server architecture.
*  Discuss the effects of the use of cloud computing for specified organizations.
* Discuss the management of issues such as copyright and intellectual property on the web.
*  Describe the interrelationship between privacy, identification and authentication.
*  Describe the role of network architecture, protocols and standards in the future development of the web.
* Explain why the web may be creating unregulated monopolies.
*  Discuss the effects of a decentralized and democratic web.  




* Describe how the web can be represented as a directed graph.  
== Standards ==
* Outline the difference between the web graph and sub-graphs.  
* Define the term computer modelling.
* Describe the main features of the web graph such as bowtie structure, strongly connected core (SCC), diameter.  
* Identify a system that can be modelled.
* Explain the role of graph theory in determining the connectivity of the web.  
* Identify the variables required to model a given system.
* Explain that search engines and web crawling use the web graph to access information.  
* Describe the limitations of computer (mathematical) models.
* Discuss whether power laws are appropriate to predict the development of the web.  
* Outline sensible grouping for collections of data items, including sample data.
* Design test-cases to evaluate a model.
* Discuss the effectiveness of a test-case in a specified situation.
* Discuss the correctness of a model by comparing generated results with data that were observed in the original problem.
* Define the term simulation.
* Explain the difference between a model and a simulation.
* Describe rules that process data appropriately and that produce results.
* Discuss rules and data representations and organization.
* Construct simple models that use different forms of data representation and organization.
* Design test-cases to evaluate a simulation program.
* Outline the software and hardware required for a simulation.
* Describe changes in rules, formulae and algorithms that would improve the correspondence between results and observed data.
* Construct examples of simulations that involve changes in rules, formulae and algorithms.
* Describe changes in data collection that could improve the model or simulation.
* Discuss the reliability of a simulation by comparing generated results with data that were observed in the original problem.
* Outline the advantages and disadvantages of simulation in a given situation rather than simply observing a real-life situation.
* Discuss advantages and disadvantages of using a simulation for making predictions.
* Define the term visualization.
* Identify a two-dimensional use of visualization.
* Outline the memory needs of 2D visualization
* Identify a three-dimensional use of visualization.
* Outline the relationship between the images in memory and the 3D visualization.
* Discuss the time and memory considerations of 3D animation in a given scenario.


=== HL standards ===


* Distinguish between the text-web and the multimedia-web.  
* Outline the use of genetic algorithms.
* Describe the aims of the semantic web.  
* Outline the structure of neural networks.
* Distinguish between an ontology and folksonomy.  
* Compare applications that use neural network modelling.
* Describe how folksonomies and emergent social structures are changing the web.  
* Compare different ways in which neural networks can be used to recognize patterns.
* Explain why there needs to be a balance between expressivity and usability on the semantic web.  
* Identify the key structures of natural language.
* Evaluate methods of searching for information on the web.  
* Discuss the differences between human and machine learning when related to language.
* Distinguish between ambient intelligence and collective intelligence.  
* Outline the evolution of modern machine translators.
* Discuss how ambient intelligence can be used to support people.  
* Describe the role of chatbots to simulate conversation.
* Explain how collective intelligence can be applied to complex issues.
* Discuss the latest advances in natural language processing.


== References ==
== References ==

Latest revision as of 09:38, 9 December 2021

Modeling & Simulation[1]

Modeling and simulation (M&S) is the use of models – physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process – as a basis for simulations – methods for implementing a model (either statically or) over time – to develop data as a basis for managerial or technical decision making. M&S supports analysis, experimentation, and training. As such, M&S can facilitate understanding a system's behavior without actually testing the system in the real world.[2]

The big ideas[edit]

The Basic Model[edit]

Simulations[edit]

Visualizations[edit]

Communication modeling and simulation (HL only)[edit]

helpful resources[edit]


Standards[edit]

  • Define the term computer modelling.
  • Identify a system that can be modelled.
  • Identify the variables required to model a given system.
  • Describe the limitations of computer (mathematical) models.
  • Outline sensible grouping for collections of data items, including sample data.
  • Design test-cases to evaluate a model.
  • Discuss the effectiveness of a test-case in a specified situation.
  • Discuss the correctness of a model by comparing generated results with data that were observed in the original problem.
  • Define the term simulation.
  • Explain the difference between a model and a simulation.
  • Describe rules that process data appropriately and that produce results.
  • Discuss rules and data representations and organization.
  • Construct simple models that use different forms of data representation and organization.
  • Design test-cases to evaluate a simulation program.
  • Outline the software and hardware required for a simulation.
  • Describe changes in rules, formulae and algorithms that would improve the correspondence between results and observed data.
  • Construct examples of simulations that involve changes in rules, formulae and algorithms.
  • Describe changes in data collection that could improve the model or simulation.
  • Discuss the reliability of a simulation by comparing generated results with data that were observed in the original problem.
  • Outline the advantages and disadvantages of simulation in a given situation rather than simply observing a real-life situation.
  • Discuss advantages and disadvantages of using a simulation for making predictions.
  • Define the term visualization.
  • Identify a two-dimensional use of visualization.
  • Outline the memory needs of 2D visualization
  • Identify a three-dimensional use of visualization.
  • Outline the relationship between the images in memory and the 3D visualization.
  • Discuss the time and memory considerations of 3D animation in a given scenario.

HL standards[edit]

  • Outline the use of genetic algorithms.
  • Outline the structure of neural networks.
  • Compare applications that use neural network modelling.
  • Compare different ways in which neural networks can be used to recognize patterns.
  • Identify the key structures of natural language.
  • Discuss the differences between human and machine learning when related to language.
  • Outline the evolution of modern machine translators.
  • Describe the role of chatbots to simulate conversation.
  • Discuss the latest advances in natural language processing.

References[edit]