1.
Tennis
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Tennis is a racket sport that can be played individually against a single opponent or between two teams of two players each. Each player uses a racket that is strung with cord to strike a hollow rubber ball covered with felt over or around a net. The object of the game is to play the ball in such a way that the opponent is not able to play a valid return, the player who is unable to return the ball will not gain a point, while the opposite player will. Tennis is an Olympic sport and is played at all levels of society, the sport can be played by anyone who can hold a racket, including wheelchair users. The modern game of tennis originated in Birmingham, England, in the late 19th century as lawn tennis and it had close connections both to various field games such as croquet and bowls as well as to the older racket sport of real tennis. The rules of tennis have changed little since the 1890s, two exceptions are that from 1908 to 1961 the server had to keep one foot on the ground at all times, and the adoption of the tiebreak in the 1970s. Tennis is played by millions of players and is also a popular worldwide spectator sport. Historians believe that the ancient origin lay in 12th century northern France. Louis X of France was a player of jeu de paume, which evolved into real tennis. Louis was unhappy with playing tennis outdoors and accordingly had indoor, in due course this design spread across royal palaces all over Europe. Because of the accounts of his death, Louis X is historys first tennis player known by name. Another of the enthusiasts of the game was King Charles V of France. It wasnt until the 16th century that rackets came into use, and the game began to be called tennis, from the French term tenez, an interjection used as a call from the server to his opponent. It was popular in England and France, although the game was played indoors where the ball could be hit off the wall. Henry VIII of England was a big fan of this game, during the 18th century and early 19th century, as real tennis declined, new racket sports emerged in England. This in turn led to the codification of rules for many sports, including lawn tennis, most football codes, lawn bowls. In 1872, along with two doctors, they founded the worlds first tennis club in Leamington Spa. Evans, turfgrass agronomist, Sports historians all agree that deserves much of the credit for the development of modern tennis, according to Honor Godfrey, museum curator at Wimbledon, Wingfield popularized this game enormously
2.
Information mapping
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Information mapping is a research-based method for writing clear and user focused information, based on the audiences needs and the purpose of the information. The method is applied primarily to designing and developing business and technical communications and it is used as a content standard within organizations throughout the world. The information mapping method is a methodology used to analyze, organize and present information based on an audiences needs. The method applies to all matter and media technology. Information mapping provides a number of tools for analyzing, organizing and presenting information, Some of Robert E. Horns best-known work was his development of the theory of information types. Horn identified six types of information that account for all the content of business. They consist of clearly outlined information units that take into account how much information a reader is able to assimilate, there is an essential difference between an information unit and the traditional text paragraph. A block is limited to a topic and consists of a single type of information. Blocks are grouped into maps, and each map consists only of relevant blocks, the hierarchical approach to structuring information greatly facilitates electronic control of content via content management systems and knowledge management systems. The information mapping method offers advantages to writers and readers, as well as to an entire organization and this means that the user doesnt have to worry about the layout, but can focus on the content of the document. FS Pro also allows the user to achieve consistency easily both within a document and among different documents, Information mapping was developed in the late 20th century by Robert E. Horn, a researcher in the cognitive and behavioral sciences. Horn was interested in visual presentation of information to improve accessibility, comprehension, Horns development of the information mapping method has won him recognition from the International Society for Performance Improvement and the Association for Computing Machinery. It also facilitates repurposing for publication in different formats, doubts have been raised over the strength of the research Horn uses to justify some of his principles. For instance, his chunking principle requires lists, paragraphs, sub-sections and sections in a document to contain no more than 7±2 chunks of information. Horn does not state where he got this principle, but an Information Mapping website stated that the principle is based on George A. Millers 1956 research. Miller did write a paper in 1956 called The Magical Number Seven, Plus or Minus Two, Some Limits on our Capacity for Processing Information, miller himself said that his research had nothing to do with writing. Robert E. Horn Brian Willison Robert E. Horn, Mapping Hypertext, The Analysis, Organization, and Display of Knowledge for the Next Generation of On-Line Text and Graphics. Examining the Evidence on Information Mappings Method of High-Performance Communication, note, This publication is available for download on Horns website, Chapter One and Chapter Two
3.
Data visualization
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Data visualization or data visualisation is viewed by many disciplines as a modern equivalent of visual communication. A primary goal of data visualization is to communicate clearly and efficiently via statistical graphics, plots. Numerical data may be encoded using dots, lines, or bars, effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable, users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic follows the task. Tables are generally used where users will look up a specific measurement, Data visualization is both an art and a science. It is viewed as a branch of statistics by some. Increased amounts of data created by Internet activity and an number of sensors in the environment are referred to as big data or Internet of things. Processing, analyzing and communicating this data present ethical and analytical challenges for data visualization, the field of data science and practitioners called data scientists help address this challenge. Data visualization refers to the used to communicate data or information by encoding it as visual objects contained in graphics. The goal is to communicate clearly and efficiently to users. It is one of the steps in analysis or data science. According to Friedman the main goal of data visualization is to communicate clearly and effectively through graphical means. It doesnt mean that data needs to look boring to be functional or extremely sophisticated to look beautiful. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information. Indeed, Fernanda Viegas and Martin M. Wattenberg have suggested that an ideal visualization should not only communicate clearly, Data visualization is closely related to information graphics, information visualization, scientific visualization, exploratory data analysis and statistical graphics. In the new millennium, data visualization has become an area of research, teaching. According to Post et al. it has united scientific and information visualization, professor Edward Tufte explained that users of information displays are executing particular analytical tasks such as making comparisons or determining causality. The design principle of the information graphic should support the analytical task, indeed graphics can be more precise and revealing than conventional statistical computations
4.
Educational psychology
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Educational psychology can in part be understood through its relationship with other disciplines. It is informed primarily by psychology, bearing a relationship to that discipline analogous to the relationship between medicine and biology and it is also informed by neuroscience. Educational psychology both draws from and contributes to science and the learning sciences. The field of educational psychology involves the study of memory, conceptual processes, Educational psychology has been built upon theories of operant conditioning, functionalism, structuralism, constructivism, humanistic psychology, Gest. psychology, and information processing. Educational psychology has seen growth and development as a profession in the last twenty years. However, school psychology itself has built a new profession based upon the practices and theories of several psychologists among many different fields. Educational psychology is a new and growing field of study. Though it can date back as early as the days of Plato and Aristotle and these topics are important to education and as a result it is important to understanding human cognition, learning, and social perception. Educational psychology dates back to the time of Aristotle and Plato, some other educational topics they spoke about were the effects of music, poetry, and the other arts on the development of individual, role of teacher, and the relations between teacher and student. Plato saw knowledge as an ability, which evolves through experience. Such a statement has evolved into an argument of nature vs. nurture in understanding conditioning and learning today. Aristotle observed the phenomenon of association and his four laws of association included succession, contiguity, similarity, and contrast. His studies examined recall and facilitated learning processes, John Locke followed by taking issue with Platos theory of innate learning processes. In place of this theory, he introduced a new theory of learning based on the tabula rasa. Locke explained that learning took place primarily through experience, and we were all born without knowledge and this doctrine is known as empiricism, the view that knowledge is primarily built on learning and experience. In the late 1600s, John Locke advanced the hypothesis that people primarily from external forces. He believed that the mind was like a tablet. Juan Vives proposed induction as the method of study and believed in the observation and investigation of the study of nature
5.
Educational technology
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Educational technology refers to the use of both physical hardware and educational theoretics. It encompasses several domains, including learning theory, computer-based training, online learning, and, Technology in education and education technology are two different concepts which deal with the use of technological tools and innovative educational strategies respectively. Educational technology is not restricted to high technology, Education technology is anything that enhances classroom learning in the utilization of blended or online learning. However, modern educational technology is an important part of society today. Each of these terms has had its advocates, who point up potential distinctive features. In practice, as technology has advanced, the narrowly defined terminological aspect that was initially emphasized by name has blended into the general field of educational technology. Initially, virtual learning as defined in a semantic sense implied entering an environmental simulation within a virtual world. In practice, an education course refers to any instructional course in which all. As a further example, ubiquitous learning emphasizes an omnipresent learning milieu, smart learning is a component of the smart city concept. Helping people learn in ways that are easier, faster, surer, or less expensive can be traced back to the emergence of early tools. Various types of abacus have been used, writing slates and blackboards have been used for at least a millennium. From their introduction, books and pamphlets have held a prominent role in education, from the early twentieth century, duplicating machines such as the mimeograph and Gestetner stencil devices were used to produce short copy runs for classroom or home use. The first all multiple choice, large-scale assessment was the Army Alpha, used to assess the intelligence, further large-scale use of technologies was employed in training soldiers during and after WWII using films and other mediated materials, such as overhead projectors. The concept of hypertext is traced to the description of memex by Vannevar Bush in 1945, slide projectors were widely used during the 1950s in educational institutional settings. Cuisenaire rods were devised in the 1920s and saw use from the late 1950s. Stanfords Education Program for Gifted Youth is descended from early experiments. In 1971, Ivan Illich published an influential book called, Deschooling Society. By the mid-1980s, accessing course content became possible at many college libraries, in computer-based training or computer-based learning, the learning interaction was between the student and computer drills or micro-world simulations
6.
Infographic
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Information graphics or infographics are graphic visual representations of information, data or knowledge intended to present information quickly and clearly. They can improve cognition by utilizing graphics to enhance the human visual system’s ability to see patterns, similar pursuits are information visualization, data visualization, statistical graphics, information design, or information architecture. Infographics have evolved in recent years to be for mass communication, Isotypes are an early example of infographics conveying information quickly and easily to the masses. Infographics have been around for years and recently the increase of a number of easy-to-use. Social media sites such as Facebook and Twitter have also allowed for individual infographics to be spread among people around the world. Infographics are widely used in the age of short attention span, in newspapers, infographics are commonly used to show the weather, as well as maps, site plans, and graphs for summaries of data. Some books are almost entirely made up of information graphics, such as David Macaulays The Way Things Work, the Snapshots in USA Today are also an example of simple infographics used to convey news and current events. Public transportation maps, such as those for the Washington Metro, public places such as transit terminals usually have some sort of integrated signage system with standardized icons and stylized maps. Indeed graphics can be more precise and revealing than conventional statistical computations, in 1626, Christoph Scheiner published the Rosa Ursina sive Sol, a book that revealed his research about the rotation of the sun. Infographics appeared in the form of demonstrating the Sun’s rotation patterns. In 1786, William Playfair, an engineer and political economist, to represent the economy of 18th Century England, Playfair used statistical graphs, bar charts, line graphs, area charts, and histograms. In his work, Statistical Breviary, he is credited with introducing the first pie chart, around 1820, modern geography was established by Carl Ritter. His maps included shared frames, agreed map legends, scales, repeatability, such a map can be considered a supersign which combines sign systems—as defined by Charles Sanders Peirce—consisting of symbols, icons, indexes as representations. Other examples can be seen in the works of geographers Ritter and Alexander von Humboldt, in 1857, English nurse Florence Nightingale used information graphics to persuade Queen Victoria to improve conditions in military hospitals. The principal one she used was the Coxcomb chart, a combination of stacked bar and pie charts, depicting the number,1861 saw the release of an influential information graphic on the subject of Napoleons disastrous march on Moscow. James Joseph Sylvester introduced the term graph in 1878 in the scientific magazine Nature and these were also some of the first mathematical graphs. The Cologne Progressives developed an approach to art which focused on communicating information. Here simple images were used to represent data in a structured way, following the victory of Austrofascism in the Austrian Civil War, the team moved to the Netherlands where they continued their work rebranding it Isotypes
7.
Visualization (graphics)
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Visualization or visualisation is any technique for creating images, diagrams, or animations to communicate a message. Visualization through visual imagery has been a way to communicate both abstract and concrete ideas since the dawn of humanity. Examples from history include cave paintings, Egyptian hieroglyphs, Greek geometry, Visualization today has ever-expanding applications in science, education, engineering, interactive multimedia, medicine, etc. Typical of an application is the field of computer graphics. The invention of computer graphics may be the most important development in visualization since the invention of perspective in the Renaissance period. The development of animation also helped advance visualization, the use of visualization to present information is not a new phenomenon. It has been used in maps, scientific drawings, and data plots for over a thousand years, examples from cartography include Ptolemys Geographia, a map of China, and Minards map of Napoleons invasion of Russia a century and a half ago. Most of the learned in devising these images carry over in a straightforward manner to computer visualization. Edward Tufte has written three critically acclaimed books that explain many of these principles, Computer graphics has from its beginning been used to study scientific problems. However, in its early days the lack of power often limited its usefulness. The recent emphasis on visualization started in 1987 with the publication of Visualization in Scientific Computing, TV also offers scientific visualizations when it shows computer drawn and animated reconstructions of road or airplane accidents. Some of the most popular examples of scientific visualizations are computer-generated images that show real spacecraft in action, out in the void far beyond Earth, dynamic forms of visualization, such as educational animation or timelines, have the potential to enhance learning about systems that change over time. Apart from the distinction between interactive visualizations and animation, the most useful categorization is probably between abstract and model-based scientific visualizations, the abstract visualizations show completely conceptual constructs in 2D or 3D. These generated shapes are completely arbitrary, the model-based visualizations either place overlays of data on real or digitally constructed images of reality or make a digital construction of a real object directly from the scientific data. Scientific visualization is usually done with specialized software, though there are a few exceptions, there are also many proprietary software packages of scientific visualization tools. Data visualization is a subcategory of visualization dealing with statistical graphics. Scientific visualization focuses and emphasizes the representation of order data using primarily graphics. It is an important part of visualization and maybe the first one, as the visualization of experiments
8.
Mental model
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A mental model is an explanation of someones thought process about how something works in the real world. It is a representation of the world, the relationships between its various parts and a persons intuitive perception about his or her own acts and their consequences. Mental models can help shape behaviour and set an approach to solving problems, a mental model is a kind of internal symbol or representation of external reality, hypothesized to play a major role in cognition, reasoning and decision-making. Kenneth Craik suggested in 1943 that the mind constructs small-scale models of reality that it uses to anticipate events, jay Wright Forrester defined general mental models as, The image of the world around us, which we carry in our head, is just a model. Nobody in his head all the world, government or country. He has only selected concepts, and relationships between them, and uses those to represent the real system, in psychology, the term mental models is sometimes used to refer to mental representations or mental simulation generally. At other times it is used to refer to models and reasoning and to the mental model theory of reasoning developed by Philip Johnson-Laird. The term mental model is believed to have originated with Kenneth Craik in his 1943 book The Nature of Explanation. Georges-Henri Luquet in Le dessin enfantin, published in 1927 by Alcan, Paris, argued that children construct internal models, Philip Johnson-Laird published Mental Models, Towards a Cognitive Science of Language, Inference and Consciousness in 1983. In the same year, Dedre Gentner and Albert Stevens edited a collection of chapters in a book also titled Mental Models, since then, there has been much discussion and use of the idea in human-computer interaction and usability by researchers including Donald Norman and Steve Krug. Walter Kintsch and Teun A. van Dijk, using the term situation model, one view of human reasoning is that it depends on mental models. In this view, mental models can be constructed from perception, imagination, in this respect, they are a little like pictures in the picture theory of language described by philosopher Ludwig Wittgenstein in 1922. Philip Johnson-Laird and Ruth M. J. Byrne developed a theory of models which makes the assumption that reasoning depends, not on logical form. Mental models are based on a set of fundamental assumptions. Each mental model represents a possibility, a mental model represents one possibility, capturing what is common to all the different ways in which the possibility may occur. Mental models are iconic, i. e. each part of a model corresponds to part of what it represents. However, mental models can represent what is false, temporarily assumed to be true, for example, in the case of counterfactual conditionals, People infer that a conclusion is valid if it holds in all the possibilities. Procedures for reasoning with mental models rely on counter-examples to refute invalid inferences, reasoners focus on a subset of the possible models of multiple-model problems, often just a single model
9.
Argument map
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In informal logic and philosophy, an argument map or argument diagram is a visual representation of the structure of an argument. An argument map typically includes the key components of the argument, traditionally called the conclusion, Argument maps can also show co-premises, objections, counterarguments, rebuttals, and lemmas. There are different styles of argument map but they are functionally equivalent and represent an arguments individual claims. Argument maps are used in the context of teaching and applying critical thinking. The purpose of mapping is to uncover the structure of arguments, identify unstated assumptions, evaluate the support an argument offers for a conclusion. Argument maps are designed to support deliberation of issues, ideas. An argument map is not to be confused with a map or a mind map. There is no consensus as to whether the conclusion should be at the top of the tree with the leading up to it or whether it should be at the bottom with the reasons leading down to it. Another variation diagrams an argument from left to right, according to Doug Walton and colleagues, an argument map has two basic components, One component is a set of circled numbers arrayed as points. Each number represents a proposition in the argument being diagrammed, the other component is a set of lines or arrows joining the points. The whole network of points and lines represents a kind of overview of the reasoning in the given argument, where several premises or groups of premises lead to a final conclusion the argument might be described as convergent. This is distinguished from a divergent argument where a single premise might be used to two separate conclusions. Intermediate conclusions or sub-conclusions, where a claim is supported by another claim that is used in turn to some further claim. An argument with this structure is called a complex argument. If there is a chain of claims containing at least one intermediate conclusion. Each of these structures can be represented by the equivalent box, in the following diagram, the contention is shown at the top, and the boxes linked to it represent supporting reasons, which comprise one or more premises. The green arrow indicates that the two support the contention, Argument maps can also represent counterarguments. Monroe Beardsleys 1950 book Practical Logic recommended the following procedure, Separate statements by brackets, put circles around the logical indicators
10.
Cladistics
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Therefore, members of a group are assumed to share a common history and are considered to be closely related. The techniques and nomenclature of cladistics have been applied to other disciplines, Cladistics in the original sense refers to a particular set of methods used in phylogenetic analysis, although it is now sometimes used to refer to the whole field. What is now called the cladistic method appeared as early as 1901 with a work by Peter Chalmers Mitchell for birds and subsequently by Robert John Tillyard in 1921, Hennig referred to his own approach as phylogenetic systematics. Phenetics was championed at this time by the numerical taxonomists Peter Sneath and Robert Sokal, originally conceived, if only in essence, by Willi Hennig in a book published in 1950, cladistics did not flourish until its translation into English in 1966. Today, cladistics is the most popular method for constructing phylogenies from morphological, unlike phenetics, cladistics is specifically aimed at reconstructing evolutionary histories. The way for computational phylogenetics was paved by phenetics, a set of commonly used from the 1950s to 1980s. Phenetics did not try to reconstruct phylogenetic trees, rather, it tried to build dendrograms from similarity data, the cladistic method interprets each character state transformation implied by the distribution of shared character states among taxa as a potential piece of evidence for grouping. The outcome of an analysis is a cladogram – a tree-shaped diagram that is interpreted to represent the best hypothesis of phylogenetic relationships. Cladists contend that these models are unjustified, every cladogram is based on a particular dataset analyzed with a particular method. Different datasets and different methods, not to mention violations of the mentioned assumptions, only scientific investigation can show which is more likely to be correct. Since the cladograms provide competing accounts of events, at most one of them is correct. Within the primates, all anthropoids are hypothesized to have had a common ancestor all of whose descendants were anthropoids, the prosimians, on the other hand, form a paraphyletic taxon. When two or more taxa that are not nested within each other share a plesiomorphy, it is a symplesiomorphy, symplesiomorphies do not mean that the taxa that exhibit that character state are necessarily closely related. For example, Reptilia is traditionally characterized by being cold-blooded, whereas birds are warm-blooded, an apomorphy or derived state is an innovation. It can thus be used to diagnose a clade – or even to help define a clade name in phylogenetic nomenclature, features that are derived in individual taxa are called autapomorphies. Autapomorphies express nothing about relationships among groups, clades are identified by synapomorphies, for example, the possession of digits that are homologous with those of Homo sapiens is a synapomorphy within the vertebrates. The tetrapods can be singled out as consisting of the first vertebrate with such digits homologous to those of Homo sapiens together with all descendants of this vertebrate and it is therefore inferred to have evolved by convergence or reversal. Both mammals and birds are able to maintain a constant body temperature
11.
Formal concept analysis
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In information science, formal concept analysis is a principled way of deriving a concept hierarchy or formal ontology from a collection of objects and their properties. The term was introduced by Rudolf Wille in 1984, and builds on applied lattice and order theory that was developed by Garrett Birkhoff and others in the 1930s. Formal concept analysis finds application in fields including data mining, text mining, machine learning, knowledge management, semantic web, software development, chemistry. In this way, formal concept analysis formalizes the semantic notions of extension and intension, the formal concepts of any formal context can—as explained below—be ordered in a hierarchy called more formally the contexts concept lattice. In French this is called a trellis de Galois because of the relation between the sets of objects and attributes is a Galois connection. The theory in its present form goes back to the early 1980s and its mathematical basis, however, was already created in the 1930s by Garrett Birkhoff as part of general lattice theory. Other previous approaches to the idea arose from various French research groups. Philosophical foundations of formal concept analysis refer in particular to Charles S. Peirce and this aim traces back to Hartmut von Hentig, who in 1972 pleaded for restructuring sciences in view of better teaching and in order to make sciences mutually available and more generally critiqueable. Hence, by its origins formal concept analysis aims at interdisciplinarity and it corrects the starting point of lattice theory during the development of formal logic in the 19th century. Then - and later in model theory - a concept as unary predicate had been reduced to its extent, now again, the philosophy of concepts should become less abstract by considering the intent. Hence, formal analysis is oriented towards the categories extension and intension of linguistics. Formal Concept Analysis aims at the clarity of concepts according to Charles S. Peirces pragmatic maxim by unfolding observable, in his late philosophy, Peirce assumed that logical thinking aims at perceiving reality, by the triade concept, judgement and conclusion. Mathematics is an abstraction of logic, develops patterns of possible realities and therefore may support rational communication. A formal context is a triple K =, where G is a set of objects, M is a set of attributes, the incidence relation can be regarded as a bipartite graph. Predicate gIm designates object gs having attribute m, for a subset A ⊆ G of objects and a subset B ⊆ M of attributes, one defines two derivation operators as follows, A =, and dually B =. Applying either derivation operator and then the other constitutes another operator, ″, with three properties, idempotent, A″″ = A″, monotonic, A1″ ⊆ A2″ whenever A1 ⊆ A2, and extensive, A ⊆ A″. Any operator satisfying those three properties is called an operator, and any set A such that A″ = A for a closure operator ″ is called closed under ″. In this matrix representation, each formal concept corresponds to a maximal submatrix all of elements equal TRUE
12.
Concept map
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A concept map or conceptual diagram is a diagram that depicts suggested relationships between concepts. It is a tool that instructional designers, engineers, technical writers. A concept map typically represents ideas and information as boxes or circles, the relationship between concepts can be articulated in linking phrases such as causes, requires, or contributes to. The technique for visualizing these relationships among different concepts is called concept mapping, Concept maps have been used to define the ontology of computer systems, for example with the object-role modeling or Unified Modeling Language formalism. In a concept map, each word or phrase connects to another, Concept maps are a way to develop logical thinking and study skills by revealing connections and helping students see how individual ideas form a larger whole. An example of the use of maps is provided in the context of learning about types of fuel. Concept maps were developed to enhance learning in the sciences. A well-made concept map grows within a context frame defined by an explicit focus question, some research evidence suggests that the brain stores knowledge as productions that act on declarative memory content, which is also referred to as chunks or propositions. Topic maps, Concept maps are similar to topic maps in that both allow to concepts or topics via graphs. Mind maps, Both concept maps and topic maps can be contrasted with mind mapping, another contrast between concept mapping and mind mapping is the speed and spontaneity when a mind map is created. A mind map reflects what you think about a single topic, a concept map can be a map, a system view, of a real system or set of concepts. Concept maps are more free form, as multiple hubs and clusters can be created, unlike mind maps, which fix on a single two centered approach. The technique of mapping was developed by Joseph D. Novak. Concept maps have their origin in the movement called constructivism. In particular, constructivists hold that learners construct knowledge. Novak taught students as young as six years old to make concept maps to represent their response to questions such as What is water. In his book Learning How to Learn, Novak states that a meaningful learning involves the assimilation of new concepts, various attempts have been made to conceptualize the process of creating concept maps. Ray McAleese, in a series of articles, has suggested that mapping is a process of off-loading, in this 1998 paper, McAleese draws on the work of Sowa and a paper by Sweller & Chandler
13.
Conceptual graph
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Conceptual graphs are a formalism for knowledge representation. In the first published paper on CGs, John F. Sowa used them to represent the conceptual schemas used in database systems, the first book on CGs applied them to a wide range of topics in artificial intelligence, computer science, and cognitive science. Since 1984, the model has developed along three main directions. In this approach, a formula in first-order logic is represented by a labeled graph, a linear notation, called the Conceptual Graph Interchange Format, has been standardized in the ISO standard for common logic. The diagram above is an example of the form for a conceptual graph. Each box is called a node, and each oval is called a relation node. The letters x and y, which are called coreference labels, show how the concept, a universal quantifier, represented @every*z in CGIF, would be represented forall in CLIF. Reasoning can be done by translating graphs into logical formulas, then applying a logical inference engine, another research branch continues the work on existential graphs of Charles Sanders Peirce, which were one of the origins of conceptual graphs as proposed by Sowa. The formalism is logically founded, i. e. it has a semantics in first-order logic, from a computational viewpoint, the graph homomorphism notion was recognized in the 1990s as a central notion, and complexity results and efficient algorithms have been obtained in several domains. COGITANT and COGUI are tools that implement the GBKR model, COGITANT is a library of C++ classes that implement most of the GBKR notions and reasoning mechanisms. COGUI is a user interface dedicated to the construction of a GBKR knowledge base. Similarity measure between syntactic parse trees can be done as an operation on the lists of sub-trees of these trees. The diagrams are representation of mapping between the syntax generalization level and semantics generalization level, chunking Resource Description Framework SPARQL Chein, Michel, Mugnier, Marie-Laure. Graph-based Knowledge Representation, Computational Foundations of Conceptual Graphs, the Logic System of Concept Graphs with Negation and Its Relationship to Predicate Logic. Sowa, John F. Conceptual Graphs for a Data Base Interface, IBM Journal of Research and Development. Sowa, John F. Conceptual Structures, Information Processing in Mind, galitsky, Boris, Dobrocsi, Gabor, de la Rosa, Josep Lluis, Kuznetsov, Sergei O. From Generalization of Syntactic Parse Trees to Conceptual Graphs, velardi, Paola, Pazienza, Maria Teresa, De Giovanetti, Mario. Conceptual graphs for the analysis and generation of sentences, IBM Journal of Research and Development
14.
Decision tree
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A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm, Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning. The paths from root to leaf represent classification rules, another use of decision trees is as a descriptive means for calculating conditional probabilities. Drawn from left to right, a tree has only burst nodes. Therefore, used manually, they can grow big and are then often hard to draw fully by hand. Traditionally, decision trees have been created manually — as the example shows — although increasingly. The decision tree can be linearized into decision rules, where the outcome is the contents of the node. In general, the rules have the form, if condition1 and condition2, Decision rules can be generated by constructing association rules with the target variable on the right. They can also denote temporal or causal relations, commonly a decision tree is drawn using flowchart symbols as it is easier for many to read and understand. Much of the information in a tree can be represented more compactly as an influence diagram, focusing attention on the issues. Decision trees can also be seen as models of induction rules from empirical data. An optimal decision tree is defined as a tree that accounts for most of the data. Several algorithms to generate such optimal trees have been devised, such as ID3/4/5, CLS, ASSISTANT, among decision support tools, decision trees have several advantages. Decision trees, Are simple to understand and interpret, people are able to understand decision tree models after a brief explanation. Have value even with little hard data, important insights can be generated based on experts describing a situation and their preferences for outcomes. Allow the addition of new possible scenarios Help determine worst, best, if a given result is provided by a model. Can be combined with other decision techniques, disadvantages of decision trees, For data including categorical variables with different number of levels, information gain in decision trees are biased in favor of those attributes with more levels. Calculations can get very complex particularly if many values are uncertain if many outcomes are linked
15.
Dendrogram
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A dendrogram is a tree diagram frequently used to illustrate the arrangement of the clusters produced by hierarchical clustering. Dendrograms are often used in biology to illustrate the clustering of genes or samples, sometimes on top of heatmaps. For a clustering example, suppose this data is to be clustered using Euclidean distance as the distance metric, iris dendrogram - Example of using a dendrogram to visualize the 3 clusters from hierarchical clustering using the complete method vs the real species category
16.
Graph drawing
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A drawing of a graph or network diagram is a pictorial representation of the vertices and edges of a graph. This drawing should not be confused with the graph itself, very different layouts can correspond to the same graph, in the abstract, all that matters is which pairs of vertices are connected by edges. In the concrete, however, the arrangement of vertices and edges within a drawing affects its understandability, usability, fabrication cost. The problem gets worse, if the changes over time by adding and deleting edges. Upward planar drawing uses the convention that every edge is oriented from a vertex to a higher vertex. Many different quality measures have been defined for graph drawings, in an attempt to find means of evaluating their aesthetics. In addition to guiding the choice between different layout methods for the graph, some layout methods attempt to directly optimize these measures. The crossing number of a drawing is the number of pairs of edges cross each other. If the graph is planar, then it is convenient to draw it without any edge intersections, that is, in this case. However, nonplanar graphs frequently arise in applications, so graph drawing algorithms must generally allow for edge crossings, the area of a drawing is the size of its smallest bounding box, relative to the closest distance between any two vertices. Drawings with smaller area are generally preferable to those with area, because they allow the features of the drawing to be shown at greater size. The aspect ratio of the box may also be important. Symmetry display is the problem of finding symmetry groups within a given graph, some layout methods automatically lead to symmetric drawings, alternatively, some drawing methods start by finding symmetries in the input graph and using them to construct a drawing. It is important that edges have shapes that are as simple as possible, in polyline drawings, the complexity of an edge may be measured by its number of bends, and many methods aim to provide drawings with few total bends or few bends per edge. Similarly for spline curves the complexity of an edge may be measured by the number of points on the edge. Several commonly used quality measures concern lengths of edges, it is desirable to minimize the total length of the edges as well as the maximum length of any edge. Additionally, it may be preferable for the lengths of edges to be rather than highly varied. Angular resolution is a measure of the sharpest angles in a graph drawing, if a graph has vertices with high degree then it necessarily will have small angular resolution, but the angular resolution can be bounded below by a function of the degree
17.
Hyperbolic tree
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A hyperbolic tree is an information visualization and graph drawing method inspired by hyperbolic geometry. Displaying hierarchical data as a tree suffers from visual clutter as the number of nodes per level can grow exponentially, for a simple binary tree, the maximum number of nodes at a level n is 2n, while the number of nodes for larger trees grows much more quickly. Drawing the tree as a node-link diagram thus requires exponential amounts of space to be displayed, one approach is to use a hyperbolic tree, first introduced by Lamping et al. Hyperbolic trees employ hyperbolic space, which intrinsically has more room than Euclidean space, displaying a hyperbolic tree commonly utilizes the Poincaré disk model of hyperbolic geometry, though the Klein-Beltrami model can also be used. Both display the hyperbolic plane within a unit disk, making the entire tree visible at once. The unit disk gives a fish-eye lens view of the plane, giving emphasis to nodes which are in focus. Traversing the hyperbolic tree requires Möbius transformations of the space, bringing new nodes into focus, hyperbolic trees have been patented in the U. S. by Xerox. Hyperbolic geometry Information visualization Radial tree – is also circular, Tree Tree The Green Tree of Life – Tree of life – University of California at Berkeley and Jepson Herbaria Tree of life Similar to the above, but with pictures
18.
Layered graph drawing
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It is also known as Sugiyama-style graph drawing after Kozo Sugiyama, who first developed this drawing style. The ideal form for a drawing would be an upward planar drawing, in which all edges are oriented in a consistent direction. Finding the smallest possible set of edges is the NP-complete feedback arc set problem, the exact solution to this problem can be formulated using integer programming. Alternatively, if the number of reversed edges is very small, the vertices of the directed acyclic graph resulting from the first step are assigned to layers, such that each edge goes from a higher layer to a lower layer. The goals of this stage are to produce a small number of layers, few edges that span large numbers of layers. For instance, by Mirskys theorem, assigning vertices by layers according to the length of the longest path starting from each vertex produces an assignment with the possible number of layers. Minimizing the width of the widest layer is NP-hard but may be solved by branch-and-cut or approximated heuristically, alternatively, the problem of minimizing the total number of layers spanned by the edges may be solved using linear programming. Integer programming procedures, although more time-consuming, may be used to combine edge length minimization with limits on the number of vertices per level. Edges that span multiple layers are replaced by paths of dummy vertices so that, after this step, the vertices within each layer are permuted in an attempt to reduce the number of crossings among the edges connecting it to the previous layer. Alternatively, the ordering of the vertices in one layer at a time may be using an algorithm that is fixed-parameter tractable in the number of crossings between it and the previous layer. Each vertex is assigned a coordinate within its layer, consistent with the permutation calculated in the previous step, Sugiyamas original work proposed a quadratic programming formulation of this step, a later method of Brandes and Köpf takes linear time and guarantees at most two bends per edge. The edges reversed in the first step of the algorithm are returned to their original orientations, the vertices are removed from the graph. In its simplest form, layered graph drawing algorithms may require O time in graphs with n vertices and m edges, because of the large number of dummy vertices that may be created. However, for some variants of the algorithm, it is possible to simulate the effect of the dummy vertices without actually constructing them explicitly, the dot tool in Graphviz produces layered drawings. A layered graph drawing algorithm is also included in Microsoft Automatic Graph Layout, in layered graph drawings with many long edges, edge clutter may be reduced by grouping sets of edges into bundles and routing them together through the same set of dummy vertices. Similarly, for drawings with many edges crossing between pairs of layers, the edges in maximal bipartite subgraphs may be grouped into confluent bundles. Drawings in which the vertices are arranged in layers may be constructed by algorithms that do not follow Sugiyamas framework, for layered drawings of concept lattices, a hybrid approach combining Sugiyamas framework with additive methods may be used. Layered graph drawing methods have also used to provide an initial placement for force-directed graph drawing algorithms
19.
Object-role modeling
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Object-role modeling is used to model the semantics of a universe of discourse. ORM is often used for modeling and software engineering. Attribute free, the predicates of an ORM Model lend themselves to the analysis, the term object-role model was coined in the 1970s and ORM based tools have been used for more than 30 years – principally for data modeling. More recently ORM has been used to model business rules, XML-Schemas, data warehouses, requirements engineering, the roots of ORM can be traced to research into semantic modeling for information systems in Europe during the 1970s. There were many pioneers and this short summary does not by any means mention them all, an early contribution came in 1973 when Michael Senko wrote about data structuring in the IBM Systems Journal. In 1974 Jean-Raymond Abrial contributed an article about Data Semantics, in June 1975, Eckhard Falkenbergs doctoral thesis was published and in 1976 one of Falkenbergs papers mentions the term object-role model. Nijssen made fundamental contributions by introducing the notation for object types and roles. Robert Meersman extended the approach by adding subtyping, and introducing the first truly conceptual query language, Object role modeling also evolved from the Natural language Information Analysis Method, a methodology that was initially developed by the academic researcher, G. M. Nijssen in the Netherlands in the mid-1970s and his team at the Control Data Corporation Research Laboratory in Belgium. In 1989 Terry Halpin completed his PhD thesis on ORM, providing the first full formalization of the approach, also in 1989, Terry Halpin and G. M. Nijssen co-authored the book Conceptual Schema and Relational Database Design and several joint papers, since then Dr. Terry Halpin has authored six books and over 160 technical papers. A graphical NIAM design tool which included the ability to generate database-creation scripts for Oracle, DB2 and it was originally named Genesys and was marketed successfully in France and later Canada. It could also handle ER diagram design and it was ported to SCO Unix, SunOs, DEC 3151s and Windows 3.0 platforms, and was later migrated to succeeding Microsoft operating systems, utilising XVT for cross operating system graphical portability. The tool was renamed OORIANE and is currently being used for data warehouse. Also evolving from NIAM is Fully Communication Oriented Information Modeling FCO-IM and it distinguishes itself from traditional ORM in that it takes a strict communication-oriented perspective. Rather than attempting to model the domain and its essential concepts, another important difference is that it does this on instance level, deriving type level and object/fact level during analysis. Standardisation of relation types, roles and concepts enables increased possibilities for model integration, object-role models are based on elementary facts, and expressed in diagrams that can be verbalised into natural language. A fact is a such as John Smith was hired on 5 January 1995 or Mary Jones was hired on 3 March 2010
20.
Organizational chart
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An organizational chart is a diagram that shows the structure of an organization and the relationships and relative ranks of its parts and positions/jobs. The term is used for similar diagrams, for example ones showing the different elements of a field of knowledge or a group of languages. The organization chart is a diagram showing graphically the relation of one official to another, or others and it is also used to show the relation of one department to another, or others, or of one function of an organization to another, or others. This chart is valuable in that it one to visualize a complete organization. A companys organizational chart typically illustrates relations between people within an organization, such relations might include managers to sub-workers, directors to managing directors, chief executive officer to various departments, and so forth. When an organization chart grows too large it can be split into smaller charts for separate departments within the organization, the titles of officials and sometimes their names are enclosed in boxes or circles. Lines are generally drawn from one box or circle to another to show the relation of one official or department to the others, the Scottish-American engineer Daniel McCallum is credited for creating the first organizational charts of American business around 1854. This chart was drawn by George Holt Henshaw, the term organization chart came into use in the early twentieth century. In 1914 Brinton declared organization charts are not nearly so widely used as they should be, in those years industrial engineers promoted the use of organization charts. The term organigram originates in the 1960s and they only show formal relationships and tell nothing of the pattern of human relationships which develop. They also often do not show horizontal relationships and they often do not include customers. The example on the shows a simple hierarchical organizational chart. An example of a relationship in this chart would be between the general and the two colonels - the colonels are directly responsible to the general. An example of a relationship in this chart would be between Captain A, and Captain B who both work on level and both report to the Colonel B. Various shapes such as rectangles, squares, triangles, circles can be used to different roles. Color can be used both for shape borders and connection lines to indicate differences in authority and responsibility, and possibly formal, advisory, a department or position yet to be created or currently vacant might be shown as a shape with a dotted outline. Importance of the position may be shown both with a change in size of the shape in addition to its placement on the chart. Figurative system of human knowledge Organizational structure
21.
Radial tree
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A radial tree, or radial map, is a method of displaying a tree structure in a way that expands outwards, radially. It is one of many ways to display a tree. In use, it is a type of information graphic, in a simple case, the first node is at the top, and the linked nodes are beneath. As each node typically has more than one child, the shape is relatively triangular. In a radial layout, instead of each successive generation being displayed a row below, each generation is displayed in a new, since the length of each orbit increases with the radius, there tends to be more room for the nodes. A radial tree will spread the larger number of nodes over an area as the levels increase. We use the level and depth interchangeably. The overall distance d is the distance between levels of the graph and it is chosen so that the overall layout will fit within a screen. Layouts are generated by working outward from the center, root, the first level is a special case because all the nodes have the same parent. The nodes for level 1 can be distributed evenly, or weighted depending on the number of children they have, for subsequent levels, the children are positioned within sectors of the remaining space, so that child nodes of one parent do not overlap with others. MindManager and MindMapper are mindmapping systems, which can make radial-like layouts, spicyNodes is an approach to visualizing hierarchies, which allows moving from node to node. Comprehensive survey and bibliography of Tree Visualization techniques Chris Harrison – WikiViz, Visualizing Wikipedia Radial maps at Visual Complexity site
22.
Semantic network
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A semantic network, or frame network, is a network that represents semantic relations between concepts. This is often used as a form of knowledge representation and it is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts. Typical standardized semantic networks are expressed as semantic triples, Semantic Nets were first invented for computers by Richard H. Richens of the Cambridge Language Research Unit in 1956 as an interlingua for machine translation of natural languages. It later featured prominently in the work of Allan M, in the subsequent decades, the distinction between semantic networks and knowledge graphs was blurred. In 2012, Google gave their knowledge graph the name Knowledge Graph, a semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another. Most semantic networks are cognitively based and they also consist of arcs and nodes which can be organized into a taxonomic hierarchy. Semantic networks contributed ideas of spreading activation, inheritance, and nodes as proto-objects and you would use the assoc function with a key of canary to extract all the information about the canary type. An example of a network is WordNet, a lexical database of English. It groups English words into sets of synonyms called synsets, provides short, general definitions, some of the most common semantic relations defined are meronymy, holonymy, hyponymy, hypernymy, synonymy and antonymy. WordNet properties have been studied from a network theory perspective and compared to other semantic networks created from Rogets Thesaurus, from this perspective the three of them are a small world structure. It is also possible to represent logical descriptions using semantic networks such as the graphs of Charles Sanders Peirce or the related conceptual graphs of John F. Sowa. These have expressive power equal to or exceeding standard first-order predicate logic, unlike WordNet or other lexical or browsing networks, semantic networks using these representations can be used for reliable automated logical deduction. Some automated reasoners exploit the features of the networks during processing. Other examples of networks are Gellish models. Gellish English with its Gellish English dictionary, is a language that is defined as a network of relations between concepts and names of concepts. Gellish English is a subset of natural English, just as Gellish Dutch is a formal subset of Dutch. Other Gellish networks consist of models and information models that are expressed in the Gellish language. A Gellish network is a network of relations between things, each relation in the network is an expression of a fact that is classified by a relation type
23.
Sociogram
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A sociogram is a graphic representation of social links that a person has. It is a drawing that plots the structure of interpersonal relations in a group situation. Sociograms were developed by Jacob L. Moreno to analyze choices or preferences within a group and they can diagram the structure and patterns of group interactions. A sociogram can be drawn on the basis of different criteria, Social relations, channels of influence. Morenos sociograms Those points on a sociogram who have many choices are called Stars and those with few or no choices are called isolates. Individuals who choose each other are known to have made a Mutual Choice, one-Way Choice refers to individuals who choose someone but the choice is not reciprocated. Cliques are groups of three or more people within a group who all choose each other. Sociograms are the charts or tools used to find the Sociometry of a social space, under the Social Discipline Model, sociograms are sometimes used to reduce misbehavior in a classroom environment. A sociogram is constructed after students answer a series of questions probing for affiliations with other classmates, the diagram can then be used to identify pathways for social acceptance for misbehaving students. In this context, the resulting sociograms are known as a friendship chart, often, the most important person/thing is in a bigger bubble in relation to everyone else. The size of the bubble represents the importance, with the biggest bubble meaning most important, Social network analysis software Corporate interlocks Diagram Network science Organizational chart Social balance theory Sociomapping Sociometry Barry Wellman
24.
Timeline
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A timeline is a way of displaying a list of events in chronological order, sometimes described as a project artifact. Timelines can use any time scale, depending on the subject, most timelines use a linear scale, where a unit of distance is equal to a set amount of time. This time scale is dependent on the events in the timeline, a timeline of evolution can be over millions of years, whereas a timeline for the day of the September 11 attacks can take place over minutes. While most timelines use a linear timescale, for large or small timespans. Historically, timelines were static images, and generally drawn or printed on paper, timelines relied heavily on graphic design, and the ability of the artist to visualize the data. Timelines, no longer constrained by space and functional limitations, are now digital and interactive. ChronoZoom is an example of computer aided interactive timeline software, timelines are often used in education to help students and researchers with understanding the order or chronology of historical events and trends for a subject. When showing time on a scale on an axis, a timeline can be used to visualize time lapses between events, durations, and the simultaneity or overlap of spans and events. Timelines are particularly useful for studying history, as they convey a sense of change over time, wars and social movements are often shown as timelines. Timelines are also useful for biographies, in these cases, timelines are used to help team members to know what milestones need to be achieved and under what time schedule. For example, in the case of establishing a timeline in the implementation phase of the life cycle of a computer system. British Library interactive timeline Port Royal des Champs museum timeline
25.
Topic map
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Topic Maps is a standard for the representation and interchange of knowledge, with an emphasis on the findability of information. Topic maps were developed in the late 1990s as a way to represent back-of-the-book index structures so that multiple indexes from different sources could be merged. However, the developers realized that with a little additional generalization. The ISO standard is known as ISO/IEC13250,2003. Topic Maps are similar to concept maps and mind maps in many respects, Topic Maps are a form of semantic web technology similar to RDF. Topics, associations, and occurrences can all be typed, where the types must be defined by the one or more creators of the topic map, the definitions of allowed types is known as the ontology of the topic map. Topic Maps explicitly support the concept of merging of identity between multiple topics or topic maps, furthermore, because ontologies are topic maps themselves, they can also be merged thus allowing for the automated integration of information from diverse sources into a coherent new topic map. Features such as identifiers and PSIs are used to control merging between differing taxonomies. Scoping on names provides a way to organise the various names given to a topic by different sources. The most recent work standardizing Topic Maps is taking place under the umbrella of the ISO/IEC JTC1/SC34/WG3 committee, the Topic Maps reference model and data model standards are defined in a way that is independent of any specific serialization or syntax. TMRM Topic Maps - Reference Model TMDM Topic Maps - Data Model There are different ways of notating Topic Maps graphically, in addition to GTM, one recently developed example is Topic Maps Martian Notation. TMMN is a graphical notation used to explain the Topic Maps data model. It is designed for use on whiteboard or paper, as well as within any diagram-based software including everyday presentation tools such as PowerPoint, the Martian refers to the archetypal Martian Scientist, namely, the ability to communicate knowledge across linguistic and cultural barriers, known and unknown. It was developed as part of the musicDNA project, advanced Topic Maps Martian Notation is currently under development by the musicDNA community and includes shorthand notation for various types of whole-part relationships. Names, resources, and relationships are said to be characteristics of abstract subjects, topics have their characteristics within scopes, i. e. the limited contexts within which the names and resources are regarded as their name, resource, and relationship characteristics. One or more interrelated documents employing this grammar is called a topic map, XML Serialization formats In the year 2000 Topic Maps was defined in an XML syntax XTM. This is now known as XTM1.0 and is still in fairly common use. The ISO standards committee published an updated XML syntax in 2006, note that XTM1.0 predates and therefore is not compatible with the more recent versions of the standard
26.
Tree structure
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A tree structure or tree diagram is a way of representing the hierarchical nature of a structure in a graphical form. A tree structure is conceptual, and appears in several forms, for a discussion of tree structures in specific fields, see Tree for computer science, insofar as it relates to graph theory, see tree, or also tree. Other related pages are listed below, the tree elements are called nodes. The lines connecting elements are called branches, nodes without children are called leaf nodes, end-nodes, or leaves. Every finite tree structure has a member that has no superior and this member is called the root or root node. The root is the starting node, but the converse is not true, infinite tree structures may or may not have a root node. The names of relationships between nodes model the kinship terminology of family relations, the gender-neutral names parent and child have largely displaced the older father and son terminology, although the term uncle is still used for other nodes at the same level as the parent. A nodes parent is a one step higher in the hierarchy. Sibling nodes share the same parent node, a nodes uncles are siblings of that nodes parent. A node that is connected to all nodes is called an ancestor. The connected lower-level nodes are descendants of the ancestor node, in the example, encyclopedia is the parent of science and culture, its children. Art and craft are siblings, and children of culture, which is their parent, also, encyclopedia, as the root of the tree, is the ancestor of science, culture, art and craft. Finally, science, art and craft, as leaves, are ancestors of no other node, the Oxford English Dictionary records use of both the terms tree structure and tree-diagram from 1965 in Noam Chomskys Aspects of the Theory of Syntax. In a tree there is one and only one path from any point to any other point. Computer science uses tree structures extensively For a formal definition see set theory, internet, usenet hierarchy Document Object Models logical structure, Yahoo. Almost always, these boil down to variations, or combinations, of a few basic styles, Classical node-link diagrams, nested sets that use enclosure/containment to show parenthood, examples include TreeMaps and fractal maps. Layered icicle diagrams that use alignment/adjacency, lists or diagrams that use indentation, sometimes called outlines or tree views. A correspondence to nested parentheses was first noticed by Sir Arthur Cayley, trees can also be represented radially
27.
Design rationale
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A design rationale is an explicit documentation of the reasons behind decisions made when designing a system or artifact. As initially developed by W. R. Kunz and Horst Rittel, design rationale seeks to provide argumentation-based structure to the political, a design rationale is the explicit listing of decisions made during a design process, and the reasons why those decisions were made. Its primary goal is to support designers by providing a means to record and communicate the argumentation, several science areas are involved in the study of design rationales, such as computer science cognitive science, artificial intelligence, and knowledge management. For supporting design rationale, various frameworks have been proposed, such as QOC, DRCS, IBIS, several variants on IBIS have since been proposed. The first was Procedural Hierarchy of Issues, first described in Ray McCalls PhD Dissertation although not named at the time, IBIS was also modified, in this case to support Software Engineering, by Potts & Bruns. The Potts & Bruns approach was extended by the Decision Representation Language. Which itself was extended by RATSpeak, the first Rationale Management System was PROTOCOL, which supported PHI, which was followed by other PHI-based systems MIKROPOLIS and PHIDIAS. The first system providing IBIS support was Hans Dehlingers STIEC, Rittel developed a small system in 1983 and the better known gIBIS was developed in 1987. Not all successful DR approaches involve structured argumentation, for example, Carroll and Rossons Scenario-Claims Analysis approach captures rationale in scenarios that describe how the system is used and how well the system features support the user goals. There are a number of ways to characterize DR approaches, some key distinguishing features are how it is captured, how it is represented, and how it can be used. The advantage of Reconstruction method is that rationales can be carefully captured, but this method might result in high cost and biases of the person producing the rationales The Record-and-replay method simply captures rationales as they unfold. Rationales are synchronously captured in a conference or asynchronously captured via bulletin board or email-based discussion. If the system has informal and semi-formal representation, the method will be helpful, the Methodological byproduct method captures rationales during the process of design following a schema. But its hard to design such a schema, the advantage of this method is its low cost. With a rich knowledge base created in advance, the Apprentice method captures rationales by asking questions when confusing or disagreeing with the designers action and this method benefits not only the user but the system. In Automatic Generation method, design rationales are automatically generated from a history at low cost. It has the ability in maintaining consistent and up-to-date rationales, but the cost of compiling the execution history is high due to the complexity and difficulty of some machine-learning problems. The Historian method let a person or computer program watches all designers actions, rationales are captured during the design process
28.
Diagrammatic reasoning
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Diagrammatic reasoning is reasoning by means of visual representations. The study of reasoning is about the understanding of concepts and ideas, visualized with the use of diagrams. Leibniz thus hoped to create a language usable within the framework of a universal logical calculation or calculus ratiocinator, since the characteristica universalis is diagrammatic and employs pictograms, the diagrams in Leibnizs work warrant close study. On at least two occasions, Leibniz illustrated his philosophical reasoning with diagrams, one diagram, the frontispiece to his 1666 De Arte Combinatoria, represents the Aristotelian theory of how all material things are formed from combinations of the elements earth, water, air, and fire. These four elements make up the four corners of a diamond, opposing pairs of these are joined by a bar labeled contraries. At the four corners of the square are the four qualities defining the elements. Each adjacent pair of these is joined by a bar labeled possible combination, starting from the top, fire is formed from the combination of dryness and heat, air from wetness and heat, water from coldness and wetness, earth from coldness and dryness. A diagram is a 2D geometric symbolic representation of information according to some visualization technique, sometimes, the technique uses a 3D visualization which is then projected onto the 2D surface. The term diagram in common sense can have two meanings, visual information device, Like the term illustration the diagram is used as a collective term standing for the whole class of technical genres, including graphs, technical drawings and tables. Specific kind of display, This is only the genre. In science you will find the used in both ways. On the other hand, Lowe defined diagrams as specifically abstract graphic portrayals of the matter they represent. Or as Bert S. Hall wrote, diagrams are simplified figures, caricatures in a way, White the characteristics of a good diagram are elegance, clarity, ease, pattern, simplicity, and validity. Elegance for White means that what you are seeing in the diagram is the simplest and most fitting solution to a problem, a logical graph is a special type of graph-theoretic structure in any one of several systems of graphical syntax that Charles Sanders Peirce developed for logic. In the century since Peirce initiated this line of development, a variety of systems have branched out from what is abstractly the same formal base of graph-theoretic structures. A conceptual graph is a notation for logic based on the graphs of Charles Sanders Peirce. In the first published paper on conceptual graphs, John F. Sowa used them to represent the conceptual schemas used in database systems and his first book applied them to a wide range of topics in artificial intelligence, computer science, and cognitive science. A linear notation, called the Conceptual Graph Interchange Format, has been standardized in the ISO standard for Common Logic, the diagram on the right is an example of the display form for a conceptual graph
29.
Olog
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Ologs were introduced in 2010 by David Spivak, a research scientist in the Department of Mathematics, MIT. The term olog is short for ontology log, ontology derives from onto-, from the Greek ὤν, ὄντος being, that which is, present participle of the verb εἰμί be, and -λογία, -logia, science, study, theory. At the basic level an olog C is a category whose objects are represented as boxes containing sentences, the structures of the sentences for both the objects and the morphisms of C need to be compatible with the mathematical definition of C. This compatibility cannot be checked mathematically, because it lies in the correspondence between mathematical ideas and natural language, every olog has a target category, which is taken to be Set, the category of sets and functions, unless otherwise mentioned. In that case, we are looking at a set of amino acids, a set of groups. In this article we usually stick to Set, though using the Kleisli category C P of the power set monad. Another possibility, though one we do not use here, would be to use the Kleisli category of probability distributions, the boxes in the above example refer to objects of Set. For example, the box containing the sentence an amino acid refers to the set of all amino acids and the box containing the sentence a side chain refers to the set of all side chains. The arrow labeled with has whose source is an amino acid, indeed, every amino acid has a unique side chain so the arrow is a valid morphism of Set. The functional nature of the morphisms in Set is expressed in an olog by labeling arrows with appropriate sentences, a morphism f, A → B in the Kleisli category C P can be seen as establishing a binary relation R. Given a ∈ A and b ∈ B we say that ∈ R if b ∈ f and we can use C P as the target category for an olog. In this case the arrows in the olog need to reflect the nature of morphisms in C P. This can be done by labeling every arrow in the olog with either is related to, or is greater than, an olog C can also be viewed as a database schema. Every box in the olog is a table T and the arrows emanating from the box are columns in C, the assignment of a particular instance to an object of C is done through a functor I, C → Set. Communication between different ologs which in practice can be communication between different models or world-views is done using functors, Spivak coins the notions of a meaningful and strongly meaningful functors. Let C and D be two ologs, I, C → Set, J, D → Set functors and F, C → D a functor and we say that a F is meaningful if there exists a natural transformation m, I → F ∗ J. We say that F is strongly meaningful if given an object X ∈ C we have I = J and this equality is equivalent to requiring m to be a natural isomorphism. Sometime it will be hard to find a meaningful functor F from C to D
30.
Semantic Web
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The Semantic Web is an extension of the Web through standards by the World Wide Web Consortium. The standards promote common data formats and exchange protocols on the Web, according to the W3C, The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. The term was coined by Tim Berners-Lee for a web of data that can be processed by machines, while its critics have questioned its feasibility, proponents argue that applications in industry, biology and human sciences research have already proven the validity of the original concept. The 2001 Scientific American article by Berners-Lee, Hendler, and Lassila described an expected evolution of the existing Web to a Semantic Web, in 2006, Berners-Lee and colleagues stated that, This simple idea…remains largely unrealized. In 2013, more than four million Web domains contained Semantic Web markup, in the following example, the text Paul Schuster was born in Dresden on a Website will be annotated, connecting a person with their place of birth. The following HTML-fragment shows how a small graph is being described, in RDFa-syntax using a schema. org vocabulary, the triples result in the graph shown in the given figure. One of the advantages of using Uniform Resource Identifier is that they can be dereferenced using the HTTP protocol, according to the so-called Linked Open Data principles, such a dereferenced URI should result in a document that offers further data about the given URI. This enables automated agents to access the Web more intelligently and perform tasks on behalf of users. He defines the Semantic Web as a web of data that can be processed directly and indirectly by machines, many of the technologies proposed by the W3C already existed before they were positioned under the W3C umbrella. In addition, other technologies with similar goals have emerged, such as microformats. A Semantic Web, which makes possible, has yet to emerge, but when it does. The intelligent agents people have touted for ages will finally materialize, the Semantic Web is regarded as an integrator across different content, information applications and systems. It has applications in publishing, blogging, and many other areas, many files on a typical computer can also be loosely divided into human readable documents and machine readable data. Documents like mail messages, reports, and brochures are read by humans, Data, such as calendars, addressbooks, playlists, and spreadsheets are presented using an application program that lets them be viewed, searched and combined. Metadata tags provide a method by which computers can categorise the content of web pages, for example, With HTML, rather, HTML can only say that the span of text X586172 is something that should be positioned near Acme Gizmo and €199, etc. There is no way to say this is a catalog or even to establish that Acme Gizmo is a kind of title or that €199 is a price. There is also no way to express that these pieces of information are together in describing a discrete item. Semantic HTML refers to the traditional HTML practice of markup following intention, for example, the use of <em> denoting emphasis rather than <i>, which specifies italics
31.
Treemapping
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In information visualization and computing, treemapping is a method for displaying hierarchical data using nested rectangles. Treemaps display hierarchical data as a set of nested rectangles, each branch of the tree is given a rectangle, which is then tiled with smaller rectangles representing sub-branches. A leaf nodes rectangle has a proportional to a specified dimension of the data. Often the leaf nodes are colored to show a separate dimension of the data, a second advantage of treemaps is that, by construction, they make efficient use of space. As a result, they can legibly display thousands of items on the screen simultaneously, to create a treemap, one must define a tiling algorithm, that is, a way to divide a rectangle into sub-rectangles of specified areas. Unfortunately, these properties have an inverse relationship, as the aspect ratio is optimized, the order of placement becomes less predictable. As the order becomes more stable, the ratio is degraded. For example, mosaic plots use rectangular tilings to show joint distributions, the main distinguishing feature of a treemap, however, is the recursive construction that allows it to be extended to hierarchical data with any number of levels. This idea was invented by professor Ben Shneiderman at the University of Maryland Human – Computer Interaction Lab in the early 1990s, Shneiderman and his collaborators then deepened the idea by introducing a variety of interactive techniques for filtering and adjusting treemaps. These early treemaps all used the simple slice-and-dice tiling algorithm, despite many desirable properties, the slice-and-dice method often produces tilings with many long, skinny rectangles. In 1994 Hascoet & Beaudouin-Lafon invented an algorithm, later popularized by Jarke van Wijk. Following its launch, treemaps enjoyed a surge of interest, especially in financial contexts, a third wave of treemap innovation came around 2004, after Marcos Weskamp created the Newsmap, a treemap that displayed news headlines. This example of a non-analytical treemap inspired many imitators, and introduced treemaps to a new, in recent years, treemaps have made their way into the mainstream media, including usage by the New York Times. Disk space analyzer Information visualization List of countries by economic complexity, marimekko Chart, a similar concept with one level of explicit hierarchy. Hypermedia exploration with interactive dynamic maps Paper by Hascoet and Beaudouin-Lafon introducing the squarified treemap layout algorithm. jp is a treemap of Google news stories
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Diagram
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A diagram is a symbolic representation of information according to some visualization technique. Diagrams have been used since ancient times, but became prevalent during the Enlightenment. Sometimes, the uses a three-dimensional visualization which is then projected onto a two-dimensional surface. The word graph is used as a synonym for diagram. Specific kind of display, This is the genre that shows qualitative data with shapes that are connected by lines, arrows. In science the term is used in both ways, on the other hand, Lowe defined diagrams as specifically abstract graphic portrayals of the subject matter they represent. Or in Halls words diagrams are simplified figures, caricatures in a way and these simplified figures are often based on a set of rules. The basic shape according to White can be characterized in terms of elegance, clarity, ease, pattern, simplicity, elegance is basically determined by whether or not the diagram is the simplest and most fitting solution to a problem. g. Many of these types of diagrams are generated using diagramming software such as Visio. Chart Diagrammatic reasoning Diagrammatology List of graphical methods Mathematical diagram Plot commons, michael Anderson, Peter Cheng, Volker Haarslev. Theory and Application of Diagrams, First International Conference, Diagrams 2000, edinburgh, Scotland, UK, September 1–3,2000. Garcia, M The Diagrams of Architecture
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Spider diagram
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In mathematics, a unitary spider diagram adds existential points to an Euler or a Venn diagram. The points indicate the existence of a described by the intersection of contours in the Euler diagram. These points may be joined together forming a shape like a spider, joined points represent an or condition, also known as a logical disjunction. A spider diagram is an expression involving unitary spider diagrams. For example, it may consist of the conjunction of two spider diagrams, the disjunction of two spider diagrams, or the negation of a spider diagram, in the image shown, the following conjunctions are apparent from the Euler diagram. The set C is only available as a subset of B, often, in complicated diagrams, singleton sets and/or conjunctions may be obscured by other set combinations. The two spiders in the example correspond to the logical expressions, Red spider, ∨ ∨ Blue spider, ∨ ∨ Howse, J. and Stapleton, G. and Taylor. Spider Diagrams London Mathematical Society Journal of Computation and Mathematics, v.8, ISSN 1461-1570 Accessed on January 8,2012 here Stapleton, G. and Howse, J. and Taylor, J. and Thompson, S. Diagrams, v.168, pgs 169-219 Accessed on January 4,2012 here Stapleton, G. and Jamnik, M. and Masthoff, on the Readability of Diagrammatic Proofs Proc. PDF Brighton and Kent University - Euler Diagrams
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Pie chart
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A pie chart is a circular statistical graphic which is divided into slices to illustrate numerical proportion. In a pie chart, the arc length of each slice, is proportional to the quantity it represents, while it is named for its resemblance to a pie which has been sliced, there are variations on the way it can be presented. The earliest known pie chart is generally credited to William Playfairs Statistical Breviary of 1801, Pie charts are very widely used in the business world and the mass media. Pie charts can be replaced in most cases by other such as the bar chart. The earliest known pie chart is generally credited to William Playfairs Statistical Breviary of 1801, playfair presented an illustration, which contained a series of pie charts. One of those charts depicting the proportions of the Turkish Empire located in Asia, Europe and this invention was not widely used at first, The French engineer Charles Joseph Minard was one of the first to use pie charts in 1858, in particular in maps. Minards map,1858 used pie charts to represent the cattle sent from all around France for consumption in Paris, playfair thought that pie charts were in need of a third dimension to add additional information. It has been said that Florence Nightingale invented it, though in fact she just popularised it, a 3d pie cake, or perspective pie cake, is used to give the chart a 3D look. The use of superfluous dimensions not used to display the data of interest is discouraged for charts in general, a doughnut chart is a variant of the pie chart, with a blank center allowing for additional information about the data as a whole to be included. A chart with one or more separated from the rest of the disk is known as an exploded pie chart. This effect is used to highlight a sector, or to highlight smaller segments of the chart with small proportions. The polar area diagram is similar to a pie chart, except sectors are equal angles. The polar area diagram is used to plot cyclic phenomena, for example, if the count of deaths in each month for a year are to be plotted then there will be 12 sectors all with the same angle of 30 degrees each. The radius of each sector would be proportional to the root of the death count for the month. Léon Lalanne later used a diagram to show the frequency of wind directions around compass points in 1843. The wind rose is used by meteorologists. Nightingale published her rose diagram in 1858, the name coxcomb is sometimes used erroneously, this was the name Nightingale used to refer to a book containing the diagrams rather than the diagrams themselves. A ring chart, also known as a sunburst chart or a pie chart, is used to visualize hierarchical data
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Tony Buzan
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Anthony Peter Tony Buzan is an English author and educational consultant. Buzan popularized the idea of mental literacy and a technique called Mind Mapping, earlier used by Leonardo da Vinci. Buzan was born in Palmers Green, Enfield, Middlesex and he is an alumnus of Kitsilano Secondary School in Vancouver, where he was Head Boys Prefect 1959-60. His brother is the academic Barry Buzan, during his time at SFU, Buzan became very involved in Mensa. He is a promoter of mnemonic systems and mind mapping techniques and he launched his own software programme to support mind mapping called iMindMap in December 2006. The Buzan Organisation holds trademarks on the mind map in the context of self-improvement educational courses in the UK. The trademark does not appear in the records of the Canadian Intellectual Property Office and he has since authored or co-authored over 100 books that have appeared in 30 languages. As a popular author, Tony Buzan has written on subjects relating to the brain, genius quotient, spiritual intelligence, memory, creativity. He is the founder and President of the Brain Foundation and also the Brain Trust Charity, the World Memory Championships and he was a co-founder of Londons Mind Body Spirit Festival as well as the Mind Sports Olympiad. Critics have called mind mapping pseudoscience, questioning the existence of evidence supporting the technique, however, a meta study about concept mapping concluded that concept mapping is more effective than reading text passages, attending lectures, and participating in class discussions. The same study concluded that concept mapping is slightly more effective than other constructive activities such as writing summaries and outlines. net Biography OpenLearn/Open University
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Brainstorming
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Brainstorming is a group creativity technique by which efforts are made to find a conclusion for a specific problem by gathering a list of ideas spontaneously contributed by its members. The term was popularized by Alex Faickney Osborn in the 1953 book Applied Imagination, advertising executive Alex F. Osborn began developing methods for creative problem-solving in 1939. He was frustrated by employees’ inability to develop creative ideas individually for ad campaigns, in response, he began hosting group-thinking sessions and discovered a significant improvement in the quality and quantity of ideas produced by employees. Osborn outlined his method in the 1948 book Your Creative Power in chapter 33, Osborn claimed that two principles contribute to ideative efficacy, these being, Defer judgment, Reach for quantity. Following these two principles were his four general rules of brainstorming, established with intention to, reduce social inhibitions among group members, increase overall creativity of the group. Go for quantity, This rule is a means of enhancing divergent production, the assumption is that the greater the number of ideas generate the bigger the chance of producing a radical and effective solution. Withhold criticism, In brainstorming, criticism of ideas generated should be put on hold, instead, participants should focus on extending or adding to ideas, reserving criticism for a later critical stage of the process. By suspending judgment, participants will feel free to generate unusual ideas, welcome wild ideas, To get a good long list of suggestions, wild ideas are encouraged. They can be generated by looking from new perspectives and suspending assumptions and these new ways of thinking might give you better solutions. Combine and improve ideas, As suggested by the slogan 1+1=3 and it is believed to stimulate the building of ideas by a process of association. Osborn notes that brainstorming should address a question, he held that sessions addressing multiple questions were inefficient. Osborn envisioned groups of around 12 participants, including experts and novices. Participants are encouraged to provide wild and unexpected answers, Ideas receive no criticism or discussion. The group simply provides ideas that lead to a solution. The judgments are reserved for a later date, participants are asked to write their ideas anonymously. Then the facilitator collects the ideas and the votes on each idea. The vote can be as simple as a show of hands in favor of a given idea, after distillation, the top ranked ideas may be sent back to the group or to subgroups for further brainstorming. For example, one group may work on the color required in a product, another group may work on the size, and so forth
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Memory
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Memory is the faculty of the mind by which information is encoded, stored, and retrieved. Memory is vital to experiences and related to systems, it is the retention of information over time for the purpose of influencing future action. If we could not remember past events, we could not learn or develop language, relationships, often memory is understood as an informational processing system with explicit and implicit functioning that is made up of a sensory processor, short-term memory, and long-term memory. The sensory processor allows information from the world to be sensed in the form of chemical and physical stimuli and attended to with various levels of focus. Working memory serves as an encoding and retrieval processor, information in the form of stimuli is encoded in accordance with explicit or implicit functions by the working memory processor. The working memory also retrieves information from previously stored material, finally, the function of long-term memory is to store data through various categorical models or systems. Explicit and implicit functions of memory are known as declarative and non-declarative systems. These systems involve the purposeful intention of memory retrieval and storage, declarative, or explicit, memory is the conscious storage and recollection of data. Under declarative memory resides semantic and episodic memory, semantic memory refers to memory that is encoded with specific meaning, while episodic memory refers to information that is encoded along a spatial and temporal plane. Declarative memory is usually the primary process thought of when referencing memory, non-declarative, or implicit, memory is the unconscious storage and recollection of information. An example of a process would be the unconscious learning or retrieval of information by way of procedural memory. Memory is not a processor, and is affected by many factors. The manner information is encoded, stored, and retrieved can all be corrupted, the amount of attention given new stimuli can diminish the amount of information that becomes encoded for storage. Also, the process can become corrupted by physical damage to areas of the brain that are associated with memory storage. Finally, the retrieval of information from long-term memory can be disrupted because of decay within long-term memory, normal functioning, decay over time, and brain damage all affect the accuracy and capacity of memory. Sensory memory holds sensory information less than one second after an item is perceived, the ability to look at an item and remember what it looked like with just a split second of observation, or memorization, is the example of sensory memory. It is out of control and is an automatic response. With very short presentations, participants often report that they seem to see more than they can actually report, the first experiments exploring this form of sensory memory were precisely conducted by George Sperling using the partial report paradigm
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