4. Data Visualization Section 1: DV Evolution of data manipulation in web applications. “Humans are, by nature and history, are dwellers of low-dimensional worlds” compared to the available tens of thousands of dimensions, “as we see today in e-commerce, the web and scientific observations”. (Fayyad et al., 2002, Pg.3) Exponential growth of Web. Section 2: AI Section 3: Real Time Eutopian View Const. Approach
5. Wide Area Information Servers (WAIS), invented by Brewster Kahle, released by Thinking Machines Corporation . Gopher released by Paul Lindner and Mark P. McCahill from the University of Minnesota World-Wide Web (WWW) released by CERN; Tim Berners-Lee developer . First Web server is nxoc01.cern.ch, launched in Nov 1990 and later renamed info.cern.ch. 1991 To 2006
6.
7. Advanced DV Resources LAST.FORWARD is a downloadable, open-source tool to visualize any Last.fm user’s social network, including relationships between other users.
8. Advanced DV Resources LAST.FORWARD is a downloadable, open-source tool to visualize any Last.fm user’s social network, including relationships between other users.
9. Advanced DV Resources Visualizing Information Flow in Scienceincludes a set of four visualizations showing relationships between citations in scholarly journals that are used to evaluate the importance of each journal.
11. Artificial Intelligence Section 1: DV Traditional Concept: As stated by McArthy,“Thestudy is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it”(Halpin,2002). Section 2: AI Section 3: Real Time Semantic Web: “The Semantic Web effort is in effect a revival of many of the claims that were given at the origins of artificial intelligence”(Halpin,2002) Google Wave Const. Approach Intelligent Agents “Capable of making autonomous decisions, “without the direct intervention of humans (or other agents), and should have control over its own actions and internal state.”(Jennings & Wooldridge,2002)
13. Today’s AI As mentioned by Khan, “One example is the ANN, Falcon. Designed by San Diego-based HNC Software, Falcon maintains a profile of how, when, and where customers use their credit cards and, fro m this, develops an ability to discern ‘deviant’ b e h a v i o u r. To date, this system is used by nine of the ten leading US credit card companies: they claim it has improved fraud detection rates from 30–70%” (Arnall,2003).
22. “Google Wave is an online tool for real-time communication and collaboration. A wave can be both a conversationand a document where people can discuss and work together using richly formatted text, photos, videos, maps, and more.”(Google,2009) Google Wave
23. References Fayyad, U., Grinstein, G. G., & Weirse, A. (Eds.). (2002). Information Visualization in Data Mining and Knowledge Discovery. 525 B street, Suite 1900, San Diego,CA94104-3205: Academic Press. Hobbes' Internet Timeline. (2006). Retrieved December 01, 2009 from The definativeARPAnet& Internet history: http://citationcenter.net/ctool.php5?style=apa&ed=5&type=web_standard &online=0&odb=0&owww=0. Flowing Data. (2008). Retrieved November 30, 2009 from Data Visualization and Statistics: http://flowingdata.com/. Change Player Size Watch this video in a new windowMicrosoft Office Labs 2019 Vision Montage. (2009). [Digital Versatile Disc]. YouTube: YouTube. Application of Intelligent Agents. (1998). Retrieved September 29, 2009 from Queen Mary & Westfield College: http://agents.umbc.edu/introduction/jennings98.pdf. Arnall, A. H. (2003). Future Technologies,Today's Choices-N a n o t e c h n o l o g y, Artificial Intelligence and Robotics;ATechnical,political,and institutional map of emerging technologies. In A report for the Greenpeace Environmental Trust(Vol. 1, pp. 1-72). Canonbury Villas: Greenpeace Environmental Tr u s t.
24. References REAL-TIME SYSTEMS: AN INTRODUCTION and State-Of-The-Art. (2008). Retrieved February 14, 2009 from CSE: http://www.cse.unt.edu/~rakl/kah08.pdf. The Semantic Web. (2006). Retrieved December 06, 2009 from The Origins of Artificial Intelligence Redux: http://www.ibiblio.org/hhalpin/homepage/publications/airedux.pdf.
Hinweis der Redaktion
What was the need for data visualization ..how did data visualization evolve and what factor played imp. In its evolution.Evolutionary steps:1 static web 2 dynamic web 3 collaborative web ___>future visual webHumans have the ability to make autonomous decisions.Although web was created by the army for communication I find this as one of the primary reasons for the exponential growth of internet and its applications.Dimension here can mean the traditional 3 dimensions of life or the dimension of parameters of parameters used in any calculations.
Data minign- machanized process of identifying or discovering useful structure in data.
Before with last.fm people use to browse profiles or watch frds playlist and select music to play and add to their list …using this relationship map and intelligent agent called scrobbler this is done automatically and kind of gives an impression of pandora but with collaboration in social network form.
Before with last.fm people use to browse profiles or watch frds playlist and select music to play and add to their list …using this relationship map and intelligent agent called scrobbler this is done automatically and kind of gives an impression of pandora but with collaboration in social network form.
http://well-formed.eigenfactor.org/ Interactive visualizations based on the Eigenfactor™ Metrics and hierarchical clustering to explore emerging patterns in citation networks.Similarly there are many other projects on data visualization like TED sphere based on semantics , live wetherforcast based on real time systems, word mappings such as the ones keith showed us in search engines and many others.
http://well-formed.eigenfactor.org/ Interactive visualizations based on the Eigenfactor™ Metrics and hierarchical clustering to explore emerging patterns in citation networks.Similarly there are many other projects on data visualization like TED sphere based on semantics , live wetherforcast based on real time systems, word mappings such as the ones keith showed us in search engines and many others.
Traditional concepts of AI were largely based on methodologies such as pattern recognition and was system designed to mimic human brain and thinking methodology. But the biggest problem with this was that the understanding of the functioning of the human brain and common sense database was not accurate enough. Autonomous decisions could not be made.It could do tasks like build cars…uniform paintining from input from sensors ..but when it came to a volumetric data analysis and implications of common sense logic the system broke and needed super computing. Formalization of common sense knowledge was still taking place.-Very domain specific.Semantic Web : Revives the concepts of old AI (which were largely based on complex equations with thousands of weights and mathematic logic and signal matching frequencies or input parameters)…but semantic web is definig this concept in a new way by adding meaning to every data present on the web theoretically. Using meta tags and agents tht crawl the web to understand applications of their own species eventually mimicing human languages and decisn making. This was possible only after the exponenetialgrwoth of the internet since its largely web based.Intelligent Agents : Are different from Semantic web that they are like bots which are specifically designed to perform a individual task but with autonomous decision making capabilities. Do not need direct intervention from humans. Take one input and manipulate the input using concepts from behavioral science.
P e rhaps the most ambitious examples of AI development that are currently occurring in this area relate to computer learning.Another one is DARPA-p robably the most challenging in ANNdevelopment today – is being undertakenby DARPA, who have launched an initiativeto develop a cognitive (i.e. thinking) system.The aim of this system is to reason in avariety of ways, learn from experience,and adapt to surprises. In the words ofMelymuka (2002): ‘It will be aware of itsbehaviour and explain itself…It will be ableto anticipate diff e rent scenarios and pre d i c tand plan for novel futures.’ The ultimateaim is to develop cognitive systems capableof assisting or replacing soldiers onh a z a rdous duty or civilians respondingto toxic spills or disasters.
The different aspects which play an important role in the development of AI and are used in Research and development are shown in the above plot. For a strong AI agent or system to be succeful it requires a platform with advanced omputing speed and capable of handling chuunks of data parellely for making complex decisions and eliminate secondary solutions to a problem with out haing to runexecute all possible scenarious. This platform is being looked at potentially in the orm of bio-materials –Cells which can hold gigabytes of data, replication of structural neral networks ,nano-technology materials such as Nano tubes,C60 atom discovered in 1985. This is direct result of the research hype which took off intth 60 70s and 80s.Also for a succesful AI model requires enhanced collaboration for resoning plans ,programs and actions. Reasoning and decision making is achieved using Algorthithim and gentic programming based on information provided by Web based weker AI agents.
Hard real-time systems have very strict timeconstraints, in which missing the specified deadline isunacceptable. The system must be designed to guaranteeall time constraints. Every resource management systemsuch as the scheduler, input–output (I/O) manager, andcommunications, must work in the correct order to meet thespecified time constraints.Soft real-time systems also have time constraints; however,missing some deadline may not lead to catastrophicfailure of the system. Thus, soft real-time systems aresimilar to hard real-time systems in their infrastructurerequirements, but it is not necessary that every time constraintbe met. In other words, some time constraints arenot strict, but they are nonetheless important. A soft realtimesystem is not equivalent to non-real-time system,because the goal of the system is still to meet as manydeadlines as possible.
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